Fetching the paper…
Reading the bibliography…
The original "Seven Motifs" set forth a roadmap of essential methods for the field of scientific computing, where a motif is an algorithmic method that captures a pattern of computation and data movement.
Soft constraints for inference with declarative knowledge
Zenna Tavares, Javier Burroni, Edgar Minaysan, Armando Solar-Lezama, and R. Ranganath · 1901
Earlier work this paper cites.
Guided image generation with conditional invertible neural networks
Lynton Ardizzone, Carsten Lüth, Jakob Kruse, Carsten Rother, and Ullrich Köthe · 1907
Earlier work this paper cites.
Reconstructing constructivism: Causal models, Bayesian learning mechanisms, and the theory theory
Alison Gopnik and Henry M. Wellman · 1939
Earlier work this paper cites.
Theory of games and economic behavior
J. Neumann and O. Morgenstern · 1945
Earlier work this paper cites.
A theory of human curiosity
D. E. Berlyne · 1954
Earlier work this paper cites.
On a measure of the information provided by an experiment
D. Lindley · 1956
Earlier work this paper cites.
Probabilistic logic and the synthesis of reliable organisms from unreliable components
J. Neumann · 1956
Earlier work this paper cites.
The structure of scientific revolutions
Thomas S. Kuhn and David Hawkins · 1962
Earlier work this paper cites.
On the distribution of points in a cube and the approximate evaluation of integrals
Ilya M. Sobol · 1967
Earlier work this paper cites.
The origins of intelligence in children
Jean Inhelder Brbel Piaget and Margaret Cook · 1971
Earlier work this paper cites.
Estimating causal effects of treatments in randomized and nonrandomized studies
D. Rubin · 1974
Earlier work this paper cites.
Guaranteed margins for lqg regulators
John C. Doyle · 1978
Earlier work this paper cites.
Neural networks and physical systems with emergent collective computational abilities
John J. Hopfield · 1982
Earlier work this paper cites.
Simulating physics with computers
RP Feynman · 1982
Earlier work this paper cites.
Bayesianly Justifiable and Relevant Frequency Calculations for the Applied Statistician
Donald B. Rubin · 1984
Earlier work this paper cites.
In Journal of the Royal Statistical Society: Series B (Methodological) , volume 46, pages 193–212, 1984
Monte Carlo Methods of Inference for Implicit Statistical Models · 1984
Earlier work this paper cites.
Latent variable models: an introduction to factor, path, and structural analysis
C. John · 1986
Earlier work this paper cites.
Sparse distributed memory
Pentti Kanerva · 1988
Earlier work this paper cites.
Remarks on picard-lindelöf iteration
Olavi Nevanlinna · 1989
Earlier work this paper cites.
Artificial adaptive agents in economic theory
J. Holland and John H. Miller · 1991
Earlier work this paper cites.
Designing economic agents that act like human agents: A behavioral approach to bounded rationality
W. Arthur · 1991
Earlier work this paper cites.
Fast parallel algorithms for short-range molecular dynamics
Steven J. Plimpton · 1993
Earlier work this paper cites.
Approximation of dynamical systems by continuous time recurrent neural networks
Ken ichi Funahashi and Yuichi Nakamura · 1993
Earlier work this paper cites.
Coarctate transition states: the discovery of a reaction principle
Rainer Herges · 1994
Earlier work this paper cites.
Bayesian experimental design: A review
K. Chaloner and I. Verdinelli · 1995
Earlier work this paper cites.
Causal diagrams for empirical research
J. Pearl · 1995
Earlier work this paper cites.
Lifelong Learning Algorithms
Sebastian Thrun · 1998
Earlier work this paper cites.
The scientist in the crib : minds, brains, and how children learn
Alison Gopnik, Andrew N. Meltzoff, and Patricia K. Kuhl · 1999
Earlier work this paper cites.
From artificial evolution to artificial life
Timothy J. Taylor · 1999
Earlier work this paper cites.
Causality: Models, reasoning and inference
J. Pearl · 2000
Earlier work this paper cites.
Helmholtz machines and wake-sleep learning
P. Dayan · 2000
Earlier work this paper cites.
The data grid: Towards an architecture for the distributed management and analysis of large scientific datasets
Ann L. Chervenak, Ian T. Foster, Carl Kesselman, Charles A. Salisbury, and Steven Tuecke · 2000
Earlier work this paper cites.
Universal differential equations for scientific machine learning
Christopher Rackauckas, Y. Ma, Julius Martensen, Collin Warner, K. Zubov, R. Supekar, Dominic J. Skinner, and Ali Ramadhan · 2001
Earlier work this paper cites.
Bayesian calibration of computer models
M. Kennedy and A. O’Hagan · 2001
Earlier work this paper cites.
Agent-based simulation of a financial market
M. Raberto, S. Cincotti, S. Focardi, and M. Marchesi · 2001
Earlier work this paper cites.
Interactive evolutionary computation: fusion of the capabilities of ec optimization and human evaluation
Hideyuki Takagi · 2001
Earlier work this paper cites.
Agent-based modeling: Methods and techniques for simulating human systems
E. Bonabeau · 2002
Earlier work this paper cites.
Modeling civil violence: an agent-based computational approach
Joshua M. Epstein · 2002
Earlier work this paper cites.
Planning as inference in epidemiological models
Frank D. Wood, Andrew Warrington, Saeid Naderiparizi, Christian Weilbach, Vaden Masrani, William Harvey, A. Scibior, Boyan Beronov, and Alireza Nasseri · 2003
Earlier work this paper cites.
Planning as inference in epidemiological models
Frank Wood, Andrew Warrington, Saeid Naderiparizi, Christian Weilbach, Vaden Masrani, William Harvey, Adam Scibior, Boyan Beronov, John Grefenstette, Duncan Campbell, et al · 2003
Earlier work this paper cites.
Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (cma-es)
Nikolaus Hansen, Sibylle D Müller, and Petros Koumoutsakos · 2003
Earlier work this paper cites.
Flows for simultaneous manifold learning and density estimation
Johann Brehmer and Kyle Cranmer · 2003
Earlier work this paper cites.
Defining Software Requirements for Scientific Computing, 2004
Phillip Colella · 2004
Earlier work this paper cites.
Inferring cellular networks using probabilistic graphical models
N. Friedman · 2004
Earlier work this paper cites.
Llvm: a compilation framework for lifelong program analysis & transformation
Chris Lattner and Vikram S. Adve · 2004
Earlier work this paper cites.
The nash equilibrium: A perspective
Charles A. Holt and Alvin E. Roth · 2004
Earlier work this paper cites.
Climate change and international tourism: a simulation study
Jacqueline M Hamilton, David J Maddison, and Richard SJ Tol · 2005
Earlier work this paper cites.
Causal inference using potential outcomes
D. Rubin · 2005
Earlier work this paper cites.
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Carl Edward Rasmussen and Christopher K. I. Williams · 2005
Earlier work this paper cites.
Blog: Probabilistic models with unknown objects
Brian Milch, Bhaskara Marthi, S. Russell, D. Sontag, D. L. Ong, and A. Kolobov · 2005
Earlier work this paper cites.
Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques
Edmund Burke and Graham Kendall, editors · 2005
Earlier work this paper cites.
The landscape of parallel computing research: A view from berkeley
K. Asanović, R. Bodík, Bryan Catanzaro, Joseph Gebis, P. Husbands, K. Keutzer, D. Patterson, W. Plishker, J. Shalf, S. Williams, and K. Yelick · 2006
Earlier work this paper cites.
Gaussian processes for machine learning
C. Rasmussen and Christopher K. I. Williams · 2006
Earlier work this paper cites.
Discovering symbolic models from deep learning with inductive biases
M. Cranmer, Alvaro Sanchez-Gonzalez, Peter W. Battaglia, Rui Xu, Kyle Cranmer, David N. Spergel, and Shirley Ho · 2006
Earlier work this paper cites.
Wiper: A multi-agent system for emergency response
T. Schoenharl, G. Madey, Gábor Szabó, and A. Barabasi · 2006
Earlier work this paper cites.
The blue brain project
Henry Markram · 2006
Earlier work this paper cites.
When and why pinns fail to train: A neural tangent kernel perspective
Sifan Wang, Xinling Yu, and P. Perdikaris · 2007
Earlier work this paper cites.
Nonmyopic active learning of gaussian processes: an exploration-exploitation approach
Andreas Krause and Carlos Guestrin · 2007
Earlier work this paper cites.
A computational framework for simulation of biogeochemical tracers in the ocean
S. Khatiwala · 2007
Earlier work this paper cites.
Using likelihood-free inference to compare evolutionary dynamics of the protein networks of h. pylori and p. falciparum
Oliver Ratmann, Ole Jørgensen, Trevor Hinkley, Michael Stumpf, Sylvia Richardson, and Carsten Wiuf · 2007
Earlier work this paper cites.
Exact bayesian structure learning from uncertain interventions
Daniel Eaton and K. Murphy · 2007
Earlier work this paper cites.
Causal discovery in physical systems from videos
Yunzhu Li, A. Torralba, Animashree Anandkumar, D. Fox, and Animesh Garg · 2007
Earlier work this paper cites.
Thinking in complexity - the computational dynamics of matter, mind, and mankind, 5th edition
K. Mainzer · 2007
Earlier work this paper cites.
Complementary computing: policies for transferring callers from dialog systems to human receptionists
E. Horvitz and Tim Paek · 2007
Earlier work this paper cites.
A grid-enabled branch and bound algorithm for solving challenging combinatorial optimization problems
Mohand-Said Mezmaz, Nouredine Melab, and El-Ghazali Talbi · 2007
Earlier work this paper cites.
Nemo ocean engine
Gurvan Madec · 2008
Earlier work this paper cites.
Quantum graphical models and belief propagation
M. Leifer and D. Poulin · 2008
Earlier work this paper cites.
Giuseppe palomba and the lotka-volterra equations
Giancarlo Gandolfo · 2008
Earlier work this paper cites.
Computing likelihood functions for high-energy physics experiments when distributions are defined by simulators with nuisance parameters
Radford M. Neal · 2008
Earlier work this paper cites.
Sequential experiment design for contour estimation from complex computer codes
Pritam Ranjan, Derek Bingham, and George Michailidis · 2008
Earlier work this paper cites.
Generative social science: Studies in agent-based computational modeling
M. Batty · 2008
Earlier work this paper cites.
Agent-based simulation of travel demand: Structure and computational performance of matsim-t
M. Balmer, K. Meister, M. Rieser, K. Nagel, and K. Axhausen · 2008
Earlier work this paper cites.
Church: a language for generative models
Noah D. Goodman, Vikash K. Mansinghka, D. M. Roy, Keith Bonawitz, and J. Tenenbaum · 2008
Earlier work this paper cites.
Towards a comprehensive simulation model of malaria epidemiology and control
T. Smith, N. Maire, A. Ross, M. Penny, N. Chitnis, A. Schapira, A. Studer, B. Genton, C. Lengeler, F. Tediosi, D. de Savigny, and M. Tanner · 2008
Earlier work this paper cites.
Provenance and scientific workflows: challenges and opportunities
Susan B. Davidson and Juliana Freire · 2008
Earlier work this paper cites.
Automatic guidance of attention from working memory
David Soto, John Hodsoll, Pia Rotshtein, and Glyn W. Humphreys · 2008
Earlier work this paper cites.
Scientific freedom: The elixir of civilization
D. Braben · 2008
Earlier work this paper cites.
Probabilistic graphical models - principles and techniques
D. Koller and N. Friedman · 2009
Earlier work this paper cites.
Variational learning of inducing variables in sparse gaussian processes
M. Titsias · 2009
Earlier work this paper cites.
Causal inference in statistics: An overview
J. Pearl · 2009
Earlier work this paper cites.
The economy needs agent-based modelling
J. Farmer and D. Foley · 2009
Earlier work this paper cites.
Figaro : An object-oriented probabilistic programming language
A. Pfeffer · 2009
Earlier work this paper cites.
The bugs project: Evolution, critique and future directions
D. Lunn, D. Spieǵelhalter, A. Thomas, and N. Best · 2009
Earlier work this paper cites.
Probabilistic programming with infer.net
John Winn and Tom Minka · 2009
Earlier work this paper cites.
