Workshop
Advances in Programming Languages and Neurosymbolic Systems (AIPLANS)
Breandan Considine 路 Disha Shrivastava 路 David Yu-Tung Hui 路 Chin-Wei Huang 路 Shawn Tan 路 Xujie Si 路 Prakash Panangaden 路 Guy Van den Broeck 路 Daniel Tarlow
Tue 14 Dec, 3:45 a.m. PST
Neural information processing systems have benefited tremendously from the availability of programming languages and frameworks for automatic differentiation (AD). Not only do NeurIPS benefit from programming languages for automatic inference but can also be considered as a language in their own right, consisting of differentiable and stochastic primitives. Combined with neural language models, these systems are increasingly capable of generating symbolic programs a human programmer might write in a high-level language. Developing neurosymbolic systems for automatic program synthesis requires insights from both statistical learning and programming languages.
AIPLANS invites all researchers working towards the same purpose in these two communities to build on common ground. Our workshop is designed to be as inclusive as possible towards researchers engaged in building programming languages and neurosymbolic systems.
Schedule
Tue 3:45 a.m. - 4:00 a.m.
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Introductory remarks
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Introductory Remarks
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SlidesLive Video |
Breandan Considine 馃敆 |
Tue 4:00 a.m. - 4:45 a.m.
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Thinking like Transformers - Gail Weiss - Technion - Israel Institute of Technology
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Invited Talk
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SlidesLive Video |
Gail Weiss 馃敆 |
Tue 4:45 a.m. - 4:55 a.m.
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Q&A - Gail Weiss
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Post Talk Q & A
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馃敆 |
Tue 5:00 a.m. - 6:00 a.m.
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When G枚del discovered Automatic Differentiation - Marie Kerjean - Centre national de la recherche scientifique
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Invited Talk
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SlidesLive Video |
AIPLANS 2021 馃敆 |
Tue 6:00 a.m. - 7:00 a.m.
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Building machines that learn and think like people by learning to write programs: progress, open problems, and next steps - Josh Tenenbaum - Massachusetts Institute of Technology
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Invited Talk
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SlidesLive Video |
Josh Tenenbaum 馃敆 |
Tue 7:00 a.m. - 7:15 a.m.
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Short break
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Tue 7:15 a.m. - 8:15 a.m.
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Panel Discussion
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Panel Discussion
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SlidesLive Video |
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Tue 8:15 a.m. - 8:55 a.m.
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Daniel Selsam Microsoft Research
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Tutorial
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SlidesLive Video |
Daniel Selsam 馃敆 |
Tue 8:55 a.m. - 9:05 a.m.
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Q&A - Daniel Selsam
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Post Talk Q & A
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馃敆 |
Tue 9:00 a.m. - 10:15 a.m.
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Lunch / Poster Session ( Poster Session ) > link | 馃敆 |
Tue 10:15 a.m. - 10:20 a.m.
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Remarks from Organisers
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Introduction
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Tue 10:20 a.m. - 10:46 a.m.
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Randomized Automatic Differentiation - Ryan Adams - Princeton University
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Invited Talk
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SlidesLive Video |
Ryan Adams 馃敆 |
Tue 10:46 a.m. - 10:56 a.m.
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Q&A - Ryan Adams
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Post Talk Q & A
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Tue 11:00 a.m. - 11:45 a.m.
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Dependent Types for Machine Learning in Dex - David Duvenaud - University of Toronto
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Invited Talk
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SlidesLive Video |
David Duvenaud 路 AIPLANS 2021 馃敆 |
Tue 11:45 a.m. - 11:55 a.m.
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Q&A - David Duvenaud
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Post Talk Q & A
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馃敆 |
Tue 12:00 p.m. - 12:45 p.m.
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Differential Inference: A Criminally Underused Tool. - Alexander Rush - Cornell University
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Invited Talk
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SlidesLive Video |
Alexander Rush 馃敆 |
Tue 12:45 p.m. - 12:55 p.m.
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Q&A - Alexander Rush
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Post Talk Q & A
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Tue 1:00 p.m. - 1:05 p.m.
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Introduction to Spotlight Speakers
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Organiser Remarks
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Tue 1:05 p.m. - 1:15 p.m.
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Meta-Learning an Inference Algorithm for Probabilistic Programs - Gwonsoo Che
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Spotlight Talks
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SlidesLive Video |
AIPLANS 2021 路 Gwonsoo Che 馃敆 |
Tue 1:15 p.m. - 1:22 p.m.
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LazyPPL: laziness and types in non-parametric probabilistic programs - Hugo Paquet
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Spotlight Talk
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SlidesLive Video |
AIPLANS 2021 路 Hugo Paquet 馃敆 |
Tue 1:22 p.m. - 1:32 p.m.
