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We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery.
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Iaroslav Ispolatov, Vaibhav Madhok, Sebastian Allende, and Michael Doebeli · 2015
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Think locally, act locally: Detection of small, medium-sized, and large communities in large networks
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Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science
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Pattern recognition and machine learning
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Discovering governing equations from data by sparse identification of nonlinear dynamical systems
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RandNLA: Randomized numerical linear algebra
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Deep learning in drug discovery
Erik Gawehn, Jan A. Hiss, and Gisbert Schneider · 2016
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Deep Learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
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Mapping the similarities of spectra: Global and locally-biased approaches to SDSS galaxy data
D. Lawlor, T. Budavari, and M. W. Mahoney · 2016
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The (Un)reliability of Saliency Methods
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Definitions, methods, and applications in interpretable machine learning
W. James Murdoch, Chandan Singh, Karl Kumbier, Reza Abbasi-Asl, and Bin Yu · 2019
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The seven tools of causal inference, with reflections on machine learning
Judea Pearl · 2019
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Visualizing probabilistic models and data with intensive principal component analysis
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Decoupling feature extraction from policy learning: assessing benefits of state representation learning in goal based robotics, 2019
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Learning from Tay’s introduction
Peter Lee · 2016
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” why should i trust you?” explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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Microsoft takes chatbot offline after it starts tweeting racist messages
Justin Worland · 2016
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Interpreting Convolutional Neural Networks Through Compression
Reza Abbasi-Asl and Bin Yu · 2017
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Union of intersections (uoi) for interpretable data driven discovery and prediction
Kristofer Bouchard, Alejandro Bujan, Fred Roosta, Shashanka Ubaru, Mr Prabhat, Antoine Snijders, Jian-Hua Mao, Edward Chang, Michael W Mahoney, and Sharmodeep Bhattacharya · 2017
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The p4 health spectrum–a predictive, preventive, personalized and participatory continuum for promoting healthspan
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Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
Maziar Raissi, Paris Perdikaris, and George E Karniadakis · 2019
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Deep learning and process understanding for data-driven Earth system science
M. Reichstein, G. Camps-Valls, B. Stevens, M. Jung, J. Denzler, N. Carvalhais, and Prabhat · 2019
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
C. Rudin · 2019
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The atmospheric river tracking method intercomparison project (artmip): quantifying uncertainties in atmospheric river climatology
Jonathan J Rutz, Christine A Shields, Juan M Lora, Ashley E Payne, Bin Guan, Paul Ullrich, Travis O’Brien, L Ruby Leung, F Martin Ralph, Michael Wehner, et al · 2019
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Explainable AI: interpreting, explaining and visualizing deep learning
Wojciech Samek, Grégoire Montavon, Andrea Vedaldi, Lars Kai Hansen, and Klaus-Robert Müller · 2019
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Unsupervised word embeddings capture latent knowledge from materials science literature
Vahe Tshitoyan, John Dagdelen, Leigh Weston, Alexander Dunn, Ziqin Rong, Olga Kononova, Kristin A Persson, Gerbrand Ceder, and Anubhav Jain · 2019
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Crowdsourcing perceptions of fair predictors for machine learning: a recidivism case study
Niels Van Berkel, Jorge Goncalves, Danula Hettiachchi, Senuri Wijenayake, Ryan M Kelly, and Vassilis Kostakos · 2019
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High-dimensional statistics: A non-asymptotic viewpoint
Martin J Wainwright · 2019
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The Scientific Method in the Science of Machine Learning
Jessica Zosa Forde and Michela Paganini · 2019