Monte carlo and quasi-monte carlo sampling
Christiane Lemieux · 2009
Earlier work this paper cites.
Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions
Peter Ertl and Ansgar Schuffenhauer · 2009
Earlier work this paper cites.
Recent advances in surrogate-based optimization
Alexander IJ Forrester and Andy J Keane · 2009
Earlier work this paper cites.
Building reliable data pipelines for managing community data using scientific workflows
Yogesh L. Simmhan, Catharine van Ingen, Alexander S. Szalay, Roger S. Barga, and James N. Heasley · 2009
Earlier work this paper cites.
Folding@home and genome@home: Using distributed computing to tackle previously intractable problem
Stefan M. Larson, Christopher D. Snow, Michael R. Shirts, and Vijay S. Pande · 2009
Earlier work this paper cites.
Fourier neural operator for parametric partial differential equations
Zong-Yi Li, Nikola B. Kovachki, K. Azizzadenesheli, Burigede Liu, K. Bhattacharya, Andrew Stuart, and Anima Anandkumar · 2010
Earlier work this paper cites.
Approximate Bayesian computation in population genetics
Mark A. Beaumont, Wenyang Zhang, and David J. Balding · 2010
Earlier work this paper cites.
An application of collaborative targeted maximum likelihood estimation in causal inference and genomics
Susan Gruber and M. J. van der Laan · 2010
Earlier work this paper cites.
Theano: A cpu and gpu math compiler in python
J. Bergstra, Olivier Breuleux, Frédéric Bastien, Pascal Lamblin, Razvan Pascanu, Guillaume Desjardins, Joseph P. Turian, David Warde-Farley, and Yoshua Bengio · 2010
Earlier work this paper cites.
The free-energy principle: a unified brain theory?
Karl J. Friston · 2010
Earlier work this paper cites.
Multi-scale modelling and simulation in systems biology
Joseph O Dada and Pedro Mendes · 2011
Earlier work this paper cites.
Domain specific languages contextualized
Michael H. Matthee and S.P. Levitt · 2011
Earlier work this paper cites.
An explicit link between gaussian fields and gaussian markov random fields: the stochastic partial differential equation approach
F. Lindgren, H. Rue, and J. Lindström · 2011
Earlier work this paper cites.
The harvard clean energy project: Large-scale computational screening and design of organic photovoltaics on the world community grid
Johannes Hachmann, Roberto Olivares-Amaya, Sule Atahan-Evrenk, Carlos Amador-Bedolla, Roel S. Sánchez-Carrera, Aryeh Gold-Parker, Leslie Vogt, Anna M. Brockway, and Alán Aspuru-Guzik · 2011
Earlier work this paper cites.
Lack of confidence in approximate bayesian computation model choice
Christian P Robert, Jean-Marie Cornuet, Jean-Michel Marin, and Natesh S Pillai · 2011
Earlier work this paper cites.
Agent-based modeling: The right mathematics for the social sciences?
P. Borrill and L. Tesfatsion · 2011
Earlier work this paper cites.
Pure reasoning in 12-month-old infants as probabilistic inference
Ernő Téglás, E. Vul, V. Girotto, Michel Gonzalez, J. Tenenbaum, and L. Bonatti · 2011
Earlier work this paper cites.
Multiscale approaches to protein modeling
Andrzej Kolinski · 2011
Earlier work this paper cites.
Novelty-Based Multiobjectivization
Jean-Baptiste Mouret · 2011
Earlier work this paper cites.
Automating string processing in spreadsheets using input-output examples
Sumit Gulwani · 2011
Earlier work this paper cites.
Quantum Computation and Quantum Information: 10th Anniversary Edition
Michael A. Nielsen and Isaac L. Chuang · 2011
Earlier work this paper cites.
Biomolecular simulation: a computational microscope for molecular biology
Ron O Dror, Robert M Dirks, JP Grossman, Huafeng Xu, and David E Shaw · 2012
Earlier work this paper cites.
The “seven dwarfs” of symbolic computation
Erich L Kaltofen · 2012
Earlier work this paper cites.
Practical bayesian optimization of machine learning algorithms
Jasper Snoek, H. Larochelle, and Ryan P. Adams · 2012
Earlier work this paper cites.
A bayesian approach to targeted experiment design
J. Vanlier, C. A. Tiemann, P. Hilbers, and N. Riel · 2012
Earlier work this paper cites.
Constructing summary statistics for approximate bayesian computation: semi-automatic approximate bayesian computation
Paul Fearnhead and Dennis Prangle · 2012
Earlier work this paper cites.
Sequential design of computer experiments for the estimation of a probability of failure
Julien Bect, David Ginsbourger, Ling Li, Victor Picheny, and Emmanuel Vazquez · 2012
Earlier work this paper cites.
Turbulent magnetic field amplification from spiral sasi modes: Implications for core-collapse supernovae and proto-neutron star magnetization
E. Endeve, C. Cardall, R. Budiardja, S. Beck, Alborz Bejnood, R. Toedte, A. Mezzacappa, and J. Blondin · 2012
Earlier work this paper cites.
Entropy search for information-efficient global optimization
Philipp Hennig and Christian J. Schuler · 2012
Earlier work this paper cites.
Active learning of inverse models with intrinsically motivated goal exploration in robots
Adrien Baranes and Pierre-Yves Oudeyer · 2012
Earlier work this paper cites.
Empirical encounters with computational irreducibility and unpredictability
Hector Zenil, Fernando Soler-Toscano, and Joost J. Joosten · 2012
Earlier work this paper cites.
Size estimation of chemical space: how big is it?
Kurt L. M. Drew, Hakim Baiman, Prashannata Khwaounjoo, Bo Yu, and Jóhannes Reynisson · 2012
Earlier work this paper cites.
Combining human and machine intelligence in large-scale crowdsourcing
Ece Kamar, Severin Hacker, and E. Horvitz · 2012
Earlier work this paper cites.
Active learning
Burr Settles · 2012
Earlier work this paper cites.
Automation bias: a systematic review of frequency, effect mediators, and mitigators
Kate Goddard, Abdul V. Roudsari, and Jeremy C. Wyatt · 2012
Earlier work this paper cites.
Layered architecture for quantum computing
N Cody Jones, Rodney Van Meter, Austin G Fowler, Peter L McMahon, Jungsang Kim, Thaddeus D Ladd, and Yoshihisa Yamamoto · 2012
Earlier work this paper cites.
Canonical microcircuits for predictive coding
André M. Bastos, W. Martin Usrey, Rick A Adams, George R. Mangun, Pascal Fries, and Karl J. Friston · 2012
Earlier work this paper cites.
Theory learning as stochastic search in the language of thought
Tomer David Ullman, Noah D. Goodman, and Joshua B. Tenenbaum · 2012
Earlier work this paper cites.
The simulation of european heat waves from an ensemble of regional climate models within the euro-cordex project
Robert Vautard, Andreas Gobiet, Daniela Jacob, Michal Belda, Augustin Colette, Michel Déqué, Jesús Fernández, Markel García-Díez, Klaus Goergen, Ivan Güttler, et al · 2013
Earlier work this paper cites.
Gfdl’s esm2 global coupled climate?carbon earth system models. part ii: Carbon system formulation and baseline simulation characteristics
John P. Dunne, Jasmin G. John, Elena Shevliakova, Ronald J. Stouffer, John P. Krasting, Sergey L. Malyshev, P. C. D. Milly, Lori T. Sentman, Alistair J. Adcroft, William Cooke, Krista A. Dunne, Stephen M. Griffies, Robert W. Hallberg, Matthew J. Harrison, Hiram Levy, Andrew T. Wittenberg, Peter J. Phillips, and Niki Zadeh · 2013
Earlier work this paper cites.
Computational approaches to energy materials
Aron Walsh, Alexey A. Sokol, and Richard Catlow · 2013
Earlier work this paper cites.
Commentary: The materials project: A materials genome approach to accelerating materials innovation
Anubhav Jain, Shyue Ping Ong, Geoffroy Hautier, Wei Chen, William Davidson Richards, Stephen T Dacek, Shreyas Cholia, Dan Gunter, D. Skinner, Gerbrand Ceder, and Kristin Aslaug Persson · 2013
Earlier work this paper cites.
Gaussian processes for big data
J. Hensman, Nicoló Fusi, and Neil Lawrence · 2013
Earlier work this paper cites.
A tutorial on adaptive design optimization
Jay I. Myung, Daniel R. Cavagnaro, and M. Pitt · 2013
Earlier work this paper cites.
Approximate Bayesian computation via regression density estimation
Yanan Fan, David J. Nott, and Scott A. Sisson · 2013
Earlier work this paper cites.
Asymptotic formulae for likelihood-based tests of new physics
Glen Cowan, Kyle Cranmer, Eilam Gross, and Ofer Vitells · 2013
Earlier work this paper cites.
Introduction to judea pearl’s do-calculus
Robert R. Tucci · 2013
Earlier work this paper cites.
Intrinsic motivation and reinforcement learning
Andrew G. Barto · 2013
Earlier work this paper cites.
Gis and agent-based models for humanitarian assistance
A. Crooks and Sarah C. Wise · 2013
Earlier work this paper cites.
An agent based model for studying optimal tax collection policy using experimental data: The cases of chile and italy
Nicolás Garrido and L. Mittone · 2013
Earlier work this paper cites.
A large scale, high resolution agent-based insurgency model
L. Overbey, B. Mitchell, Samuel Yaryan, and K. McCullough · 2013
Earlier work this paper cites.
Simulation as an engine of physical scene understanding
P. Battaglia, Jessica B. Hamrick, and J. Tenenbaum · 2013
Earlier work this paper cites.
Approximate bayesian image interpretation using generative probabilistic graphics programs
Vikash K. Mansinghka, Tejas D. Kulkarni, Yura N. Perov, and J. Tenenbaum · 2013
Earlier work this paper cites.
Hoomd-blue: A python package for high-performance molecular dynamics and hard particle monte carlo simulations
Joshua A. Anderson, Jens Glaser, and Sharon C. Glotzer · 2013
Earlier work this paper cites.
Syntax-guided synthesis
Rajeev Alur, Rastislav Bodik, Garvit Juniwal, Milo M. K. Martin, Mukund Raghothaman, Sanjit A. Seshia, Rishabh Singh, Armando Solar-Lezama, Emina Torlak, and Abhishek Udupa · 2013
Earlier work this paper cites.
From ordinary differential equations to structural causal models: the deterministic case
Joris M. Mooij, Dominik Janzing, and Bernhard Schölkopf · 2013
Earlier work this paper cites.
Data fusion: Resolving conflicts from multiple sources
Xin Dong, Laure Berti-Équille, and Divesh Srivastava · 2013
Earlier work this paper cites.
Synergistic challenges in data-intensive science and exascale computing: Doe ascac data subcommittee report
Jan-Jo. Chen, Alok N. Choudhary, Steven D. Feldman, Bruce Hendrickson, C. R. Johnson, Richard P. Mount, Vivek Sarkar, Vicky White, and Dean Williams · 2013
Earlier work this paper cites.
Development of the coastal storm modeling system (cosmos) for predicting the impact of storms on high-energy, active-margin coasts
Patrick L. Barnard, M. van Ormondt, Li H. Erikson, Jodi L. Eshleman, Cheryl J. Hapke, Peter Ruggiero, Peter N. Adams, and Amy C. Foxgrover · 2014
Earlier work this paper cites.
GPS-ABC: Gaussian process surrogate approximate Bayesian computation
Edward Meeds and Max Welling · 2014
Earlier work this paper cites.
Counterfactuals and the potential outcome model
S. Morgan and Christopher Winship · 2014
Earlier work this paper cites.
An agent-based model for mrna export through the nuclear pore complex
Mohammad Azimi, Evgeny Bulat, Karsten Weis, and Mohammad R. K. Mofrad · 2014
Earlier work this paper cites.
Probabilistic programming
A. Gordon, T. Henzinger, A. Nori, and S. Rajamani · 2014
Earlier work this paper cites.
Venture: a higher-order probabilistic programming platform with programmable inference
Vikash K. Mansinghka, Daniel Selsam, and Yura N. Perov · 2014
Earlier work this paper cites.
A new approach to probabilistic programming inference
F. Wood, Jan-Willem van de Meent, and Vikash K. Mansinghka · 2014
Earlier work this paper cites.
