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Neurosymbolic Programming (NP) techniques have the potential to accelerate scientific discovery.
Quantifying influence of human choice on the automated detection of Drosophila behavior by a supervised machine learning algorithm
Xubo Leng, Margot Wohl, Kenichi Ishii, Pavan Nayak, and Kenta Asahina · 1932
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Learning a theory of causality
Noah D. Goodman, Tomer D. Ullman, and Joshua B. Tenenbaum · 1939
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Programming by demonstration: An inductive learning formulation
Tessa A Lau and Daniel S Weld · 1998
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Smooth interpretation
Swarat Chaudhuri and Armando Solar-Lezama · 2010
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Automating String Processing in Spreadsheets using Input-Output Examples
Sumit Gulwani · 2011
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Synthesis of biological models from mutation experiments
Ali Sinan Koksal, Yewen Pu, Saurabh Srivastava, Rastislav Bodik, Jasmin Fisher, and Nir Piterman · 2013
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Toward a science of computational ethology
David J Anderson and Pietro Perona · 2014
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Detecting Social Actions of Fruit Flies
Eyrun Eyjolfsdottir, Steve Branson, Xavier P. Burgos-Artizzu, Eric D. Hoopfer, Jonathan Schor, David J. Anderson, and Pietro Perona · 2014
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RobustFill: Neural Program Learning under Noisy I/O
Jacob Devlin, Jonathan Uesato, Surya Bhupatiraju, Rishabh Singh, Abdel-rahman Mohamed, and Pushmeet Kohli · 2017
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Towards a rigorous science of interpretable machine learning
Finale Doshi-Velez and Been Kim · 2017
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Optimized risk scores
Berk Ustun and Cynthia Rudin · 2017
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Human decisions and machine predictions
Jon Kleinberg, Himabindu Lakkaraju, Jure Leskovec, Jens Ludwig, and Sendhil Mullainathan · 2018
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Deeplabcut: markerless pose estimation of user-defined body parts with deep learning
Alexander Mathis, Pranav Mamidanna, Kevin M. Cury, Taiga Abe, Venkatesh N. Murthy, Mackenzie W. Mathis, and Matthias Bethge · 2018
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Houdini: Lifelong learning as program synthesis
Lazar Valkov, Dipak Chaudhari, Akash Srivastava, Charles Sutton, and Swarat Chaudhuri · 2018
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Programmatically interpretable reinforcement learning
Abhinav Verma, Vijayaraghavan Murali, Rishabh Singh, Pushmeet Kohli, and Swarat Chaudhuri · 2018
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Computational neuroethology: a call to action
Sandeep Robert Datta, David J Anderson, Kristin Branson, Pietro Perona, and Andrew Leifer · 2019
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Neural architecture search: A survey
Thomas Elsken, Jan Hendrik Metzen, and Frank Hutter · 2019
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Cynthia Rudin · 2019
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Pysr: Fast & parallelized symbolic regression in python/julia, 2020
Miles Cranmer · 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
Acquisition of chess knowledge in alphazero
Thomas McGrath, Andrei Kapishnikov, Nenad Tomašev, Adam Pearce, Demis Hassabis, Been Kim, Ulrich Paquet, and Vladimir Kramnik · 2021
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The mouse action recognition system (mars) software pipeline for automated analysis of social behaviors in mice
Cristina Segalin, Jalani Williams, Tomomi Karigo, May Hui, Moriel Zelikowsky, Jennifer J Sun, Pietro Perona, David J Anderson, and Ann Kennedy · 2021
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Task programming: Learning data efficient behavior representations
Jennifer J Sun, Ann Kennedy, Eric Zhan, David J Anderson, Yisong Yue, and Pietro Perona · 2021
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Interpreting expert annotation differences in animal behavior
Megan Tjandrasuwita, Jennifer J Sun, Ann Kennedy, Swarat Chaudhuri, and Yisong Yue · 2021
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Leveraging language to learn program abstractions and search heuristics
Catherine Wong, Kevin M Ellis, Joshua Tenenbaum, and Jacob Andreas · 2021
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Quantifying behavior to understand the brain
Talmo D Pereira, Joshua W Shaevitz, and Mala Murthy · 2020
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Learning Differentiable Programs with Admissible Neural Heuristics
Ameesh Shah, Eric Zhan, Jennifer Sun, Abhinav Verma, Yisong Yue, and Swarat Chaudhuri · 2020
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Neurosymbolic programming
Swarat Chaudhuri, Kevin Ellis, Oleksandr Polozov, Rishabh Singh, Armando Solar-Lezama, Yisong Yue, et al · 2021
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Differentiable synthesis of program architectures
Guofeng Cui and He Zhu · 2021
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DreamCoder: bootstrapping inductive program synthesis with wake-sleep library learning
Kevin Ellis, Catherine Wong, Maxwell Nye, Mathias Sablé-Meyer, Lucas Morales, Luke Hewitt, Luc Cary, Armando Solar-Lezama, and Joshua B. Tenenbaum · 2021
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Data-efficient graph grammar learning for molecular generation
Minghao Guo, Veronika Thost, Beichen Li, Payel Das, Jie Chen, and Wojciech Matusik · 2021
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Unsupervised learning of neurosymbolic encoders
Eric Zhan, Jennifer J Sun, Ann Kennedy, Yisong Yue, and Swarat Chaudhuri · 2021
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Synthesizing theories of human language with bayesian program induction
Kevin Ellis, Adam Albright, Armando Solar-Lezama, Joshua B Tenenbaum, and Timothy J O’Donnell · 2022
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Toward the explainability, transparency, and universality of machine learning for behavioral classification in neuroscience
Nastacia L Goodwin, Simon RO Nilsson, Jia Jie Choong, and Sam A Golden · 2022
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Sleap: A deep learning system for multi-animal pose tracking
Talmo D Pereira, Nathaniel Tabris, Arie Matsliah, David M Turner, Junyu Li, Shruthi Ravindranath, Eleni S Papadoyannis, Edna Normand, David S Deutsch, Z. Yan Wang, Grace C McKenzie-Smith, Catalin C Mitelut, Marielisa Diez Castro, John D’Uva, Mikhail Kislin, Dan H Sanes, Sarah D Kocher, Samuel S-H, Annegret L Falkner, Joshua W Shaevitz, and Mala Murthy · 2022
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Current progress and open challenges for applying deep learning across the biosciences
Nicolae Sapoval, Amirali Aghazadeh, Michael G Nute, Dinler A Antunes, Advait Balaji, Richard Baraniuk, CJ Barberan, Ruth Dannenfelser, Chen Dun, Mohammadamin Edrisi, et al · 2022
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Automatic synthesis of diverse weak supervision sources for behavior analysis
Albert Tseng, Jennifer J Sun, and Yisong Yue · 2022
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Top-down synthesis for library learning
Matthew Bowers, Theo X. Olausson, Catherine Wong, Gabriel Grand, Joshua B. Tenenbaum, Kevin Ellis, and Armando Solar-Lezama · 2023
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Probabilistic Machine Learning: Advanced Topics
Kevin P. Murphy · 2023
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