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Learning Rules with Stratified Negation in Differentiable ILP - Giri Krishnan
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Spotlight Talks
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SlidesLive Video |
AIPLANS 2021 路 Giri Krishnan 馃敆 |
Tue 1:32 p.m. - 1:41 p.m.
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Learning Adaptive Control Flow in Transformers for Improved Systematic Generalization - R贸bert Csord谩s
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Spotlight Talk
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SlidesLive Video |
AIPLANS 2021 路 R贸bert Csord谩s 馃敆 |
Tue 1:41 p.m. - 1:51 p.m.
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Type Inference as Optimization - Eirene V. Pandi
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Spotlight Talk
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SlidesLive Video |
AIPLANS 2021 路 Eirini V. Pandi 馃敆 |
Tue 1:51 p.m. - 2:00 p.m.
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Q&A for Spotlight Authors
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Q & A
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馃敆 |
Tue 2:00 p.m. - 2:15 p.m.
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Closing Remarks
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Closing remarks
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SlidesLive Video |
Breandan Considine 馃敆 |
Tue 2:15 p.m. - 3:00 p.m.
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Poster Session ( Poster Session ) > link | AIPLANS 2021 馃敆 |
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Type Inference as Optimization ( Poster ) > link | Eirini V. Pandi 路 Earl Barr 路 Andrew Gordon 路 Charles Sutton 馃敆 |
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Are Transformers All That Karel Needs? ( Poster ) > link | Abhay Garg 路 Anand Sriraman 路 Shirish Karande 馃敆 |
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Towards Neural Functional Program Evaluation ( Poster ) > link | Torsten Scholak 路 Jonathan Pilault 馃敆 |
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Staged compilation of tensor expressions ( Poster ) > link | Marco Zocca 馃敆 |
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Safe Neurosymbolic Learning with Differentiable Symbolic Execution ( Poster ) > link | Chenxi Yang 路 Swarat Chaudhuri 馃敆 |
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AutoCoder: Leveraging Transformers for Automatic Code Synthesis ( Poster ) > link | Mrinal Anand 路 Mayank Singh 馃敆 |
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Learning Rules with Stratified Negation in Differentiable ILP. ( Poster ) > link | Giri Krishnan 路 Ramyaa Ramyaa 馃敆 |
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AutumnSynth: Synthesis of Reactive Programs with Structured Latent State ( Poster ) > link | Ria Das 路 Zenna Tavares 路 Josh Tenenbaum 路 Armando Solar-Lezama 馃敆 |
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PAC Synthesis of Machine Learning Programs ( Poster ) > link | Osbert Bastani 馃敆 |
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Learning compositional programs with arguments and sampling ( Poster ) > link | Giovanni De Toni 路 Andrea Passerini 馃敆 |
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Learning Adaptive Control Flow in Transformers for Improved Systematic Generalization ( Poster ) > link | R贸bert Csord谩s 路 Kazuki Irie 路 J眉rgen Schmidhuber 馃敆 |
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Adversarial Robustness of Program Synthesis Models ( Poster ) > link | Mrinal Anand 路 Mayank Singh 馃敆 |
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Learning C to x86 Translation: An Experiment in Neural Compilation ( Poster ) > link | Jordi Armengol-Estap茅 路 Michael O'Boyle 馃敆 |
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Synthesizing Video Trajectory Queries ( Poster ) > link | Stephen Mell 路 Favyen Bastani 路 Stephan Zdancewic 路 Osbert Bastani 馃敆 |
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Augmenting Classic Algorithms with Neural Components for Strong Generalisation on Ambiguous and High-Dimensional Data ( Poster ) > link | Imanol Schlag 路 J眉rgen Schmidhuber 馃敆 |
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Meta-Learning an Inference Algorithm for Probabilistic Programs ( Poster ) > link | Gwonsoo Che 路 Hongseok Yang 馃敆 |
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Scallop: From Probabilistic Deductive Databases to Scalable Differentiable Reasoning ( Poster ) > link | Jiani Huang 路 Ziyang Li 路 Binghong Chen 路 Karan Samel 路 Mayur Naik 路 Le Song 路 Xujie Si 馃敆 |
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LazyPPL: laziness and types in non-parametric probabilistic programs ( Poster ) > link | Hugo Paquet 路 Sam Staton 馃敆 |
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Proof Extraction for Logical Neural Networks ( Poster ) > link | Thabang Lebese 路 Ndivhuwo Makondo 路 Cristina Cornelio 路 Naweed A Khan 馃敆 |
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A Genetic Programming Approach To Zero-Shot Neural Architecture Ranking ( Poster ) > link | Yash Akhauri 路 Juan Munoz 路 Ravishankar Iyer 路 Nilesh Jain 馃敆 |