The Design and Implementation of Probabilistic Programming Languages
Noah D. Goodman and Andreas Stuhlmüller · 2014
Earlier work this paper cites.
Petascale high order dynamic rupture earthquake simulations on heterogeneous supercomputers
A. Heinecke, Alexander Breuer, Sebastian Rettenberger, M. Bader, A. Gabriel, C. Pelties, A. Bode, W. Barth, Xiangke Liao, K. Vaidyanathan, M. Smelyanskiy, and P. Dubey · 2014
Earlier work this paper cites.
A. Graves, Greg Wayne, and Ivo Danihelka · 2014
Earlier work this paper cites.
The no-u-turn sampler: adaptively setting path lengths in hamiltonian monte carlo
M. Hoffman and A. Gelman · 2014
Earlier work this paper cites.
Win-Stay, Lose-Sample: A simple sequential algorithm for approximating Bayesian inference
Elizabeth Bonawitz, Stephanie Denison, Alison Gopnik, and Thomas L. Griffiths · 2014
Earlier work this paper cites.
Predictive entropy search for efficient global optimization of black-box functions
José Miguel Hernández-Lobato, Matthew W. Hoffman, and Zoubin Ghahramani · 2014
Earlier work this paper cites.
A novel human-computer collaboration: combining novelty search with interactive evolution
Brian G. Woolley and Kenneth O. Stanley · 2014
Earlier work this paper cites.
Novelty search creates robots with general skills for exploration
Roby Velez and Jeff Clune · 2014
Earlier work this paper cites.
Build, compute, critique, repeat: Data analysis with latent variable models
David M. Blei · 2014
Earlier work this paper cites.
Robust control of uncertain systems: Classical results and recent developments
Ian R. Petersen and Roberto Tempo · 2014
Earlier work this paper cites.
Enzo: An adaptive mesh refinement code for astrophysics
The Enzo Collaboration Greg L. Bryan, Michael L. Norman, Brian W. O’Shea, Tom Abel, John H. Wise, Matthew J. Turk, Daniel R. Reynolds, D. Collins, Peng Wang, Samuel W. Skillman, Britton D. Smith, Robert Harkness, James Bordner, Ji hoon Kim, Michael Kuhlen, Haoting Xu, Nathan J. Goldbaum, Cameron B. Hummels, Alexei G. Kritsuk, Elizabeth J. Tasker, S. A. Skory, Christine M. Simpson, Oliver Hahn, Jeffrey S. Oishi, Geoffrey C. So, Fen Zhao, Renyue Cen, and Yuan Li · 2014
Earlier work this paper cites.
Anton 2: Raising the bar for performance and programmability in a special-purpose molecular dynamics supercomputer
David E. Shaw, J.P. Grossman, Joseph A. Bank, Brannon Batson, J. Adam Butts, Jack C. Chao, Martin M. Deneroff, Ron O. Dror, Amos Even, Christopher H. Fenton, Anthony Forte, Joseph Gagliardo, Gennette Gill, Brian Greskamp, C. Richard Ho, Douglas J. Ierardi, Lev Iserovich, Jeffrey S. Kuskin, Richard H. Larson, Timothy Layman, Li-Siang Lee, Adam K. Lerer, Chester Li, Daniel Killebrew, Kenneth M. Mackenzie, Shark Yeuk-Hai Mok, Mark A. Moraes, Rolf Mueller, Lawrence J. Nociolo, Jon L. Peticolas, Terry Quan, Daniel Ramot, John K. Salmon, Daniele P. Scarpazza, U. Ben Schafer, Naseer Siddique, Christopher W. Snyder, Jochen Spengler, Ping Tak Peter Tang, Michael Theobald, Horia Toma, Brian Towles, Benjamin Vitale, Stanley C. Wang, and Cliff Young · 2014
Earlier work this paper cites.
Probabilistic machine learning and artificial intelligence
Zoubin Ghahramani · 2015
Earlier work this paper cites.
Probabilistic numerics and uncertainty in computations
Philipp Hennig, Michael A. Osborne, and Mark A. Girolami · 2015
Earlier work this paper cites.
The aflow standard for high-throughput materials science calculations
Camilo E. Calderon, Jose J Plata, Cormac Toher, Corey Oses, Ohad Levy, Marco Fornari, Amir Natan, Michael J. Mehl, Gus L. W. Hart, Marco Buongiorno Nardelli, and Stefano Curtarolo · 2015
Earlier work this paper cites.
Machine learning components in deterministic models: hybrid synergy in the age of data
E. Goldstein and G. Coco · 2015
Earlier work this paper cites.
Thoughts on massively scalable gaussian processes
A. G. Wilson, Christoph Dann, and H. Nickisch · 2015
Earlier work this paper cites.
Deep learning
I. Goodfellow, Yoshua Bengio, and Aaron C. Courville · 2015
Earlier work this paper cites.
Bayesian uncertainty propagation using gaussian processes
Ilias Bilionis and Nicholas Zabaras · 2015
Earlier work this paper cites.
Cancer evolution: Mathematical models and computational inference
N. Beerenwinkel, R. Schwarz, M. Gerstung, and F. Markowetz · 2015
Earlier work this paper cites.
Reconstruction and simulation of neocortical microcircuitry
Henry Markram, Eilif Muller, Srikanth Ramaswamy, Michael W Reimann, Marwan Abdellah, Carlos Aguado Sanchez, Anastasia Ailamaki, Lidia Alonso-Nanclares, Nicolas Antille, Selim Arsever, et al · 2015
Earlier work this paper cites.
Approximating Likelihood Ratios with Calibrated Discriminative Classifiers
Kyle Cranmer, Juan Pavez, and Gilles Louppe · 2015
Earlier work this paper cites.
Optimization Monte Carlo: Efficient and embarrassingly parallel likelihood-free inference
Edward Meeds and Max Welling · 2015
Earlier work this paper cites.
Considerations and best practices in agent-based modeling to inform policy
Ross A. Hammond · 2015
Earlier work this paper cites.
Picture: A probabilistic programming language for scene perception
Tejas D. Kulkarni, P. Kohli, J. Tenenbaum, and Vikash K. Mansinghka · 2015
Earlier work this paper cites.
Human-level concept learning through probabilistic program induction
B. Lake, R. Salakhutdinov, and J. Tenenbaum · 2015
Earlier work this paper cites.
Learning to transduce with unbounded memory
Edward Grefenstette, K. Hermann, Mustafa Suleyman, and P. Blunsom · 2015
Earlier work this paper cites.
Variational inference with normalizing flows
Danilo Rezende and Shakir Mohamed · 2015
Earlier work this paper cites.
The Psychology and Neuroscience of Curiosity
Celeste Kidd and Benjamin Y. Hayden · 2015
Earlier work this paper cites.
Illuminating search spaces by mapping elites
Jean-Baptiste Mouret and Jeff Clune · 2015
Earlier work this paper cites.
Robots that can adapt like animals
Antoine Cully, Jeff Clune, Danesh Tarapore, and Jean-Baptiste Mouret · 2015
Earlier work this paper cites.
Scalable bayesian optimization using deep neural networks
Jasper Snoek, Oren Rippel, Kevin Swersky, Ryan Kiros, Nadathur Satish, Narayanan Sundaram, Mostofa Patwary, Mr Prabhat, and Ryan Adams · 2015
Earlier work this paper cites.
Learning structured output representation using deep conditional generative models
Kihyuk Sohn, Honglak Lee, and Xinchen Yan · 2015
Earlier work this paper cites.
Practical probabilistic programming with monads
A. Scibior, Zoubin Ghahramani, and Andrew D. Gordon · 2015
Earlier work this paper cites.
"data intensive sciences in high performance computing" panel at the 2015 international conference on high performance computing & simulation (hpcs 2015)
Zhiming Zhao · 2015
Earlier work this paper cites.
Big data challenges in climate science
John L. Schnase, Tsengdar J. Lee, Chris Mattmann, Christopher Lynnes, Luca Cinquini, Maglio Paul, Ramire, Andrew F. Hart, Dean N. Williams, Duane E. Waliser, Pamela Livingstone Rinsland, William Webster, Daniel Q. Duffy, Mark A. McInerney, Glenn S. Tamkin, Gerald Potter, and Laura Carrier · 2015
Earlier work this paper cites.
A new class of accurate, mesh-free hydrodynamic simulation methods
Philip F. Hopkins · 2015
Earlier work this paper cites.
Computing beyond moore’s law
John M Shalf and Robert Leland · 2015
Earlier work this paper cites.
Principles of solution of the governing equations
Jiri Blazek · 2015
Earlier work this paper cites.
Sensitivity of emergent sociohydrologic dynamics to internal system properties and external sociopolitical factors: Implications for water management
Yasmina Elshafei, Matthew Tonts, M Sivapalan, and MR Hipsey · 2016
Earlier work this paper cites.
Model inversion via multi-fidelity bayesian optimization: a new paradigm for parameter estimation in haemodynamics, and beyond
P. Perdikaris and G. Karniadakis · 2016
Earlier work this paper cites.
Multiscale modeling and simulation of brain blood flow
P. Perdikaris, L. Grinberg, and G. Karniadakis · 2016
Earlier work this paper cites.
Reproducibility in density functional theory calculations of solids
Kurt Lejaeghere, Gustav Bihlmayer, Torbjörn Björkman, Peter Blaha, Stefan Blügel, Volker Blum, Damien Caliste, Ivano Eligio Castelli, Stewart J Clark, Andrea Dal Corso, Stefano de Gironcoli, Thierry Deutsch, J. K. Dewhurst, Igor Di Marco, Claudia Draxl, Marcin Dulak, Olle Eriksson, José A. Flores-Livas, Kevin F. Garrity, Luigi Genovese, Paolo Giannozzi, Matteo Giantomassi, Stefan Goedecker, Xavier Gonze, Oscar Grånäs, Eberhard K. U. Gross, Andris Gulans, François Gygi, Don R. Hamann, Philip Hasnip, N. A. W. Holzwarth, Diana Iuşan, Dominik Jochym, François Jollet, Daniel Jones, Georg Kresse, Klaus Koepernik, Emine Küçükbenli, Yaroslav O Kvashnin, Inka L. M. Locht, Sven Lubeck, Martijn Marsman, Nicola Marzari, Ulrike Nitzsche, Lars Nordström, Taisuke Ozaki, Lorenzo Paulatto, Chris J. Pickard, Ward Poelmans, Matt Probert, Keith Refson, Manuel Richter, Gian-Marco Rignanese, Santanu Saha, Matthias Scheffler, Martin Schlipf, Karlheinz Schwarz, Sangeeta Sharma, Francesca Tavazza, Patrik Thunström, Alexandre Tkatchenko, Marc Torrent, David Vanderbilt, Michiel J. van Setten, Veronique Van Speybroeck, John Michael Wills, Jonathan R. Yates, Guo-Xu Zhang, and Stefaan Cottenier · 2016
Earlier work this paper cites.
Taking the human out of the loop: A review of bayesian optimization
B. Shahriari, Kevin Swersky, Ziyu Wang, Ryan P. Adams, and N. D. Freitas · 2016
Earlier work this paper cites.
Sparse gaussian processes for bayesian optimization
M. McIntire, D. Ratner, and S. Ermon · 2016
Earlier work this paper cites.
Sparse identification of nonlinear dynamics (sindy)
S. Brunton, J. Proctor, and N. Kutz · 2016
Earlier work this paper cites.
Inferring biological networks by sparse identification of nonlinear dynamics
N. Mangan, S. Brunton, J. Proctor, and J. Kutz · 2016
Earlier work this paper cites.
Learning in Implicit Generative Models
Shakir Mohamed and Balaji Lakshminarayanan · 2016
Earlier work this paper cites.
Likelihood-free inference by ratio estimation
Owen Thomas, Ritabrata Dutta, Jukka Corander, Samuel Kaski, and Michael U. Gutmann · 2016
Earlier work this paper cites.
Fast e-free inference of simulation models with Bayesian conditional density estimation
George Papamakarios and Iain Murray · 2016
Earlier work this paper cites.
Bayesian optimization for likelihood-free inference of simulator-based statistical models
Michael U. Gutmann and Jukka Corander · 2016
Earlier work this paper cites.
