Fetching the paper…
Reading the bibliography…
Probabilistic programming languages (PPLs) are receiving widespread attention for performing Bayesian inference in complex generative models.
Partial-symmetries of weak interactions
Sheldon L Glashow. 1961 · 1961
Earlier work this paper cites.
Phys. Rev. Lett 19 (1967), 1264
S Weinberg. 1967 · 1967
Earlier work this paper cites.
Proceedings of the Eighth Nobel Symposium on Elementary Particle Theory, Relativistic Groups, and Analyticity, Stockholm, Sweden, 1968
A Salam. 1968 · 1968
Earlier work this paper cites.
Regularization and renormalization of gauge fields
Martinus Veltman et al · 1972
Earlier work this paper cites.
Dynamical likelihood method for reconstruction of events with missing momentum. I. Method and toy models
Kunitaka Kondo. 1988 · 1988
Earlier work this paper cites.
Probabilistic inference using Markov chain Monte Carlo methods
Radford M Neal. 1993 · 1993
Earlier work this paper cites.
Mixture density networks
Christopher M Bishop. 1994 · 1994
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. 1998 · 1998
Earlier work this paper cites.
A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking
M Sanjeev Arulampalam, Simon Maskell, Neil Gordon, and Tim Clapp. 2002 · 2002
Earlier work this paper cites.
GEANT4—a simulation toolkit
Sea Agostinelli, John Allison, K al Amako, J Apostolakis, H Araujo, P Arce, M Asai, D Axen, S Banerjee, G Barrand, et al · 2003
Earlier work this paper cites.
Pattern Recognition and Machine Learning
Christopher M Bishop. 2006 · 2006
Earlier work this paper cites.
TrueSkill™: a Bayesian skill rating system. In
Ralf Herbrich, Tom Minka, and Thore Graepel. 2007 · 2007
Earlier work this paper cites.
The ATLAS Experiment at the CERN Large Hadron Collider
G. Aad et al · 2008
Earlier work this paper cites.
Introduction to elementary particles
David Griffiths. 2008 · 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, and et al. 2008 · 2008
Earlier work this paper cites.
Curriculum learning. In
Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston. 2009 · 2009
Earlier work this paper cites.
A tutorial on particle filtering and smoothing: Fifteen years later
Arnaud Doucet and Adam M Johansen. 2009 · 2009
Earlier work this paper cites.
Event generation with SHERPA 1.1
Tanju Gleisberg, Stefan Höche, F Krauss, M Schönherr, S Schumann, F Siegert, and J Winter. 2009 · 2009
Earlier work this paper cites.
MCMC using Hamiltonian dynamics
Radford M. Neal. 2011 · 2011
Earlier work this paper cites.
HOGWILD!: A Lock-free Approach to Parallelizing Stochastic Gradient Descent. In
Feng Niu, Benjamin Recht, Christopher Re, and Stephen J. Wright. 2011 · 2011
Earlier work this paper cites.
Lightweight implementations of probabilistic programming languages via transformational compilation. In
David Wingate, Andreas Stuhlmüller, and Noah Goodman. 2011 · 2011
Earlier work this paper cites.
Large Scale Distributed Deep Networks. In
Jeffrey Dean, Greg S. Corrado, Rajat Monga, Kai Chen, Matthieu Devin, Quoc V. Le, Mark Z. Mao, Marc’Aurelio Ranzato, Andrew Senior, Paul Tucker, Ke Yang, and Andrew Y. Ng. 2012 · 2012
Earlier work this paper cites.
Climate interactive: the C-ROADS climate policy model
John Sterman, Thomas Fiddaman, Travis Franck, Andrew Jones, Stephanie McCauley, Philip Rice, Elizabeth Sawin, and Lori Siegel. 2012 · 2012
Earlier work this paper cites.
Bayesian data analysis
Andrew Gelman, Hal S Stern, John B Carlin, David B Dunson, Aki Vehtari, and Donald B Rubin. 2013 · 2013
Earlier work this paper cites.
ZeroMQ: messaging for many applications
Pieter Hintjens. 2013 · 2013
Earlier work this paper cites.
Stochastic variational inference
Matthew D Hoffman, David M Blei, Chong Wang, and John Paisley. 2013 · 2013
Cited alongside, same era.
Auto-encoding variational Bayes
Diederik P Kingma and Max Welling. 2013 · 2013
Cited alongside, same era.
Amortized inference in probabilistic reasoning. In
Samuel Gershman and Noah Goodman. 2014 · 2014
Cited alongside, same era.
The No-U-Turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo
Matthew D Hoffman and Andrew Gelman. 2014 · 2014
Cited alongside, same era.
Adam: A Method for Stochastic Optimization
D. P. Kingma and J. Ba. 2014 · 2014
Cited alongside, same era.
Search for the Standard Model Higgs boson produced in association with top quarks and decaying into bb in pp collisions at sqrt s=8 TeV with the ATLAS detector
CARLA: An Open Urban Driving Simulator. In
Alexey Dosovitskiy, German Ros, Felipe Codevilla, Antonio Lopez, and Vladlen Koltun. 2017 · 2017
Later among the works it cites.
Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour
Priya Goyal, Piotr Dollar, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, and Kaiming He. 2017 · 2017
Later among the works it cites.
Deep learning at 15PF: supervised and semi-supervised classification for scientific data, In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis
Thorsten Kurth, Jian Zhang, Nadathur Satish, Evan Racah, Ioannis Mitliagkas, Md Mostofa Ali Patwary, Tareq Malas, Narayanan Sundaram, Wahid Bhimji, Mikhail Smorkalov, et al · 2017
Later among the works it cites.