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Learning epistatic polygenic phenotypes with boolean interactions
Merle Behr, Karl Kumbier, Aldo Cordova-Palomera, Matthew Aguirre, Euan Ashley, Atul Butte, Rima Arnaout, James B Brown, James Preist, and Bin Yu · 2020
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‘It will change everything’: DeepMind’s AI makes gigantic leap in solving protein structures
Ewen Callaway · 2020
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Discovering Symbolic Models from Deep Learning with Inductive Biases
Miles Cranmer, Alvaro Sanchez-Gonzalez, Peter Battaglia, Rui Xu, Kyle Cranmer, David Spergel, and Shirley Ho · 2020
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Improved guarantees and a multiple-descent curve for Column Subset Selection and the Nystrom method
M. Derezinski, R. Khanna, and M. W. Mahoney · 2020
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Explaining Explanations: Axiomatic Feature Interactions for Deep Networks
Joseph D. Janizek, Pascal Sturmfels, and Su-In Lee · 2020
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Persistent homology advances interpretable machine learning for nanoporous materials
Aditi S. Krishnapriyan, Joseph Montoya, Jens Hummelshøj, and Dmitriy Morozov · 2020
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Zhenyu Liao, Romain Couillet, and Michael W. Mahoney · 2020
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AI Poincare: Machine learning conservation laws from trajectories
Ziming Liu and Max Tegmark · 2020
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Self-driving laboratory for accelerated discovery of thin-film materials
Benjamin P MacLeod, Fraser GL Parlane, Thomas D Morrissey, Florian Häse, Loïc M Roch, Kevan E Dettelbach, Raphaell Moreira, Lars PE Yunker, Michael B Rooney, Joseph R Deeth, et al · 2020
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Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies
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Artificial intelligence: Who is responsible for the diagnosis?, 2020
Emanuele Neri, Francesca Coppola, Vittorio Miele, Corrado Bibbolino, and Roberto Grassi · 2020
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A machine learning automated recommendation tool for synthetic biology
Tijana Radivojević, Zak Costello, Kenneth Workman, and Hector Garcia Martin · 2020
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On the interpretability of artificial intelligence in radiology: Challenges and opportunities
Mauricio Reyes, Raphael Meier, Sérgio Pereira, Carlos A Silva, Fried-Michael Dahlweid, Hendrik von Tengg-Kobligk, Ronald M Summers, and Roland Wiest · 2020
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Ai for science
Rick Stevens, Valerie Taylor, Jeff Nichols, Arthur Barney Maccabe, Katherine Yelick, and David Brown · 2020
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Decoupling Representation Learning from Reinforcement Learning
Adam Stooke, Kimin Lee, Pieter Abbeel, and Michael Laskin · 2020
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Can exascale computing and explainable artificial intelligence applied to plant biology deliver on the united nations sustainable development goals?
Jared Streich, Jonathon Romero, João Gabriel Felipe Machado Gazolla, David Kainer, Ashley Cliff, Erica Teixeira Prates, James B Brown, Sacha Khoury, Gerald A Tuskan, Michael Garvin, et al · 2020
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AI Feynman: A physics-inspired method for symbolic regression
Silviu-Marian Udrescu and Max Tegmark · 2020
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Adversarially-Trained Deep Nets Transfer Better
Francisco Utrera, Evan Kravitz, N. Benjamin Erichson, Rajiv Khanna, and Michael W. Mahoney · 2020
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Veridical data science
Bin Yu and Karl Kumbier · 2020
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Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism
Jie Zhang, Søren D Petersen, Tijana Radivojevic, Andrés Ramirez, Andrés Pérez-Manríquez, Eduardo Abeliuk, Benjamín J Sánchez, Zak Costello, Yu Chen, Michael J Fero, et al · 2020
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Distribution-free, risk-controlling prediction sets
Stephen Bates, Anastasios Angelopoulos, Lihua Lei, Jitendra Malik, and Michael I Jordan · 2021
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Highly accurate protein structure prediction with AlphaFold
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Characterizing possible failure modes in physics-informed neural networks
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Implicit self-regularization in deep neural networks: Evidence from random matrix theory and implications for learning
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Predicting trends in the quality of state-of-the-art neural networks without access to training or testing data
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