Extreme learning machine for multilayer perceptron
Jiexiong Tang, Chenwei Deng, and Guangbin Huang · 2016
Earlier work this paper cites.
Long-term causal effects via behavioral game theory
Panos Toulis and D. Parkes · 2016
Earlier work this paper cites.
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, J. Schulman, Jie Tang, and Wojciech Zaremba · 2016
Earlier work this paper cites.
Mastering the game of go with deep neural networks and tree search
D. Silver, Aja Huang, Chris J. Maddison, A. Guez, L. Sifre, G. V. D. Driessche, Julian Schrittwieser, Ioannis Antonoglou, Vedavyas Panneershelvam, Marc Lanctot, S. Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, T. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, and D. Hassabis · 2016
Earlier work this paper cites.
Understanding the dynamics of violent political revolutions in an agent-based framework
Alessandro Moro · 2016
Earlier work this paper cites.
Ldef formalism for agent-based model development
J. Bae and Il-Chul Moon · 2016
Earlier work this paper cites.
Mapping translation ’hot-spots’ in live cells by tracking single molecules of mrna and ribosomes
Zachary B Katz, Brian P. English, Timothée Lionnet, Young J. Yoon, Nilah Monnier, Ben Ovryn, Mark Bathe, and Robert H. Singer · 2016
Earlier work this paper cites.
Regulation of rna-binding proteins affinity to export receptors enables the nuclear basket proteins to distinguish and retain aberrant mrnas
M. Soheilypour and M. Mofrad · 2016
Earlier work this paper cites.
Edward: A library for probabilistic modeling, inference, and criticism
Dustin Tran, Alp Kucukelbir, Adji B. Dieng, M. Rudolph, Dawen Liang, and D. Blei · 2016
Earlier work this paper cites.
Practical optimal experiment design with probabilistic programs
L. Ouyang, Michael Henry Tessler, Daniel Ly, and Noah D. Goodman · 2016
Earlier work this paper cites.
Building machines that learn and think like people
B. Lake, Tomer D. Ullman, J. Tenenbaum, and S. Gershman · 2016
Earlier work this paper cites.
Plans, habits, and theory of mind
S. Gershman, Tobias Gerstenberg, Chris L. Baker, and F. Cushman · 2016
Earlier work this paper cites.
Inferring mass in complex scenes by mental simulation
Jessica B. Hamrick, P. Battaglia, T. Griffiths, and J. Tenenbaum · 2016
Earlier work this paper cites.
Hybrid computing using a neural network with dynamic external memory
A. Graves, Greg Wayne, M. Reynolds, Tim Harley, Ivo Danihelka, Agnieszka Grabska-Barwinska, Sergio Gomez Colmenarejo, Edward Grefenstette, Tiago Ramalho, J. Agapiou, Adrià Puigdomènech Badia, K. Hermann, Yori Zwols, Georg Ostrovski, Adam Cain, Helen King, C. Summerfield, P. Blunsom, K. Kavukcuoglu, and D. Hassabis · 2016
Earlier work this paper cites.
Tensorflow: A system for large-scale machine learning
Martín Abadi, P. Barham, Jianmin Chen, Z. Chen, Andy Davis, J. Dean, M. Devin, S. Ghemawat, Geoffrey Irving, M. Isard, M. Kudlur, Josh Levenberg, Rajat Monga, Sherry Moore, D. Murray, Benoit Steiner, P. Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Y. Yu, and Xiaoqiang Zhang · 2016
Earlier work this paper cites.
Defining and simulating open-ended novelty: requirements, guidelines, and challenges
W. Banzhaf, Bert O. Baumgaertner, G. Beslon, R. Doursat, J. Foster, B. McMullin, Vinicius Veloso de Melo, Thomas Miconi, L. Spector, S. Stepney, and Roger White · 2016
Earlier work this paper cites.
What learning systems do intelligent agents need? complementary learning systems theory updated
Dharshan Kumaran, Demis Hassabis, and James L. McClelland · 2016
Earlier work this paper cites.
Accelerating the evolution of cognitive behaviors through human-computer collaboration
Mathias Löwe and Sebastian Risi · 2016
Earlier work this paper cites.
Quality Diversity: A New Frontier for Evolutionary Computation
Justin K. Pugh, Lisa B. Soros, and Kenneth O. Stanley · 2016
Earlier work this paper cites.
Incremental learning algorithms and applications
Alexander Rainer Tassilo Gepperth and Barbara Hammer · 2016
Cited alongside, same era.
Biological and Machine Intelligence (BAMI)
J. Hawkins, S. Ahmad, S. Purdy, and A. Lavin · 2016
Cited alongside, same era.
Verified lifting of stencil computations
Shoaib Kamil, Alvin Cheung, Shachar Itzhaky, and Armando Solar-Lezama · 2016
Cited alongside, same era.
Production-run software failure diagnosis via adaptive communication tracking
Mohammad Mejbah Ul Alam and Abdullah Muzahid · 2016
Cited alongside, same era.
Density estimation using real nvp
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2016
Cited alongside, same era.
The generalization of latin hypercube sampling
Michael D Shields and Jiaxin Zhang · 2016
Nist big data interoperability framework: Volume 6, reference architecture
Wo L. Chang, David Boyd, and Orit Levin · 2019
Later among the works it cites.
Dan Lu and Daniel M. Ricciuto · 2019
Later among the works it cites.
Future directions for parallel and distributed computing: Spx 2019 workshop report
Scott D. Stoller, Michael Carbin, Sarita V. Adve, Kunal Agrawal, Guy E. Blelloch, Dan, Stanzione, Katherine A. Yelick, and Matei A. Zaharia · 2019
Later among the works it cites.
Arbor — a morphologically-detailed neural network simulation library for contemporary high-performance computing architectures
Nora Abi Akar, Benjamin Cumming, Vasileios Karakasis, Anne Küsters, Wouter Klijn, Alexander Peyser, and Stuart Yates · 2019
Later among the works it cites.
Inverse-designed metastructures that solve equations
Nasim Mohammadi Estakhri, Brian Edwards, and Nader Engheta · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Y. Gal and Zoubin Ghahramani · 2016
Cited alongside, same era.
Probabilistic programming in python using pymc3
John Salvatier, Thomas V. Wiecki, and Christopher J Fonnesbeck · 2016
Cited alongside, same era.
Neural networks for the prediction of organic chemistry reactions
Jennifer N. Wei, David Kristjanson Duvenaud, and Alán Aspuru-Guzik · 2016
Cited alongside, same era.
Subjective logic: A formalism for reasoning under uncertainty
Audun Jøsang · 2016
Cited alongside, same era.
The fair guiding principles for scientific data management and stewardship
Mark D. Wilkinson, Michel Dumontier, IJsbrand Jan Aalbersberg, Gabrielle Appleton, Myles Axton, Arie Baak, Niklas Blomberg, Jan-Willem Boiten, Luiz Olavo Bonino da Silva Santos, Philip E. Bourne, Jildau Bouwman, Anthony J. Brookes, Tim Clark, Mercè Crosas, Ingrid Dillo, Olivier Dumon, Scott C. Edmunds, Chris T. A. Evelo, Richard Finkers, Alejandra N. González-Beltrán, Alasdair J. G. Gray, Paul Groth, Carole A. Goble, Jeffrey S. Grethe, Jaap Heringa, Peter A. C. ‘t Hoen, Rob W. W. Hooft, Tobias Kuhn, Ruben G. Kok, Joost N. Kok, Scott J. Lusher, Maryann E. Martone, Albert Mons, Abel Laerte Packer, Bengt Persson, Philippe Rocca-Serra, Marco Roos, René C van Schaik, Susanna-Assunta Sansone, Erik Anthony Schultes, Thierry Sengstag, Ted Slater, George O. Strawn, Morris A. Swertz, Mark Thompson, Johan van der Lei, Erik M van Mulligen, Jan Velterop, Andra Waagmeester, Peter Wittenburg, Katy Wolstencroft, Jun Zhao, and Barend Mons · 2016
Cited alongside, same era.
Data integration and mining for synthetic biology design
Goksel Misirli, Jennifer S. Hallinan, Matthew R. Pocock, Phillip W. Lord, James Alastair McLaughlin, Herbert M. Sauro, and Anil Wipat · 2016
Cited alongside, same era.
Later among the works it cites.
Recent advances in physical reservoir computing: A review
Gouhei Tanaka, Toshiyuki Yamane, Jean Benoit Héroux, Ryosho Nakane, Naoki Kanazawa, Seiji Takeda, Hidetoshi Numata, Daiju Nakano, and Akira Hirose · 2019
Later among the works it cites.
Wave physics as an analog recurrent neural network
Tyler W Hughes, Ian AD Williamson, Momchil Minkov, and Shanhui Fan · 2019
Later among the works it cites.
A brief history of simulation neuroscience
Xue Fan and Henry Markram · 2019
Later among the works it cites.
Using neuroscience to develop artificial intelligence
Shimon Ullman · 2019
Later among the works it cites.
Gauge equivariant convolutional networks and the icosahedral cnn
Taco Cohen, Maurice Weiler, Berkay Kicanaoglu, and Max Welling · 2019
Later among the works it cites.
A comprehensive survey on graph neural networks
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and Philip S. Yu · 2019
Later among the works it cites.
How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
Later among the works it cites.
Invariant and equivariant graph networks
Haggai Maron, Heli Ben-Hamu, Nadav Shamir, and Yaron Lipman · 2019
Later among the works it cites.
Ffjord: Free-form continuous dynamics for scalable reversible generative models
Will Grathwohl, Ricky T. Q. Chen, Jesse Bettencourt, Ilya Sutskever, and David Kristjanson Duvenaud · 2019
Later among the works it cites.
Neural spline flows
Conor Durkan, Artur Bekasov, Iain Murray, and George Papamakarios · 2019
Later among the works it cites.
Quantum supremacy using a programmable superconducting processor
Frank Arute, Kunal Arya, Ryan Babbush, Dave Bacon, Joseph C Bardin, Rami Barends, Rupak Biswas, Sergio Boixo, Fernando GSL Brandao, David A Buell, et al · 2019
Later among the works it cites.
Perspective: Dimensions of the scientific method
Eberhard O. Voit · 2019
Later among the works it cites.
Big data: the end of the scientific method?
Sauro Succi and Peter V. Coveney · 2019
Later among the works it cites.
Capturing missing physics in climate model parameterizations using neural differential equations
Ali Ramadhan, J. Marshall, A. Souza, G. Wagner, Manvitha Ponnapati, and Christopher Rackauckas · 2020
Later among the works it cites.
The ai economist: Improving equality and productivity with ai-driven tax policies
Stephan Zheng, Alexander Trott, Sunil Srinivasa, Nikhil Naik, Melvin Gruesbeck, David Parkes, and R. Socher · 2020
Later among the works it cites.
Stagnation and scientific incentives
Jay Bhattacharya and Mikko Packalen · 2020
Later among the works it cites.
Nvidia simnet: an ai-accelerated multi-physics simulation framework
Oliver Hennigh, S. Narasimhan, M. Nabian, A. Subramaniam, K. Tangsali, M. Rietmann, J. Ferrandis, Wonmin Byeon, Zhiwei Fang, and Sanjay Choudhry · 2020
Later among the works it cites.
Matern gaussian processes on riemannian manifolds
V. Borovitskiy, Alexander Terenin, P. Mostowsky, and M. Deisenroth · 2020
Later among the works it cites.
Physics-informed gaussian process for online optimization of particle accelerators
A. Hanuka, X. Huang, J. Shtalenkova, D. Kennedy, A. Edelen, V. Lalchand, D. Ratner, and J. Duris · 2020
Later among the works it cites.
A composite neural network that learns from multi-fidelity data: Application to function approximation and inverse pde problems
X. Meng and G. Karniadakis · 2020
Later among the works it cites.
Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data
Luning Sun, Han Gao, Shaowu Pan, and Jian-Xun Wang · 2020
Later among the works it cites.
Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations
M. Raissi, A. Yazdani, and G. Karniadakis · 2020
Later among the works it cites.
Jax md: A framework for differentiable physics
S. Schoenholz and E. D. Cubuk · 2020
Later among the works it cites.