Inference Compilation and Universal Probabilistic Programming. In
Tuan Anh Le, Atılım Güneş Baydin, and Frank Wood. 2017 · 2017
Later among the works it cites.
Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators. In
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
G. Aad et al · 2015
Cited alongside, same era.
Approximate Bayesian computation for forward modeling in cosmology
Joël Akeret, Alexandre Refregier, Adam Amara, Sebastian Seehars, and Caspar Hasner. 2015 · 2015
Cited alongside, same era.
Probabilistic machine learning and artificial intelligence
Zoubin Ghahramani. 2015 · 2015
Cited alongside, same era.
FireCaffe: near-linear acceleration of deep neural network training on compute clusters
F. N. Iandola, K. Ashraf, M. W. Moskewicz, and K. Keutzer. 2015 · 2015
Cited alongside, same era.
Inference for higher order probabilistic programs
Tuan Anh Le. 2015 · 2015
Cited alongside, same era.
Reconstruction of hadronic decay products of tau leptons with the ATLAS experiment
G. Aad et al · 2016
Cited alongside, same era.
Tensorflow: A system for large-scale machine learning. In
Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al · 2016
Cited alongside, same era.
Mario Lezcano Casado, Atılım Güneş Baydin, David Martinez Rubio, Tuan Anh Le, Frank Wood, Lukas Heinrich, Gilles Louppe, Kyle Cranmer, Wahid Bhimji, Karen Ng, and Prabhat. 2017 · 2017
Later among the works it cites.
Scaling GRPC Tensorflow on 512 nodes of Cori Supercomputer. In
Amrita Mathuriya, Thorsten Kurth, Vivek Rane, Mustafa Mustafa, Lei Shao, Debbie Bard, Victor W Lee, et al · 2017
Later among the works it cites.
Revisiting Distributed Synchronous SGD
X. Pan, J. Chen, R. Monga, S. Bengio, and R. Jozefowicz. 2017 · 2017
Later among the works it cites.
Automatic differentiation in PyTorch. In
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. 2017 · 2017
Later among the works it cites.
Don’t Decay the Learning Rate, Increase the Batch Size
Samuel L Smith, Pieter-Jan Kindermans, Chris Ying, and Quoc V. Le. 2017 · 2017
Later among the works it cites.
Large batch training of convolutional networks
Yang You, Igor Gitman, and Boris Ginsburg. 2017 · 2017
Later among the works it cites.
Automatic differentiation in machine learning: a survey
Atılım Güneş Baydin, Barak A. Pearlmutter, Alexey Andreyevich Radul, and Jeffrey Mark Siskind. 2018 · 2018
Later among the works it cites.
Pyro: Deep universal probabilistic programming
Eli Bingham, Jonathan P Chen, Martin Jankowiak, Fritz Obermeyer, Neeraj Pradhan, Theofanis Karaletsos, Rohit Singh, Paul Szerlip, Paul Horsfall, and Noah D Goodman. 2018 · 2018
Later among the works it cites.
Mining gold from implicit models to improve likelihood-free inference
Johann Brehmer, Gilles Louppe, Juan Pavez, and Kyle Cranmer. 2018 · 2018
Later among the works it cites.
Fast inference of deep neural networks in FPGAs for particle physics
Javier Duarte et al · 2018
Later among the works it cites.
Layer-Wise Adaptive Rate Control for Training of Deep Networks
B. Ginsburg, I. Gitman, and O. Kuchaiev. 2018 · 2018
Later among the works it cites.
Exascale Deep Learning for Climate Analytics. In
Thorsten Kurth, Sean Treichler, Joshua Romero, Mayur Mudigonda, Nathan Luehr, Everett Phillips, Ankur Mahesh, Michael Matheson, Jack Deslippe, Massimiliano Fatica, Prabhat, and Michael Houston. 2018 · 2018
Later among the works it cites.
CosmoFlow: Using Deep Learning to Learn the Universe at Scale. In
Amrita Mathuriya, Deborah Bard, Peter Mendygral, Lawrence Meadows, James Arnemann, Lei Shao, Siyu He, Tuomas Karna, Daina Moise, Simon J. Pennycook, Kristyn Maschoff, Jason Sewall, Nalini Kumar, Shirley Ho, Mike Ringenburg, Prabhat, and Victor Lee. 2018 · 2018
Later among the works it cites.
An Empirical Model of Large-Batch Training
Sam McCandlish, Jared Kaplan, and et.al Amodei, Dario. 2018 · 2018
Later among the works it cites.
ImageNet/ResNet-50 Training in 224 Seconds
Hiroaki Mikami, Hisahiro Suganuma, Pongsakorn U.-Chupala, Yoshiki Tanaka, and Yuichi Kageyama. 2018 · 2018
Later among the works it cites.
/Infer.NET 0.3
T. Minka, J.M. Winn, J.P. Guiver, Y. Zaykov, D. Fabian, and J. Bronskill. 2018 · 2018
Later among the works it cites.
An introduction to quantum field theory
Michael E Peskin. 2018 · 2018
Later among the works it cites.
Measuring the Effects of Data Parallelism on Neural Network training
Christopher J. Shallue, Jaehoom Lee, Joseph Antognini, Jascha Sohl-Dickstein, Roy Frostig, and George E. Dahl. 2018 · 2018
Later among the works it cites.
Bayesian Distributed Stochastic Gradient Descent. In
Michael Teng and Frank Wood. 2018 · 2018
Later among the works it cites.
An Introduction to Probabilistic Programming
Jan-Willem van de Meent, Brooks Paige, Hongseok Yang, and Frank Wood. 2018 · 2018
Later among the works it cites.
Linux Programmer’s Manual
Linux man-pages project. 2019 · 2019
Closest in time.