Orbnet: Deep learning for quantum chemistry using symmetry-adapted atomic-orbital features
Zhuoran Qiao, Matthew Welborn, Animashree Anandkumar, Frederick R. Manby, and Thomas F. Miller · 2020
Later among the works it cites.
Backpropagation algorithms and reservoir computing in recurrent neural networks for the forecasting of complex spatiotemporal dynamics
Pantelis R. Vlachas, Jaideep Pathak, B. Hunt, T. Sapsis, M. Girvan, E. Ott, and P. Koumoutsakos · 2020
Later among the works it cites.
On robustness of neural ordinary differential equations
Hanshu Yan, Jiawei Du, V. Tan, and Jiashi Feng · 2020
Later among the works it cites.
Bayesian optimization for materials design with mixed quantitative and qualitative variables
Yichi Zhang, D. Apley, and W. Chen · 2020
Later among the works it cites.
Sequential bayesian experiment design for optically detected magnetic resonance of nitrogen-vacancy centers
S. Dushenko, K. Ambal, and R. McMichael · 2020
Later among the works it cites.
Black-box optimization with local generative surrogates
Sergey Shirobokov, Vladislav Belavin, Michael Kagan, Andrey Ustyuzhanin, and Atilim Gunes Baydin · 2020
Later among the works it cites.
Up to two billion times acceleration of scientific simulations with deep neural architecture search
Muhammad F. Kasim, Duncan Watson-Parris, Lavinia Deaconu, S. Oliver, Peter W Hatfield, Dustin H. Froula, Gianluca Gregori, Michael Jarvis, Samar Khatiwala, Jun Korenaga, J. Topp-Mugglestone, E. Viezzer, and Sam M. Vinko · 2020
Later among the works it cites.
Improved surrogates in inertial confinement fusion with manifold and cycle consistencies
Rushil Anirudh, Jayaraman J. Thiagarajan, P. Bremer, and B. Spears · 2020
Later among the works it cites.
Machine learning for active matter
F. Cichos, K. Gustavsson, B. Mehlig, and G. Volpe · 2020
Later among the works it cites.
Deep learning of physical laws from scarce data
Zhao Chen, Y. Liu, and Hao Sun · 2020
Later among the works it cites.
Ai feynman: A physics-inspired method for symbolic regression
Silviu-Marian Udrescu and Max Tegmark · 2020
Later among the works it cites.
Ai feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity
Silviu-Marian Udrescu, Andrew Yong-Yi Tan, Jiahai Feng, Orisvaldo Neto, Tailin Wu, and Max Tegmark · 2020
Later among the works it cites.
sbi: A toolkit for simulation-based inference
Alvaro Tejero-Cantero, Jan Boelts, Michael Deistler, Jan-Matthis Lueckmann, Conor Durkan, Pedro J Gonçalves, David S Greenberg, and Jakob H Macke · 2020
Later among the works it cites.
Training deep neural density estimators to identify mechanistic models of neural dynamics
Pedro J Gonçalves, Jan-Matthis Lueckmann, Michael Deistler, Marcel Nonnenmacher, Kaan Öcal, Giacomo Bassetto, Chaitanya Chintaluri, William F Podlaski, Sara A Haddad, Tim P Vogels, et al · 2020
Later among the works it cites.
Differentiable likelihoods for fast inversion of’likelihood-free’dynamical systems
Hans Kersting, Nicholas Krämer, Martin Schiegg, Christian Daniel, Michael Tiemann, and Philipp Hennig · 2020
Later among the works it cites.
Lightning-Fast Gravitational Wave Parameter Inference through Neural Amortization
Arnaud Delaunoy, Antoine Wehenkel, Tanja Hinderer, Samaya Nissanke, Christoph Weniger, Andrew R. Williamson, and Gilles Louppe · 2020
Later among the works it cites.
Bayesian inference for biophysical neuron models enables stimulus optimization for retinal neuroprosthetics
Jonathan Oesterle, Christian Behrens, Cornelius Schröder, Thoralf Hermann, Thomas Euler, Katrin Franke, Robert G Smith, Guenther Zeck, and Philipp Berens · 2020
Later among the works it cites.
Causal inference and counterfactual prediction in machine learning for actionable healthcare
M. Prosperi, Yi Guo, M. Sperrin, J. Koopman, Jae Min, Xing He, S. Rich, Mo Wang, I. Buchan, and J. Bian · 2020
Later among the works it cites.
Maxime Peyrard and Robert West · 2020
Later among the works it cites.
Liuyi Yao, Zhixuan Chu, Sheng Li, Y. Li, Jing Gao, and A. Zhang · 2020
Later among the works it cites.
A survey of learning causality with data
Ruocheng Guo, Lu Cheng, Jundong Li, P. R. Hahn, and Huan Liu · 2020
Later among the works it cites.
Ai for science
Rick L. Stevens, Valerie E. Taylor, Jeffrey A. Nichols, Arthur B. Maccabe, Katherine A. Yelick, and David Brown · 2020
Later among the works it cites.
Challenges and opportunities with causal discovery algorithms: Application to alzheimer’s pathophysiology
Xinpeng Shen, Sisi Ma, P. Vemuri, György J. Simon, Michael W. Paul Ronald Clifford R. Andrew J. William John Weiner Aisen Petersen Jack Saykin Jagust Trojanowk, M. Weiner, P. Aisen, R. Petersen, C. Jack, A. Saykin, W. Jagust, J. Trojanowki, A. Toga, L. Beckett, R. Green, John C. Morris, L. Shaw, Z. Khachaturian, G. Sorensen, M. Carrillo, L. Kuller, M. Raichle, Steven M. Paul, P. Davies, H. Fillit, F. Hefti, D. Holtzman, M. M. Mesulam, W. Potter, P. Snyder, Adam Schwartz, T. Montine, Ronald G. Thomas, M. Donohue, Sarah Walter, Devon Gessert, T. Sather, G. Jiminez, Archana B. Balasubramanian, J. Mason, Iris Sim, D. Harvey, M. Bernstein, N. Fox, P. Thompson, N. Schuff, C. DeCarli, B. Borowski, J. Gunter, M. Senjem, David T. Jones, K. Kantarci, C. Ward, R. Koeppe, N. Foster, E. Reiman, K. Chen, C. Mathis, S. Landau, N. Cairns, Erin E. Franklin, L. Taylor-Reinwald, V. Lee, M. Korecka, Michał J. Figurski, K. Crawford, S. Neu, T. Foroud, S. Potkin, K. Faber, Sungeun Kim, K. Nho, L. Thal, N. Buckholtz, M. Albert, Richard Frank, John Hsiao, J. Kaye, J. Quinn, L. Silbert, Betty Lind, Raina Carter, Sara Dolen, L. Schneider, S. Pawluczyk, Mauricio Beccera, Liberty Teodoro, B. Spann, J. Brewer, Helen Vanderswag, A. Fleisher, J. Heidebrink, Joanne L. Lord, S. Mason, Colleen S. Albers, D. Knopman, Kris A. Johnson, R. Doody, J. Villanueva-Meyer, V. Pavlik, Victoria Shibley, M. Chowdhury, S. Rountree, Mimi Dang, Y. Stern, L. Honig, K. Bell, B. Ances, Maria Carroll, Mary L. Creech, M. Mintun, Stacy Schneider, A. Oliver, D. Marson, D. Geldmacher, M. N. Love, Randall Griffith, David Clark, J. Brockington, E. Roberson, Hillel Grossman, E. Mitsis, R. Shah, L. deToledo-Morrell, R. Duara, M. Greig-Custo, W. Barker, C. Onyike, D. D’Agostino, S. Kielb, M. Sadowski, Mohammed O. Sheikh, Anaztasia Ulysse, Mrunalini Gaikwad, P. Doraiswamy, J. Petrella, S. Borges-Neto, T. Wong, Edward Coleman, S. Arnold, J. Karlawish, D. Wolk, C. Clark, Charles D. Smith, G. Jicha, Peter Hardy, P. Sinha, Elizabeth Oates, G. Conrad, O. Lopez, MaryAnn Oakley, D. M. Simpson, A. Porsteinsson, Bonnie S. Goldstein, Kim Martin, Kelly M. Makino, M. Ismail, Connie Brand, A. Preda, D. Nguyen, K. Womack, D. Mathews, M. Quiceno, A. Levey, J. Lah, J. Cellar, J. Burns, R. Swerdlow, W. Brooks, L. Apostolova, K. Tingus, E. Woo, D. Silverman, P. Lu, G. Bartzokis, N. Graff-Radford, F. Parfitt, Kim Poki-Walker, M. Farlow, A. Hake, B. Matthews, J. Brosch, S. Herring, C. V. van Dyck, R. Carson, M. Macavoy, P. Varma, H. Chertkow, H. Bergman, Chris Hosein, S. Black, B. Stefanovic, Curtis Caldwell, G. Hsiung, B. Mudge, V. Sossi, H. Feldman, M. Assaly, E. Finger, S. Pasternack, Irina Rachisky, J. Rogers, Dick Trost, A. Kertesz, C. Bernick, D. Munic, E. Rogalski, Kristine Lipowski, S. Weintraub, B. Bonakdarpour, D. Kerwin, Chuang-Kuo Wu, N. Johnson, C. Sadowsky, Teresa Villena, R. Turner, Kathleen Johnson, Brigid Reynolds, R. Sperling, Keith A. Johnson, G. Marshall, J. Yesavage, Joy L. Taylor, B. Lane, A. Rosen, J. Tinklenberg, M. Sabbagh, C. Belden, S. Jacobson, Sherye A. Sirrel, N. Kowall, R. Killiany, A. Budson, A. Norbash, P. L. Johnson, T. Obisesan, S. Wolday, J. Allard, A. Lerner, P. Ogrocki, C. Tatsuoka, Parianne Fatica, E. Fletcher, P. Maillard, J. Olichney, O. Carmichael, S. Kittur, M. Borrie, T.-Y. Lee, R. Bartha, Sterling C. Johnson, S. Asthana, C. Carlsson, P. Tariot, Anna D. Burke, A. Milliken, Nadira Trncic, S. Reeder, V. Bates, H. Capote, M. Rainka, D. Scharre, M. Kataki, B. Kelly, E. Zimmerman, Dzintra F Celmins, Alice D. Brown, G. Pearlson, K. Blank, K. Anderson, L. Flashman, M. Seltzer, M. Hynes, R. Santulli, K. Sink, Leslie Gordineer, J. Williamson, P. Garg, Franklin S. Watkins, B. Ott, G. Tremont, L. Daiello, S. Salloway, P. Malloy, S. Correia, H. Rosen, B. Miller, D. Perry, J. Mintzer, K. Spicer, D. Bachman, N. Pomara, Raymundo T. Hernando, Antero Sarrael, S. Schultz, Karen E Smith, Hristina K Koleva, Ki Won Nam, Hyungsub Shim, N. Relkin, Gloria Chaing, Michael P. Lin, L. Ravdin, Amanda G. Smith, Balebail Ashok Raj, and Kristin Fargher · 2020
Later among the works it cites.
Clevrer: Collision events for video representation and reasoning
Kexin Yi, Chuang Gan, Yunzhu Li, P. Kohli, Jiajun Wu, A. Torralba, and J. Tenenbaum · 2020
Later among the works it cites.
Causal bayesian optimization
Virginia Aglietti, Xiaoyu Lu, Andrei Paleyes, and Javier Gonz’ alez · 2020
Later among the works it cites.
L. Oneto and Silvia Chiappa · 2020
Later among the works it cites.
Algorithmic fairness from a non-ideal perspective
Sina Fazelpour and Zachary Chase Lipton · 2020
Later among the works it cites.
Open problems in cooperative ai
A. Dafoe, Edward Hughes, Yoram Bachrach, Tantum Collins, Kevin R. McKee, Joel Z. Leibo, K. Larson, and T. Graepel · 2020
Later among the works it cites.
Mastering atari, go, chess and shogi by planning with a learned model
Julian Schrittwieser, Ioannis Antonoglou, Thomas Hubert, K. Simonyan, L. Sifre, Simon Schmitt, A. Guez, Edward Lockhart, D. Hassabis, T. Graepel, T. Lillicrap, and D. Silver · 2020
Later among the works it cites.
The hanabi challenge: A new frontier for ai research
N. Bard, Jakob N. Foerster, A. P. S. Chandar, Neil Burch, Marc Lanctot, H. F. Song, Emilio Parisotto, Vincent Dumoulin, Subhodeep Moitra, Edward Hughes, Iain Dunning, Shibl Mourad, H. Larochelle, Marc G. Bellemare, and Michael H. Bowling · 2020
Later among the works it cites.
Emergent tool use from multi-agent autocurricula
Bowen Baker, I. Kanitscheider, Todor Markov, Yi Wu, Glenn Powell, Bob McGrew, and Igor Mordatch · 2020
Later among the works it cites.
Using machine learning to emulate agent-based simulations
C. Angione, E. Silverman, and E. Yaneske · 2020
Later among the works it cites.
Inferring signaling pathways with probabilistic programming
David Merrell and A. Gitter · 2020
Later among the works it cites.
Simulation-based inference for global health decisions
C. S. D. Witt, Bradley Gram-Hansen, Nantas Nardelli, Andrew Gambardella, R. Zinkov, P. Dokania, N. Siddharth, Ana Belén Espinosa-González, A. Darzi, Philip H. S. Torr, and Atilim Gunes Baydin · 2020
Later among the works it cites.
Spacecraft collision risk assessment with probabilistic programming
G. Acciarini, Francesco Pinto, Sascha Metz, Sarah Boufelja, S. Kaczmarek, K. Merz, Jose Martinez-Heras, F. Letizia, C. Bridges, and Atilim Gunes Baydin · 2020
Later among the works it cites.
Simulation-based inference for global health decisions
Christian Schroeder de Witt, Bradley Gram-Hansen, Nantas Nardelli, Andrew Gambardella, Rob Zinkov, Puneet Dokania, N. Siddharth, Ana Belen Espinosa-Gonzalez, Ara Darzi, Philip Torr, and Atılım Güneş Baydin · 2020
Later among the works it cites.
Barking up the right tree: an approach to search over molecule synthesis dags
John Bradshaw, Brooks Paige, Matt J. Kusner, Marwin H. S. Segler, and José Miguel Hernández-Lobato · 2020
Later among the works it cites.
Bayesian models of conceptual development: Learning as building models of the world
Tomer D. Ullman and J. Tenenbaum · 2020
Later among the works it cites.
The child as hacker
Joshua Rule, J. Tenenbaum, and S. Piantadosi · 2020
Later among the works it cites.
Dreamcoder: Growing generalizable, interpretable knowledge with wake-sleep bayesian program learning
Kevin Ellis, Catherine Wong, Maxwell Nye, Mathias Sablé-Meyer, Luc Cary, Lucas Morales, Luke Hewitt, Armando Solar-Lezama, and J. Tenenbaum · 2020
Later among the works it cites.
Difftaichi: Differentiable programming for physical simulation
Yuanming Hu, Luke Anderson, Tzu-Mao Li, Qi Sun, N. Carr, Jonathan Ragan-Kelley, and F. Durand · 2020
Later among the works it cites.
Multiscale simulation approaches to modeling drug-protein binding
Benjamin R. Jagger, Sarah E. Kochanek, Susanta Haldar, Rommie E. Amaro, and Adrian J. Mulholland · 2020
Later among the works it cites.
A simple differentiable programming language
M. Abadi and G. Plotkin · 2020
Later among the works it cites.
The information theory of individuality
David Krakauer, Nils Bertschinger, Eckehard Olbrich, Jessica C. Flack, and Nihat Ay · 2020
Later among the works it cites.
Algorithmic probability-guided machine learning on non-differentiable spaces
Santiago Hernández-Orozco, Hector Zenil, Jürgen Riedel, Adam Uccello, Narsis Aftab Kiani, and Jesper Tegnér · 2020
Later among the works it cites.
Novelty Search makes Evolvability Inevitable
Stephane Doncieux, Giuseppe Paolo, Alban Laflaquière, and Alexandre Coninx · 2020
Later among the works it cites.
Exploring Exploration: Comparing Children with RL Agents in Unified Environments
Eliza Kosoy, Jasmine Collins, David M. Chan, Sandy Huang, Deepak Pathak, Pulkit Agrawal, John Canny, Alison Gopnik, and Jessica B. Hamrick · 2020
Later among the works it cites.
Lessons from archives: Strategies for collecting sociocultural data in machine learning
Eun Seo Jo and Timnit Gebru · 2020
Later among the works it cites.
Sumedh A. Sontakke, Arash Mehrjou, Laurent Itti, and Bernhard Schölkopf · 2020
Later among the works it cites.
Scaling MAP-Elites to Deep Neuroevolution
Cédric Colas, Joost Huizinga, Vashisht Madhavan, and Jeff Clune · 2020
Later among the works it cites.
Materials acceleration platforms: On the way to autonomous experimentation
Martha M Flores-Leonar, L. M. Mejía-Mendoza, Andrés Aguilar-Granda, Benjamín Sánchez-Lengeling, Hermann Tribukait, Carlos Amador-Bedolla, and Alán Aspuru-Guzik · 2020
Later among the works it cites.
Self-driving laboratory for accelerated discovery of thin-film materials
Benjamin P. MacLeod, Fraser G. L. Parlane, Thomas D. Morrissey, Florian Häse, Loïc M. Roch, Kevan E Dettelbach, R. Moreira, Lars P. E. Yunker, Michael Rooney, J. R. Deeth, Veronica Lai, G. J. Ng, Henry Situ, Regan-Heng Zhang, Michael S. Elliott, Ted H. Haley, David J. Dvorak, Alán Aspuru-Guzik, Jason E. Hein, and Curtis P. Berlinguette · 2020
Later among the works it cites.
Memo: A deep network for flexible combination of episodic memories
Andrea Banino, Adrià Puigdomènech Badia, Raphael Köster, Martin J. Chadwick, Vinícius Flores Zambaldi, Demis Hassabis, Caswell Barry, Matthew M. Botvinick, Dharshan Kumaran, and Charles Blundell · 2020
Later among the works it cites.
Embracing change: Continual learning in deep neural networks
Raia Hadsell, Dushyant Rao, Andrei A. Rusu, and Razvan Pascanu · 2020
Later among the works it cites.
Product kanerva machines: Factorized bayesian memory
Adam H. Marblestone, Y. Wu, and Greg Wayne · 2020
Later among the works it cites.
MISIM: A Novel Code Similarity System, 2020
Fangke Ye, Shengtian Zhou, Anand Venkat, Ryan Marcus, Nesime Tatbul, Jesmin Jahan Tithi, Niranjan Hasabnis, Paul Petersen, Timothy Mattson, Tim Kraska, Pradeep Dubey, Vivek Sarkar, and Justin Gottschlich · 2020
Later among the works it cites.
Deep learning enabled inverse design in nanophotonics
Sunae So, Trevon Badloe, Jae-Kyo Noh, J. Bravo-Abad, and J. Rho · 2020
Later among the works it cites.
Machine-enabled inverse design of inorganic solid materials: promises and challenges
Juhwan Noh, Geun Ho Gu, Sungwon Kim, and Yousung Jung · 2020
Later among the works it cites.
Invertible generative models for inverse problems: mitigating representation error and dataset bias
Muhammad Asim, Max Daniels, Oscar Leong, Ali Ahmed, and Paul Hand · 2020
Later among the works it cites.
He Sun and Katherine L Bouman · 2020
Later among the works it cites.
Variational inference for computational imaging inverse problems
Francesco Tonolini, Jack Radford, Alex Turpin, Daniele Faccio, and Roderick Murray-Smith · 2020
Later among the works it cites.
Amortized finite element analysis for fast pde-constrained optimization
Tianju Xue, Alex Beatson, Sigrid Adriaenssens, and Ryan Adams · 2020
Later among the works it cites.
Benchmarking deep inverse models over time, and the neural-adjoint method
Simiao Ren, Willie Padilla, and Jordan Malof · 2020
Later among the works it cites.
Hao Wu, Jonas Köhler, and Frank Noé · 2020
Later among the works it cites.
Survae flows: Surjections to bridge the gap between vaes and flows
Didrik Nielsen, Priyank Jaini, Emiel Hoogeboom, Ole Winther, and Max Welling · 2020
Later among the works it cites.
Neural-adjoint method for the inverse design of all-dielectric metasurfaces
Yang Deng, Simiao Ren, Kebin Fan, Jordan M Malof, and Willie J Padilla · 2020
Later among the works it cites.
On the quantification and efficient propagation of imprecise probabilities with copula dependence
Jiaxin Zhang and Michael Shields · 2020
Later among the works it cites.
Bayesian neural ordinary differential equations
R. Dandekar, Vaibhav Dixit, Mohamed Tarek, Aslan Garcia-Valadez, and C. Rackauckas · 2020
Later among the works it cites.
Neural circuit policies enabling auditable autonomy
Mathias Lechner, Ramin M. Hasani, Alexander Amini, Thomas A. Henzinger, Daniela Rus, and Radu Grosu · 2020
Later among the works it cites.
Algorithmic Information Dynamics
H. Zenil, N. A. Kiani, F. S Abrahão, and J. N. Tegnér · 2020
Later among the works it cites.
Chemos: An orchestration software to democratize autonomous discovery
Loïc M. Roch, Florian Häse, Christoph Kreisbeck, Teresa Tamayo-Mendoza, Lars P. E. Yunker, Jason E. Hein, and Alán Aspuru-Guzik · 2020
Later among the works it cites.
A deep learning approach to programmable rna switches
Nicolaas M. Angenent-Mari, Alexander S Garruss, L. Soenksen, G. Church, and J. J. Collins · 2020
Later among the works it cites.
Learning to complement humans
B. Wilder, E. Horvitz, and Ece Kamar · 2020
Later among the works it cites.
Rapid trust calibration through interpretable and uncertainty-aware ai
Richard J. Tomsett, Alun David Preece, Dave Braines, Federico Cerutti, Supriyo Chakraborty, Mani B. Srivastava, Gavin Pearson, and Lance M. Kaplan · 2020
Later among the works it cites.
Advancing fusion with machine learning research needs workshop report
David A. Humphreys, Ana Kupresanin, Mark D. Boyer, J. M. Canik, C. S. Chang, Eric C. Cyr, R. S. Granetz, Jeffrey A. F. Hittinger, Egemen Kolemen, Earl Christopher Lawrence, Valerio Pascucci, Anggi Patra, and David P. Schissel · 2020
Later among the works it cites.
Convergence of artificial intelligence and high performance computing on nsf-supported cyberinfrastructure
Eliu A. Huerta, Asad Khan, Edward Davis, Colleen Bushell, William Gropp, Daniel S. Katz, Volodymyr V. Kindratenko, Seid Koric, William T. C. Kramer, Brendan McGinty, Kenton McHenry, and Aaron Saxton · 2020
Later among the works it cites.
Differentially private synthetic medical data generation using convolutional gans
Amirsina Torfi, Edward A. Fox, and Chandan K. Reddy · 2020
Later among the works it cites.
Reliability of supervised machine learning using synthetic data in health care: Model to preserve privacy for data sharing
Debbie Rankin, Michaela M. Black, Raymond R. Bond, Jonathan G. Wallace, Maurice D. Mulvenna, and Gorka Epelde · 2020
Later among the works it cites.
A survey on machine learning for data fusion
Tong Meng, Xuyang Jing, Zheng Yan, and Witold Pedrycz · 2020
Later among the works it cites.
Understanding the landscape of scientific software used on high-performance computing platforms
Alexander M. Grannan, Kanika Sood, Boyana Norris, and Anshu Dubey · 2020
Later among the works it cites.
Rapid development of cloud-native intelligent data pipelines for scientific data streams using the haste toolkit
Ben Blamey, Salman Zubair Toor, Martin Dahlö, Håkan Wieslander, Philip J Harrison, Ida-Maria Sintorn, Alan Sabirsh, Carolina Wählby, Ola Spjuth, and Andreas Hellander · 2020
Later among the works it cites.
Fast Stencil-Code Computation on a Wafer-Scale Processor
Kamil Rocki, Dirk Van Essendelft, Ilya Sharapov, Robert Schreiber, Michael Morrison, Vladimir Kibardin, Andrey Portnoy, Jean Francois Dietiker, Madhava Syamlal, and Michael James · 2020
Later among the works it cites.
Establishing the quantum supremacy frontier with a 281 pflop/s simulation
Benjamin Villalonga, Dmitry Lyakh, Sergio Boixo, Hartmut Neven, Travis S Humble, Rupak Biswas, Eleanor G Rieffel, Alan Ho, and Salvatore Mandrà · 2020
Later among the works it cites.
Machine learning for quantum matter
Juan Carrasquilla · 2020
Later among the works it cites.
Analog architectures for neural network acceleration based on non-volatile memory
T Patrick Xiao, Christopher H Bennett, Ben Feinberg, Sapan Agarwal, and Matthew J Marinella · 2020
Later among the works it cites.
Inference in artificial intelligence with deep optics and photonics
Gordon Wetzstein, Aydogan Ozcan, Sylvain Gigan, Shanhui Fan, Dirk Englund, Marin Soljačić, Cornelia Denz, David AB Miller, and Demetri Psaltis · 2020
Later among the works it cites.
A review of learning in biologically plausible spiking neural networks
Aboozar Taherkhani, Ammar Belatreche, Yuhua Li, Georgina Cosma, Liam P. Maguire, and T. Martin McGinnity · 2020
Later among the works it cites.
From captcha to commonsense: How brain can teach us about artificial intelligence
Dileep George, Miguel Lázaro-Gredilla, and J. Swaroop Guntupalli · 2020
Later among the works it cites.
Pim de Haan, Taco Cohen, and Max Welling · 2020
Later among the works it cites.
A deep learning approach to antibiotic discovery
Jonathan M. Stokes, Kevin Yang, Kyle Swanson, Wengong Jin, Andres Cubillos-Ruiz, Nina M. Donghia, Craig R Macnair, Shawn French, Lindsey A. Carfrae, Zohar Bloom-Ackermann, Victoria M. Tran, Anush Chiappino-Pepe, Ahmed H. Badran, Ian W. Andrews, Emma J. Chory, George M. Church, Eric D. Brown, T. Jaakkola, Regina Barzilay, and James J. Collins · 2020
Later among the works it cites.
Reinforcement learning for molecular design guided by quantum mechanics
Gregor N. C. Simm, Robert Pinsler, and José Miguel Hernández-Lobato · 2020
Later among the works it cites.
Theoretical aspects of group equivariant neural networks
Carlos Esteves · 2020
Later among the works it cites.
Learning to simulate complex physics with graph networks
Alvaro Sanchez-Gonzalez, Jonathan Godwin, Tobias Pfaff, Rex Ying, Jure Leskovec, and Peter W. Battaglia · 2020
Later among the works it cites.
Autosimulate:(quickly) learning synthetic data generation
Harkirat Singh Behl, Atilim Güneş Baydin, Ran Gal, Philip HS Torr, and Vibhav Vineet · 2020
Later among the works it cites.
Deflating dataset bias using synthetic data augmentation
Nikita Jaipuria, Xianling Zhang, Rohan Bhasin, Mayar Arafa, Punarjay Chakravarty, Shubham Shrivastava, Sagar Manglani, and Vidya N Murali · 2020
Later among the works it cites.
Differentiable rendering: A survey
Hiroharu Kato, Deniz Beker, Mihai Morariu, Takahiro Ando, Toru Matsuoka, Wadim Kehl, and Adrien Gaidon · 2020
Later among the works it cites.
Normalizing flows: An introduction and review of current methods
Ivan Kobyzev, Simon Prince, and Marcus Brubaker · 2020
Later among the works it cites.
Equivariant flows: exact likelihood generative learning for symmetric densities
Jonas Köhler, Leon Klein, and Frank Noé · 2020
Later among the works it cites.
Anthropomorphism in ai
Arleen Salles, Kathinka Evers, and Michele Farisco · 2020
Later among the works it cites.
Quantum computers as universal quantum simulators: State-of-the-art and perspectives
Francesco Tacchino, Alessandro Chiesa, Stefano Carretta, and Dario Gerace · 2020
Later among the works it cites.
Quantum computational chemistry
Sam McArdle, Suguru Endo, Alán Aspuru-Guzik, Simon C Benjamin, and Xiao Yuan · 2020
Later among the works it cites.
A non-review of quantum machine learning: trends and explorations
Vedran Dunjko and Peter Wittek · 2020
Later among the works it cites.
Tensorflow quantum: A software framework for quantum machine learning
Michael Broughton, Guillaume Verdon, Trevor McCourt, Antonio J Martinez, Jae Hyeon Yoo, Sergei V Isakov, Philip Massey, Ramin Halavati, Murphy Yuezhen Niu, Alexander Zlokapa, et al · 2020
Later among the works it cites.
Machine learning–accelerated computational fluid dynamics
D. Kochkov, J. A. Smith, Ayya Alieva, Qing Wang, M. Brenner, and Stephan Hoyer · 2021
Closest in time.
Physics-informed machine learning
G. Karniadakis, I. Kevrekidis, Lu Lu, P. Perdikaris, Sifan Wang, and Liu Yang · 2021
Closest in time.
Physics-informed neural networks (pinns) for fluid mechanics: A review
Shengze Cai, Zhiping Mao, Zhicheng Wang, Minglang Yin, and G. Karniadakis · 2021
Closest in time.
Ginns: Graph-informed neural networks for multiscale physics
E. J. Hall, Søren Taverniers, M. Katsoulakis, and D. Tartakovsky · 2021
Closest in time.
B-pinns: Bayesian physics-informed neural networks for forward and inverse pde problems with noisy data
L. Yang, X. Meng, and G. Karniadakis · 2021
Closest in time.
Orbnet denali: A machine learning potential for biological and organic chemistry with semi-empirical cost and dft accuracy
Anders S. Christensen, Sai Krishna Sirumalla, Zhuoran Qiao, Michael B. O’Connor, Daniel G. A. Smith, Feizhi Ding, Peter J. Bygrave, Anima Anandkumar, Matthew Welborn, Frederick R. Manby, and Thomas F. Miller · 2021
Closest in time.
Symmetry-aware actor-critic for 3d molecular design
Gregor N. C. Simm, Robert Pinsler, Gábor Csányi, and José Miguel Hernández-Lobato · 2021
Closest in time.
Unite: Unitary n-body tensor equivariant network with applications to quantum chemistry
Zhuoran Qiao, Anders S. Christensen, Frederick R. Manby, Matthew Welborn, Anima Anandkumar, and Thomas F. Miller · 2021
Closest in time.
Physically-consistent generative adversarial networks for coastal flood visualization
Björn Lütjens, Brandon Leshchinskiy, Christian Requena-Mesa, F. Chishtie, Natalia Díaz Rodríguez, Océane Boulais, Aruna Sankaranarayanan, A. Piña, Y. Gal, Chedy Raïssi, Alexander Lavin, and Dava Newman · 2021
Closest in time.
Golem: An algorithm for robust experiment and process optimization
Matteo Aldeghi, Florian Hase, Riley J. Hickman, Isaac Tamblyn, and Alán Aspuru-Guzik · 2021
Closest in time.
Deep adaptive design: Amortizing sequential bayesian experimental design
Adam Foster, Desi R. Ivanova, I. Malik, and Tom Rainforth · 2021
Closest in time.
Gemini: Dynamic bias correction for autonomous experimentation and molecular simulation
Riley J. Hickman, Florian Hase, L. Roch, and Alán Aspuru-Guzik · 2021
Closest in time.
The digital revolution of earth-system science
P. Bauer, P. Dueben, Torsten Hoefler, T. Quintino, T. Schulthess, and N. Wedi · 2021
Closest in time.
Digital twin earth – coasts: Developing a fast and physics-informed surrogate model for coastal floods via neural operators
Peishi Jiang, Nis Meinert, Helga Jordão, Constantin Weisser, Simon J. Holgate, Alexander Lavin, Bjorn Lutjens, Dava Newman, Haruko M. Wainwright, Catherine Walker, and Patrick L. Barnard · 2021
Closest in time.
Bananas: Bayesian optimization with neural architectures for neural architecture search
Colin White, W. Neiswanger, and Yash Savani · 2021
Closest in time.
Technology readiness levels for machine learning systems, 2021
Alexander Lavin, Ciarán M. Gilligan-Lee, Alessya Visnjic, Siddha Ganju, Dava Newman, Sujoy Ganguly, Danny Lange, Atılım Güneş Baydin, Amit Sharma, Adam Gibson, Yarin Gal, Eric P. Xing, Chris Mattmann, and James Parr · 2021
Closest in time.
Benchmarking simulation-based inference
Jan-Matthis Lueckmann, Jan Boelts, David Greenberg, Pedro Goncalves, and Jakob Macke · 2021
Closest in time.
Neural approximate sufficient statistics for implicit models
Yanzhi Chen, Dinghuai Zhang, Michael U Gutmann, Aaron Courville, and Zhanxing Zhu · 2021
Closest in time.
Truncated marginal neural ratio estimation
Benjamin Miller, Alex Cole, Patrick Forré, Gilles Louppe, and Christoph Weniger · 2021
Closest in time.
Real-time gravitational-wave science with neural posterior estimation
Maximilian Dax, Stephen R. Green, Jonathan Gair, Jakob H. Macke, Alessandra Buonanno, and Bernhard Schölkopf · 2021
Closest in time.
Towards constraining warm dark matter with stellar streams through neural simulation-based inference
Joeri Hermans, Nilanjan Banik, Christoph Weniger, Gianfranco Bertone, and Gilles Louppe · 2021
Closest in time.
Simulation-based inference of evolutionary parameters from adaptation dynamics using neural networks
Grace Avecilla, Julie Chuong, Fangfei Li, Gavin J Sherlock, David Gresham, and Yoav Ram · 2021
Closest in time.
Neuronal circuits overcome imbalance in excitation and inhibition by adjusting connection numbers
Nirit Sukenik, Oleg Vinogradov, Eyal Weinreb, Menahem Segal, Anna Levina, and Elisha Moses · 2021
Closest in time.
Interrogating theoretical models of neural computation with emergent property inference
Sean R Bittner, Agostina Palmigiano, Alex T Piet, Chunyu A Duan, Carlos D Brody, Kenneth D Miller, and John P Cunningham · 2021
Closest in time.
Ancestral circuits for vertebrate colour vision emerge at the first retinal synapse
Takeshi Yoshimatsu, Philipp Bartel, Cornelius Schröder, Filip K Janiak, Francois St-Pierre, Philipp Berens, and Tom Baden · 2021
Closest in time.
Neural posterior domain randomization
Fabio Muratore, Theo Gruner, Florian Wiese, Boris Belousov, Michael Gienger, and Jan Peters · 2021
Closest in time.
Simulation-based bayesian inference for multi-fingered robotic grasping
Norman Marlier, Olivier Brüls, and Gilles Louppe · 2021
Closest in time.
Parametrized classifiers for optimal EFT sensitivity
Siyu Chen, Alfredo Glioti, Giuliano Panico, and Andrea Wulzer · 2021
Closest in time.
Machine Learning the Higgs-Top CP Phase
Rahool Kumar Barman, Dorival Gonçalves, and Felix Kling · 2021
Closest in time.
Constraining CP-violation in the Higgs-top-quark interaction using machine-learning-based inference
Henning Bahl and Simon Brass · 2021
Closest in time.
Toward causal representation learning
B. Scholkopf, Francesco Locatello, Stefan Bauer, N. Ke, Nal Kalchbrenner, Anirudh Goyal, and Yoshua Bengio · 2021
Closest in time.
Patterns, predictions, and actions: A story about machine learning
Moritz Hardt and B. Recht · 2021
Closest in time.
On pearl’s hierarchy and the foundations of causal inference
E. Bareinboim, Juan David Correa, D. Ibeling, and Thomas F. Icard · 2021
Closest in time.
Neuro-symbolic neurodegenerative disease modeling as probabilistic programmed deep kernels
Alexander Lavin · 2021
Closest in time.
On determinism of game engines used for simulation-based autonomous vehicle verification
Greg Chance, A. Ghobrial, Kevin McAreavey, S. Lemaignan, T. Pipe, and K. Eder · 2021
Closest in time.
A counterfactual simulation model of causal judgments for physical events
Tobias Gerstenberg, Noah D. Goodman, D. Lagnado, and J. Tenenbaum · 2021
Closest in time.
Causalcity: Complex simulations with agency for causal discovery and reasoning
Daniel McDuff, Yale Song, Jiyoung Lee, Vibhav Vineet, Sai Vemprala, N. Gyde, Hadi Salman, Shuang Ma, K. Sohn, and Ashish Kapoor · 2021
Closest in time.
Nobel turing challenge: creating the engine for scientific discovery
H. Kitano · 2021
Closest in time.
Automating turbulence modelling by multi-agent reinforcement learning
Guido Novati, Hugues Lascombes de Laroussilhe, and Petros Koumoutsakos · 2021
Closest in time.
Multi-agent deep reinforcement learning: a survey
Sven Gronauer and K. Diepold · 2021
Closest in time.
The minerl basalt competition on learning from human feedback
Rohin Shah, Cody Wild, Steven H. Wang, Neel Alex, Brandon Houghton, William H. Guss, Sharada Prasanna Mohanty, Anssi Kanervisto, Stephanie Milani, Nicholay Topin, P. Abbeel, Stuart J. Russell, and Anca D. Dragan · 2021
Closest in time.
Generating agent-based models from scratch with genetic programming
Rory Greig and Jordi Arranz · 2021
Closest in time.
Intra-group decision-making in agent-based models
Allegra A Beal Cohen, Rachata Muneepeerakul, and Gregory A. Kiker · 2021
Closest in time.
Insights for ai from the human mind
G. Marcus and E. Davis · 2021
Closest in time.
Highly accurate protein structure prediction with alphafold
John M Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Zídek, Anna Potapenko, Alex Bridgland, Clemens Meyer, Simon A A Kohl, Andy Ballard, Andrew Cowie, Bernardino Romera-Paredes, Stanislav Nikolov, Rishub Jain, Jonas Adler, Trevor Back, Stig Petersen, David A. Reiman, Ellen Clancy, Michal Zielinski, Martin Steinegger, Michalina Pacholska, Tamas Berghammer, Sebastian Bodenstein, David Silver, Oriol Vinyals, Andrew W. Senior, Koray Kavukcuoglu, Pushmeet Kohli, and Demis Hassabis · 2021
Closest in time.
Lammps - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales
Aidan P. Thompson, H. Metin Aktulga, Richard Berger, Dan S. Bolintineanu, W. Michael Brown, Paul S. Crozier, Pieter J. in ’t Veld, Axel Kohlmeyer, Stan G. Moore, Trung Dac Nguyen, Ray Shan, Mark Stevens, Julien Tranchida, Christian R. Trott, and Steven J. Plimpton · 2021
Closest in time.
Kohn-sham equations as regularizer: building prior knowledge into machine-learned physics
Laurent Li, Stephan Hoyer, Ryan Pederson, Ruoxi Sun, Ekin Dogus Cubuk, Patrick F. Riley, and Kieron Burke · 2021
Closest in time.
A practical method for constructing equivariant multilayer perceptrons for arbitrary matrix groups
Marc Finzi, Max Welling, and Andrew Gordon Wilson · 2021
Closest in time.
Brax - a differentiable physics engine for large scale rigid body simulation
C. Daniel Freeman, Erik Frey, Anton Raichuk, Sertan Girgin, Igor Mordatch, and Olivier Bachem · 2021
Closest in time.
Modelling and measuring open-endedness
Susan Stepney · 2021
Closest in time.
Towards Teachable Autonomous Agents
Olivier Sigaud, Hugo Caselles-Dupré, Cédric Colas, Ahmed Akakzia, Pierre-Yves Oudeyer, and Mohamed Chetouani · 2021
Closest in time.
Open-Ended learning leads to Generally Capable Agents
Max Jaderberg, Michael Mathieu, Nat McAleese, Nathalie Bradley-Schmieg, Nathaniel Wong, Nicolas Porcel, Steph Hughes-Fitt, Valentin Dalibard, and Wojciech Marian Czarnecki · 2021
Closest in time.
Hopfield networks is all you need
Hubert Ramsauer, Bernhard Schafl, Johannes Lehner, Philipp Seidl, Michael Widrich, Lukas Gruber, Markus Holzleitner, Milena Pavlovi’c, Geir Kjetil Sandve, Victor Greiff, David P. Kreil, Michael Kopp, Günter Klambauer, Johannes Brandstetter, and Sepp Hochreiter · 2021
Closest in time.
Questions for the Open-Ended Evolution Community: Reflections from the 2021 Cross Labs Innovation Science Workshop
Kevin Frans, L B Soros, and Olaf Witkowski · 2021
Closest in time.
A Survey on Semantic Parsing for Machine Programming
Celine Lee, Justin Gottschlich, and Dan Roth · 2021
Closest in time.
Controlflag: A self-supervised idiosyncratic pattern detection system for software control structures
Niranjan Hasabnis and Justin Gottschlich · 2021
Closest in time.
Program synthesis for scientific computing
Nuno Lopes, Farhana Aleen, Abid Muslim Malik, Frank Alexander, Saman P. Amarasinghe, Pavan Balaji, M. Naik, Bill Carlson, Boyana Norris, Barbara Chapman, Swarat Chaudhuri, Madhusudan Parthasarathy, and Krishnan Raghavan · 2021
Closest in time.
The hardware lottery
Sara Hooker · 2021
Closest in time.
Intermediate layer optimization for inverse problems using deep generative models
Giannis Daras, Joseph Dean, Ajil Jalal, and Alexandros G Dimakis · 2021
Closest in time.
Trumpets: Injective flows for inference and inverse problems
Konik Kothari, AmirEhsan Khorashadizadeh, V Maarten, and Ivan Dokmanic · 2021
Closest in time.
Neural-adjoint method for the inverse design of all-dielectric metasurfaces
Yang Deng, Simiao Ren, Kebin Fan, Jordan M Malof, and Willie J Padilla · 2021
Closest in time.
Deep learning the electromagnetic properties of metamaterials—a comprehensive review
Omar Khatib, Simiao Ren, Jordan Malof, and Willie J Padilla · 2021
Closest in time.
Benchmarking invertible architectures on inverse problems
Jakob Kruse, Lynton Ardizzone, Carsten Rother, and Ullrich Köthe · 2021
Closest in time.
Modeling Agents with Probabilistic Programs
Owain Evans, Andreas Stuhlmüller, John Salvatier, and Daniel Filan · 2021
Closest in time.
Causal navigation by continuous-time neural networks
Charles Vorbach, Ramin M. Hasani, Alexander Amini, Mathias Lechner, and Daniela Rus · 2021
Closest in time.
Liquid time-constant networks
Ramin M. Hasani, Mathias Lechner, Alexander Amini, Daniela Rus, and Radu Grosu · 2021
Closest in time.
Machine learning and computation-enabled intelligent sensor design
Z. Ballard, C. Brown, A.M. Madni, and et al · 2021
Closest in time.
A graph placement methodology for fast chip design
Azalia Mirhoseini, Anna Goldie, Mustafa Yazgan, Joe Wenjie Jiang, Ebrahim M. Songhori, Shen Wang, Young-Joon Lee, Eric Johnson, Omkar Pathak, Azade Nazi, Jiwoo Pak, Andy Tong, Kavya Srinivasa, Will Hang, Emre Tuncer, Quoc V. Le, James Laudon, Richard Ho, Roger Carpenter, and Jeff Dean · 2021
Closest in time.
Multivariate deep evidential regression
Nis Meinert and Alexander Lavin · 2021
Closest in time.
Data readiness report
Shazia Afzal, C Rajmohan, Manish Kesarwani, Sameep Mehta, and Hima Patel · 2021
Closest in time.
Towards accountability for machine learning datasets: Practices from software engineering and infrastructure
Ben Hutchinson, Andrew Smart, A. Hanna, Emily L. Denton, Christina Greer, Oddur Kjartansson, Parker Barnes, and Margaret Mitchell · 2021
Closest in time.
PETSc/TAO users manual
Satish Balay, Shrirang Abhyankar, Mark F. Adams, Steven Benson, Jed Brown, Peter Brune, Kris Buschelman, Emil Constantinescu, Lisandro Dalcin, Alp Dener, Victor Eijkhout, William D. Gropp, Václav Hapla, Tobin Isaac, Pierre Jolivet, Dmitry Karpeev, Dinesh Kaushik, Matthew G. Knepley, Fande Kong, Scott Kruger, Dave A. May, Lois Curfman McInnes, Richard Tran Mills, Lawrence Mitchell, Todd Munson, Jose E. Roman, Karl Rupp, Patrick Sanan, Jason Sarich, Barry F. Smith, Stefano Zampini, Hong Zhang, Hong Zhang, and Junchao Zhang · 2021
Closest in time.
Stream-ai-md: Streaming ai-driven adaptive molecular simulations for heterogeneous computing platforms
Alexander Brace, Michael Salim, Vishal Subbiah, Heng Ma, Murali Emani, Anda Trifa, Austin R. Clyde, Corey Adams, Thomas Uram, Hyunseung Yoo, Andew Hock, Jessica Liu, Venkatram Vishwanath, and Arvind Ramanathan · 2021
Closest in time.
A data quality-driven view of mlops
Cédric Renggli, Luka Rimanic, Nezihe Merve Gurel, Bojan Karlavs, Wentao Wu, and Ce Zhang · 2021
Closest in time.
Probabilistic simulation of quantum circuits using a deep-learning architecture
Juan Carrasquilla, Di Luo, Felipe Pérez, Ashley Milsted, Bryan K Clark, Maksims Volkovs, and Leandro Aolita · 2021
Closest in time.
Towards the simulation of large scale protein-ligand interactions on nisq-era quantum computers
Fionn D Malone, Robert M Parrish, Alicia R Welden, Thomas Fox, Matthias Degroote, Elica Kyoseva, Nikolaj Moll, Raffaele Santagati, and Michael Streif · 2021
Closest in time.
Physical deep learning based on optimal control of dynamical systems
Genki Furuhata, Tomoaki Niiyama, and Satoshi Sunada · 2021
Closest in time.
Category theory in machine learning
Dan Shiebler, Bruno Gavranovi’c, and Paul Wilson · 2021
Closest in time.
Relating graph neural networks to structural causal models
M. Zecevic, Devendra Singh Dhami, Petar Velickovic, and Kristian Kersting · 2021
Closest in time.
Geometric deep learning and equivariant neural networks
Jan E. Gerken, Jimmy Aronsson, Oscar Carlsson, Hampus Linander, Fredrik Ohlsson, Christoffer Petersson, and Daniel Persson · 2021
Closest in time.
Learning mesh-based simulation with graph networks
Tobias Pfaff, Meire Fortunato, Alvaro Sanchez-Gonzalez, and Peter W. Battaglia · 2021
Closest in time.
Fake it till you make it: Face analysis in the wild using synthetic data alone, 2021
Erroll Wood, Tadas Baltrušaitis, Charlie Hewitt, Sebastian Dziadzio, Matthew Johnson, Virginia Estellers, Thomas J. Cashman, and Jamie Shotton · 2021
Closest in time.
Isay Katsman, Aaron Lou, D. Lim, Qingxuan Jiang, Ser-Nam Lim, and Christopher De Sa · 2021
Closest in time.
Sampling using su(n) gauge equivariant flows
Denis Boyda, Gurtej Kanwar, Sébastien Racanière, Danilo Jimenez Rezende, M. S. Albergo, Kyle Cranmer, Daniel C. Hackett, and Phiala Shanahan · 2021
Closest in time.
Noisy intermediate-scale quantum (nisq) algorithms
Kishor Bharti, Alba Cervera-Lierta, Thi Ha Kyaw, Tobias Haug, Sumner Alperin-Lea, Abhinav Anand, Matthias Degroote, Hermanni Heimonen, Jakob S Kottmann, Tim Menke, et al · 2021
Closest in time.
Variational quantum algorithms
Marco Cerezo, Andrew Arrasmith, Ryan Babbush, Simon C Benjamin, Suguru Endo, Keisuke Fujii, Jarrod R McClean, Kosuke Mitarai, Xiao Yuan, Lukasz Cincio, et al · 2021
Closest in time.
Uncertainty-aware image reconstruction with robust generative flows
Jiaxin Zhang, Victor Fung, and Sirui Bi · 2022
Closest in time.