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ORCIDAnirudh Goyal
Parth Goyal
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- affiliation: Mila - Quebec AI Institute, Montreal, QC, Canada
- affiliation: University of Montreal, QC, Canada
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2020 – today
- 2024
[j4]Ayush Agrawal, Raghav Prabhakar, Anirudh Goyal, Dianbo Liu:
Physical Reasoning and Object Planning for Household Embodied Agents. Trans. Mach. Learn. Res. 2024 (2024)- [c49]Aniket Rajiv Didolkar, Anirudh Goyal, Yoshua Bengio:
Cycle Consistency Driven Object Discovery. ICLR 2024 - [c48]Cristian Meo, Louis Mahon, Anirudh Goyal, Justin Dauwels:
αTC-VAE: On the relationship between Disentanglement and Diversity. ICLR 2024 - [c47]Dingli Yu, Simran Kaur, Arushi Gupta, Jonah Brown-Cohen, Anirudh Goyal, Sanjeev Arora:
SKILL-MIX: a Flexible and Expandable Family of Evaluations for AI Models. ICLR 2024 - [i78]Haonan Wang, James Zou, Michael Mozer, Anirudh Goyal, Alex Lamb, Linjun Zhang, Weijie J. Su, Zhun Deng, Michael Qizhe Xie, Hannah Brown, Kenji Kawaguchi:
Can AI Be as Creative as Humans? CoRR abs/2401.01623 (2024) - [i77]Kaifeng Lyu, Haoyu Zhao, Xinran Gu, Dingli Yu, Anirudh Goyal, Sanjeev Arora:
Keeping LLMs Aligned After Fine-tuning: The Crucial Role of Prompt Templates. CoRR abs/2402.18540 (2024) - [i76]Yiran Zhao, Wenyue Zheng, Tianle Cai, Xuan Long Do, Kenji Kawaguchi, Anirudh Goyal, Michael Shieh:
Accelerating Greedy Coordinate Gradient via Probe Sampling. CoRR abs/2403.01251 (2024) - [i75]Yuxi Xie, Anirudh Goyal, Wenyue Zheng, Min-Yen Kan, Timothy P. Lillicrap, Kenji Kawaguchi, Michael Shieh:
Monte Carlo Tree Search Boosts Reasoning via Iterative Preference Learning. CoRR abs/2405.00451 (2024) - [i74]Aniket Didolkar, Anirudh Goyal, Nan Rosemary Ke, Siyuan Guo, Michal Valko, Timothy P. Lillicrap, Danilo J. Rezende, Yoshua Bengio, Michael Mozer, Sanjeev Arora:
Metacognitive Capabilities of LLMs: An Exploration in Mathematical Problem Solving. CoRR abs/2405.12205 (2024) - [i73]Siyuan Guo, Aniket Didolkar, Nan Rosemary Ke, Anirudh Goyal, Ferenc Huszár, Bernhard Schölkopf:
Learning Beyond Pattern Matching? Assaying Mathematical Understanding in LLMs. CoRR abs/2405.15485 (2024) - [i72]Cristian Meo, Ksenia Sycheva, Anirudh Goyal, Justin Dauwels:
Bayesian-LoRA: LoRA based Parameter Efficient Fine-Tuning using Optimal Quantization levels and Rank Values trough Differentiable Bayesian Gates. CoRR abs/2406.13046 (2024) - [i71]Vedant Shah, Dingli Yu, Kaifeng Lyu, Simon Park, Nan Rosemary Ke, Michael Mozer, Yoshua Bengio, Sanjeev Arora, Anirudh Goyal:
AI-Assisted Generation of Difficult Math Questions. CoRR abs/2407.21009 (2024) - [i70]Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, Anirudh Goyal, Anthony Hartshorn, Aobo Yang, Archi Mitra, Archie Sravankumar, Artem Korenev, Arthur Hinsvark, Arun Rao, Aston Zhang, Aurélien Rodriguez, Austen Gregerson, Ava Spataru, Baptiste Rozière, Bethany Biron, Binh Tang, Bobbie Chern, Charlotte Caucheteux, Chaya Nayak, Chloe Bi, Chris Marra, Chris McConnell, Christian Keller, Christophe Touret, Chunyang Wu, Corinne Wong, Cristian Canton Ferrer, Cyrus Nikolaidis, Damien Allonsius, Daniel Song, Danielle Pintz, Danny Livshits, David Esiobu, Dhruv Choudhary, Dhruv Mahajan, Diego Garcia-Olano, Diego Perino, Dieuwke Hupkes, Egor Lakomkin, Ehab AlBadawy, Elina Lobanova, Emily Dinan, Eric Michael Smith, Filip Radenovic, Frank Zhang, Gabriel Synnaeve, Gabrielle Lee, Georgia Lewis Anderson, Graeme Nail, Grégoire Mialon, Guan Pang, Guillem Cucurell, Hailey Nguyen, Hannah Korevaar, Hu Xu, Hugo Touvron, Iliyan Zarov, Imanol Arrieta Ibarra, Isabel M. Kloumann, Ishan Misra, Ivan Evtimov, Jade Copet, Jaewon Lee, Jan Geffert, Jana Vranes, Jason Park, Jay Mahadeokar, Jeet Shah, Jelmer van der Linde, Jennifer Billock, Jenny Hong, Jenya Lee, Jeremy Fu, Jianfeng Chi, Jianyu Huang, Jiawen Liu, Jie Wang, Jiecao Yu, Joanna Bitton, Joe Spisak, Jongsoo Park, Joseph Rocca, Joshua Johnstun, Joshua Saxe, Junteng Jia, Kalyan Vasuden Alwala, Kartikeya Upasani, Kate Plawiak, Ke Li, Kenneth Heafield, Kevin Stone, et al.:
The Llama 3 Herd of Models. CoRR abs/2407.21783 (2024) - [i69]Aniket Didolkar, Andrii Zadaianchuk, Anirudh Goyal, Michael C. Mozer, Yoshua Bengio, Georg Martius, Maximilian Seitzer:
Zero-Shot Object-Centric Representation Learning. CoRR abs/2408.09162 (2024) - [i68]Gus Kristiansen, Mark Sandler, Andrey Zhmoginov, Nolan Miller, Anirudh Goyal, Jihwan Lee, Max Vladymyrov:
Narrowing the Focus: Learned Optimizers for Pretrained Models. CoRR abs/2408.09310 (2024) - [i67]Simran Kaur, Simon Park, Anirudh Goyal, Sanjeev Arora:
Instruct-SkillMix: A Powerful Pipeline for LLM Instruction Tuning. CoRR abs/2408.14774 (2024) - [i66]Haoyu Zhao, Simran Kaur, Dingli Yu, Anirudh Goyal, Sanjeev Arora:
Can Models Learn Skill Composition from Examples? CoRR abs/2409.19808 (2024) - 2023
- [j3]Kapil Rana, Aman Pandey, Parth Goyal, Gurinder Singh, Puneet Goyal
:
A novel privacy protection approach with better human imperceptibility. Appl. Intell. 53(19): 21788-21798 (2023) - [j2]Nan Rosemary Ke, Olexa Bilaniuk, Anirudh Goyal, Stefan Bauer, Hugo Larochelle, Bernhard Schölkopf, Michael Curtis Mozer, Christopher Pal, Yoshua Bengio:
Neural Causal Structure Discovery from Interventions. Trans. Mach. Learn. Res. 2023 (2023) - [c46]Riashat Islam, Hongyu Zang, Manan Tomar, Aniket Didolkar, Md Mofijul Islam, Samin Yeasar Arnob, Tariq Iqbal, Xin Li, Anirudh Goyal, Nicolas Heess, Alex Lamb:
Representation Learning in Deep RL via Discrete Information Bottleneck. AISTATS 2023: 8699-8722 - [c45]Nan Rosemary Ke, Silvia Chiappa, Jane X. Wang, Jörg Bornschein, Anirudh Goyal, Mélanie Rey, Theophane Weber, Matthew M. Botvinick, Michael Curtis Mozer, Danilo Jimenez Rezende:
Learning to Induce Causal Structure. ICLR 2023 - [c44]Dianbo Liu, Vedant Shah, Oussama Boussif, Cristian Meo, Anirudh Goyal, Tianmin Shu, Michael Curtis Mozer, Nicolas Heess, Yoshua Bengio:
Stateful Active Facilitator: Coordination and Environmental Heterogeneity in Cooperative Multi-Agent Reinforcement Learning. ICLR 2023 - [c43]Dianbo Liu, Moksh Jain, Bonaventure F. P. Dossou, Qianli Shen, Salem Lahlou, Anirudh Goyal, Nikolay Malkin, Chris Chinenye Emezue, Dinghuai Zhang, Nadhir Hassen, Xu Ji, Kenji Kawaguchi, Yoshua Bengio:
GFlowOut: Dropout with Generative Flow Networks. ICML 2023: 21715-21729 - [c42]Mihir Prabhudesai, Anirudh Goyal, Sujoy Paul, Sjoerd van Steenkiste, Mehdi S. M. Sajjadi, Gaurav Aggarwal, Thomas Kipf, Deepak Pathak, Katerina Fragkiadaki:
Test-time Adaptation with Slot-Centric Models. ICML 2023: 28151-28166 - [c41]Frederik Träuble, Anirudh Goyal, Nasim Rahaman, Michael Curtis Mozer, Kenji Kawaguchi, Yoshua Bengio, Bernhard Schölkopf:
Discrete Key-Value Bottleneck. ICML 2023: 34431-34455 - [i65]Sumukh K. Aithal, Anirudh Goyal, Alex Lamb, Yoshua Bengio, Michael Mozer:
Leveraging the Third Dimension in Contrastive Learning. CoRR abs/2301.11790 (2023) - [i64]Nan Rosemary Ke, Sara-Jane Dunn, Jörg Bornschein, Silvia Chiappa, Mélanie Rey, Jean-Baptiste Lespiau, Albin Cassirer, Jane X. Wang, Theophane Weber, David G. T. Barrett, Matthew M. Botvinick, Anirudh Goyal, Michael Mozer, Danilo J. Rezende:
DiscoGen: Learning to Discover Gene Regulatory Networks. CoRR abs/2304.05823 (2023) - [i63]Ayush Chakravarthy, Trang Nguyen, Anirudh Goyal, Yoshua Bengio, Michael C. Mozer:
Spotlight Attention: Robust Object-Centric Learning With a Spatial Locality Prior. CoRR abs/2305.19550 (2023) - [i62]Aniket Didolkar, Anirudh Goyal, Yoshua Bengio:
Cycle Consistency Driven Object Discovery. CoRR abs/2306.02204 (2023) - [i61]Sanjeev Arora, Anirudh Goyal:
A Theory for Emergence of Complex Skills in Language Models. CoRR abs/2307.15936 (2023) - [i60]Dingli Yu, Simran Kaur, Arushi Gupta, Jonah Brown-Cohen, Anirudh Goyal, Sanjeev Arora:
Skill-Mix: a Flexible and Expandable Family of Evaluations for AI models. CoRR abs/2310.17567 (2023) - [i59]Ayush Agrawal, Raghav Prabhakar, Anirudh Goyal, Dianbo Liu:
Physical Reasoning and Object Planning for Household Embodied Agents. CoRR abs/2311.13577 (2023) - [i58]Vedant Shah, Frederik Träuble, Ashish Malik, Hugo Larochelle, Michael Mozer, Sanjeev Arora, Yoshua Bengio, Anirudh Goyal:
Unlearning via Sparse Representations. CoRR abs/2311.15268 (2023) - 2022
- [c40]Samarth Sinha, Karsten Roth, Anirudh Goyal, Marzyeh Ghassemi, Zeynep Akata, Hugo Larochelle, Animesh Garg:
Uniform Priors for Data-Efficient Learning. CVPR Workshops 2022: 4016-4027 - [c39]Anirudh Goyal, Aniket Rajiv Didolkar, Alex Lamb, Kartikeya Badola, Nan Rosemary Ke, Nasim Rahaman, Jonathan Binas, Charles Blundell, Michael Curtis Mozer, Yoshua Bengio:
Coordination Among Neural Modules Through a Shared Global Workspace. ICLR 2022 - [c38]David Silver, Anirudh Goyal, Ivo Danihelka, Matteo Hessel, Hado van Hasselt:
Learning by Directional Gradient Descent. ICLR 2022 - [c37]Anirudh Goyal, Abram L. Friesen, Andrea Banino, Theophane Weber, Nan Rosemary Ke, Adrià Puigdomènech Badia, Arthur Guez, Mehdi Mirza, Peter Conway Humphreys, Ksenia Konyushkova, Michal Valko, Simon Osindero, Timothy P. Lillicrap, Nicolas Heess, Charles Blundell:
Retrieval-Augmented Reinforcement Learning. ICML 2022: 7740-7765 - [c36]Aniket Didolkar, Kshitij Gupta, Anirudh Goyal, Nitesh B. Gundavarapu, Alex Lamb, Nan Rosemary Ke, Yoshua Bengio:
Temporal Latent Bottleneck: Synthesis of Fast and Slow Processing Mechanisms in Sequence Learning. NeurIPS 2022 - [c35]Riashat Islam, Hongyu Zang, Anirudh Goyal, Alex Lamb, Kenji Kawaguchi, Xin Li, Romain Laroche, Yoshua Bengio, Remi Tachet des Combes:
Discrete Compositional Representations as an Abstraction for Goal Conditioned Reinforcement Learning. NeurIPS 2022 - [i57]Anirudh Goyal, Abram L. Friesen, Andrea Banino, Theophane Weber, Nan Rosemary Ke, Adrià Puigdomènech Badia, Arthur Guez, Mehdi Mirza, Ksenia Konyushkova, Michal Valko, Simon Osindero, Timothy P. Lillicrap, Nicolas Heess, Charles Blundell:
Retrieval-Augmented Reinforcement Learning. CoRR abs/2202.08417 (2022) - [i56]Mihir Prabhudesai, Anirudh Goyal, Deepak Pathak, Katerina Fragkiadaki:
Generating Fast and Slow: Scene Decomposition via Reconstruction. CoRR abs/2203.11194 (2022) - [i55]Nan Rosemary Ke, Silvia Chiappa, Jane Wang, Jörg Bornschein, Theophane Weber, Anirudh Goyal, Matthew M. Botvinick, Michael Mozer, Danilo Jimenez Rezende:
Learning to Induce Causal Structure. CoRR abs/2204.04875 (2022) - [i54]Dianbo Liu, Vedant Shah, Oussama Boussif, Cristian Meo, Anirudh Goyal, Tianmin Shu, Michael Mozer, Nicolas Heess, Yoshua Bengio:
Coordinating Policies Among Multiple Agents via an Intelligent Communication Channel. CoRR abs/2205.10607 (2022) - [i53]Aniket Didolkar, Kshitij Gupta, Anirudh Goyal, Alex Lamb, Nan Rosemary Ke, Yoshua Bengio:
Temporal Latent Bottleneck: Synthesis of Fast and Slow Processing Mechanisms in Sequence Learning. CoRR abs/2205.14794 (2022) - [i52]Nino Scherrer, Anirudh Goyal, Stefan Bauer, Yoshua Bengio, Nan Rosemary Ke:
On the Generalization and Adaption Performance of Causal Models. CoRR abs/2206.04620 (2022) - [i51]Frederik Träuble, Anirudh Goyal, Nasim Rahaman, Michael Mozer, Kenji Kawaguchi, Yoshua Bengio, Bernhard Schölkopf:
Discrete Key-Value Bottleneck. CoRR abs/2207.11240 (2022) - [i50]Dianbo Liu, Vedant Shah, Oussama Boussif, Cristian Meo, Anirudh Goyal, Tianmin Shu, Michael Mozer, Nicolas Heess, Yoshua Bengio:
Stateful active facilitator: Coordination and Environmental Heterogeneity in Cooperative Multi-Agent Reinforcement Learning. CoRR abs/2210.03022 (2022) - [i49]Dianbo Liu, Moksh Jain, Bonaventure Dossou, Qianli Shen, Salem Lahlou, Anirudh Goyal, Nikolay Malkin, Chris Emezue, Dinghuai Zhang, Nadhir Hassen, Xu Ji, Kenji Kawaguchi, Yoshua Bengio:
GFlowOut: Dropout with Generative Flow Networks. CoRR abs/2210.12928 (2022) - [i48]Riashat Islam, Hongyu Zang, Anirudh Goyal, Alex Lamb, Kenji Kawaguchi, Xin Li, Romain Laroche, Yoshua Bengio, Remi Tachet des Combes:
Discrete Factorial Representations as an Abstraction for Goal Conditioned Reinforcement Learning. CoRR abs/2211.00247 (2022) - [i47]Riashat Islam, Hongyu Zang, Manan Tomar, Aniket Didolkar, Md Mofijul Islam, Samin Yeasar Arnob, Tariq Iqbal, Xin Li, Anirudh Goyal, Nicolas Heess, Alex Lamb:
Representation Learning in Deep RL via Discrete Information Bottleneck. CoRR abs/2212.13835 (2022) - 2021
- [j1]Bernhard Schölkopf, Francesco Locatello, Stefan Bauer, Nan Rosemary Ke, Nal Kalchbrenner, Anirudh Goyal, Yoshua Bengio:
Toward Causal Representation Learning. Proc. IEEE 109(5): 612-634 (2021) - [c34]Samarth Sinha, Homanga Bharadhwaj, Anirudh Goyal, Hugo Larochelle, Animesh Garg, Florian Shkurti:
DIBS: Diversity Inducing Information Bottleneck in Model Ensembles. AAAI 2021: 9666-9674 - [c33]Alex Lamb, Anirudh Goyal, Agnieszka Slowik, Michael Mozer, Philippe Beaudoin, Yoshua Bengio:
Neural Function Modules with Sparse Arguments: A Dynamic Approach to Integrating Information across Layers. AISTATS 2021: 919-927 - [c32]Ossama Ahmed, Frederik Träuble, Anirudh Goyal, Alexander Neitz, Manuel Wuthrich, Yoshua Bengio, Bernhard Schölkopf, Stefan Bauer:
CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning. ICLR 2021 - [c31]Anirudh Goyal, Alex Lamb, Phanideep Gampa, Philippe Beaudoin, Charles Blundell, Sergey Levine, Yoshua Bengio, Michael Curtis Mozer:
Factorizing Declarative and Procedural Knowledge in Structured, Dynamical Environments. ICLR 2021 - [c30]Anirudh Goyal, Alex Lamb, Jordan Hoffmann, Shagun Sodhani, Sergey Levine, Yoshua Bengio, Bernhard Schölkopf:
Recurrent Independent Mechanisms. ICLR 2021 - [c29]Kanika Madan, Nan Rosemary Ke, Anirudh Goyal, Bernhard Schölkopf, Yoshua Bengio:
Fast And Slow Learning Of Recurrent Independent Mechanisms. ICLR 2021 - [c28]Nasim Rahaman, Anirudh Goyal, Muhammad Waleed Gondal, Manuel Wuthrich, Stefan Bauer, Yash Sharma, Yoshua Bengio, Bernhard Schölkopf:
Spatially Structured Recurrent Modules. ICLR 2021 - [c27]Saeid Asgari Taghanaki, Kristy Choi, Amir Hosein Khasahmadi, Anirudh Goyal:
Robust Representation Learning via Perceptual Similarity Metrics. ICML 2021: 10043-10053 - [c26]Frederik Träuble, Elliot Creager, Niki Kilbertus, Francesco Locatello, Andrea Dittadi, Anirudh Goyal, Bernhard Schölkopf, Stefan Bauer:
On Disentangled Representations Learned from Correlated Data. ICML 2021: 10401-10412 - [c25]Stefan Bauer, Manuel Wüthrich, Felix Widmaier, Annika Buchholz, Sebastian Stark, Anirudh Goyal, Thomas Steinbrenner, Joel Akpo, Shruti Joshi, Vincent Berenz, Vaibhav Agrawal, Niklas Funk, Julen Urain De Jesus, Jan Peters, Joe Watson, Claire Chen, Krishnan Srinivasan, Junwu Zhang, Jeffrey Zhang, Matthew R. Walter, Rishabh Madan, Takuma Yoneda, Denis Yarats, Arthur Allshire, Ethan K. Gordon, Tapomayukh Bhattacharjee, Siddhartha S. Srinivasa, Animesh Garg, Takahiro Maeda, Harshit Sikchi, Jilong Wang, Qingfeng Yao, Shuyu Yang, Robert McCarthy, Francisco Roldan Sanchez, Qiang Wang, David Cordova Bulens, Kevin McGuinness, Noel E. O'Connor, Stephen J. Redmond, Bernhard Schölkopf:
Real Robot Challenge: A Robotics Competition in the Cloud. NeurIPS (Competition and Demos) 2021: 190-204 - [c24]Dianbo Liu, Alex Lamb, Kenji Kawaguchi, Anirudh Goyal, Chen Sun, Michael C. Mozer, Yoshua Bengio:
Discrete-Valued Neural Communication. NeurIPS 2021: 2109-2121 - [c23]Aniket Didolkar, Anirudh Goyal, Nan Rosemary Ke, Charles Blundell, Philippe Beaudoin, Nicolas Heess, Michael Mozer, Yoshua Bengio:
Neural Production Systems. NeurIPS 2021: 25673-25687 - [c22]Nan Rosemary Ke, Aniket Didolkar, Sarthak Mittal, Anirudh Goyal, Guillaume Lajoie, Stefan Bauer, Danilo Jimenez Rezende, Michael Mozer, Yoshua Bengio, Chris Pal:
Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning. NeurIPS Datasets and Benchmarks 2021 - [i46]Bernhard Schölkopf, Francesco Locatello, Stefan Bauer, Nan Rosemary Ke, Nal Kalchbrenner, Anirudh Goyal, Yoshua Bengio:
Towards Causal Representation Learning. CoRR abs/2102.11107 (2021) - [i45]Alex Lamb, Di He, Anirudh Goyal, Guolin Ke, Chien-Feng Liao, Mirco Ravanelli, Yoshua Bengio:
Transformers with Competitive Ensembles of Independent Mechanisms. CoRR abs/2103.00336 (2021) - [i44]Anirudh Goyal, Aniket Didolkar, Alex Lamb, Kartikeya Badola, Nan Rosemary Ke, Nasim Rahaman, Jonathan Binas, Charles Blundell, Michael Mozer, Yoshua Bengio:
Coordination Among Neural Modules Through a Shared Global Workspace. CoRR abs/2103.01197 (2021) - [i43]Anirudh Goyal, Aniket Didolkar, Nan Rosemary Ke, Charles Blundell, Philippe Beaudoin, Nicolas Heess, Michael Mozer, Yoshua Bengio:
Neural Production Systems. CoRR abs/2103.01937 (2021) - [i42]Kanika Madan, Nan Rosemary Ke, Anirudh Goyal, Bernhard Schölkopf, Yoshua Bengio:
Fast and Slow Learning of Recurrent Independent Mechanisms. CoRR abs/2105.08710 (2021) - [i41]Saeid Asgari Taghanaki, Kristy Choi, Amir Khasahmadi, Anirudh Goyal:
Robust Representation Learning via Perceptual Similarity Metrics. CoRR abs/2106.06620 (2021) - [i40]Yashas Annadani, Jonas Rothfuss, Alexandre Lacoste, Nino Scherrer, Anirudh Goyal, Yoshua Bengio, Stefan Bauer:
Variational Causal Networks: Approximate Bayesian Inference over Causal Structures. CoRR abs/2106.07635 (2021) - [i39]Nan Rosemary Ke, Aniket Didolkar, Sarthak Mittal, Anirudh Goyal, Guillaume Lajoie, Stefan Bauer, Danilo J. Rezende, Yoshua Bengio, Michael Mozer, Christopher J. Pal:
Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning. CoRR abs/2107.00848 (2021) - [i38]Dianbo Liu, Alex Lamb, Kenji Kawaguchi, Anirudh Goyal, Chen Sun, Michael Curtis Mozer, Yoshua Bengio:
Discrete-Valued Neural Communication. CoRR abs/2107.02367 (2021) - [i37]Nino Scherrer, Olexa Bilaniuk, Yashas Annadani, Anirudh Goyal, Patrick Schwab, Bernhard Schölkopf, Michael C. Mozer, Yoshua Bengio, Stefan Bauer, Nan Rosemary Ke:
Learning Neural Causal Models with Active Interventions. CoRR abs/2109.02429 (2021) - 2020
- [c21]Dhaval Adjodah, Dan Calacci, Abhimanyu Dubey, Anirudh Goyal, P. M. Krafft, Esteban Moro, Alex Pentland:
Leveraging Communication Topologies Between Learning Agents in Deep Reinforcement Learning. AAMAS 2020: 1738-1740 - [c20]Yoshua Bengio, Tristan Deleu, Nasim Rahaman, Nan Rosemary Ke, Sébastien Lachapelle, Olexa Bilaniuk, Anirudh Goyal, Christopher J. Pal:
A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms. ICLR 2020 - [c19]Anirudh Goyal, Yoshua Bengio, Matthew M. Botvinick, Sergey Levine:
The Variational Bandwidth Bottleneck: Stochastic Evaluation on an Information Budget. ICLR 2020 - [c18]Anirudh Goyal, Shagun Sodhani, Jonathan Binas, Xue Bin Peng, Sergey Levine, Yoshua Bengio:
Reinforcement Learning with Competitive Ensembles of Information-Constrained Primitives. ICLR 2020 - [c17]Nasim Rahaman, Steffen Wolf, Anirudh Goyal, Roman Remme, Yoshua Bengio:
Learning the Arrow of Time for Problems in Reinforcement Learning. ICLR 2020 - [c16]Sarthak Mittal, Alex Lamb, Anirudh Goyal, Vikram Voleti, Murray Shanahan, Guillaume Lajoie, Michael Mozer, Yoshua Bengio:
Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules. ICML 2020: 6972-6986 - [c15]Samarth Sinha, Han Zhang, Anirudh Goyal, Yoshua Bengio, Hugo Larochelle, Augustus Odena:
Small-GAN: Speeding up GAN Training using Core-Sets. ICML 2020: 9005-9015 - [c14]Giancarlo Kerg, Bhargav Kanuparthi, Anirudh Goyal, Kyle Goyette, Yoshua Bengio, Guillaume Lajoie:
Untangling tradeoffs between recurrence and self-attention in artificial neural networks. NeurIPS 2020 - [c13]Samarth Sinha, Zhengli Zhao, Anirudh Goyal, Colin Raffel, Augustus Odena:
Top-k Training of GANs: Improving GAN Performance by Throwing Away Bad Samples. NeurIPS 2020 - [i36]Samarth Sinha, Anirudh Goyal, Colin Raffel, Augustus Odena:
Top-K Training of GANs: Improving Generators by Making Critics Less Critical. CoRR abs/2002.06224 (2020) - [i35]Samarth Sinha, Homanga Bharadhwaj, Anirudh Goyal, Hugo Larochelle, Animesh Garg, Florian Shkurti:
DIBS: Diversity inducing Information Bottleneck in Model Ensembles. CoRR abs/2003.04514 (2020) - [i34]Anirudh Goyal, Yoshua Bengio, Matthew M. Botvinick, Sergey Levine:
The Variational Bandwidth Bottleneck: Stochastic Evaluation on an Information Budget. CoRR abs/2004.11935 (2020) - [i33]Frederik Träuble, Elliot Creager, Niki Kilbertus, Anirudh Goyal, Francesco Locatello, Bernhard Schölkopf, Stefan Bauer:
Is Independence all you need? On the Generalization of Representations Learned from Correlated Data. CoRR abs/2006.07886 (2020) - [i32]Giancarlo Kerg, Bhargav Kanuparthi, Anirudh Goyal, Kyle Goyette, Yoshua Bengio, Guillaume Lajoie:
Untangling tradeoffs between recurrence and self-attention in neural networks. CoRR abs/2006.09471 (2020) - [i31]Anirudh Goyal, Alex Lamb, Phanideep Gampa, Philippe Beaudoin, Sergey Levine, Charles Blundell, Yoshua Bengio, Michael Mozer:
Object Files and Schemata: Factorizing Declarative and Procedural Knowledge in Dynamical Systems. CoRR abs/2006.16225 (2020) - [i30]Samarth Sinha, Anirudh Goyal, Animesh Garg:
Maximum Entropy Models for Fast Adaptation. CoRR abs/2006.16524 (2020) - [i29]Sarthak Mittal, Alex Lamb, Anirudh Goyal, Vikram Voleti, Murray Shanahan, Guillaume Lajoie, Michael Mozer, Yoshua Bengio:
Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules. CoRR abs/2006.16981 (2020) - [i28]Nasim Rahaman, Anirudh Goyal, Muhammad Waleed Gondal, Manuel Wuthrich, Stefan Bauer, Yash Sharma, Yoshua Bengio, Bernhard Schölkopf:
S2RMs: Spatially Structured Recurrent Modules. CoRR abs/2007.06533 (2020) - [i27]Ossama Ahmed, Frederik Träuble, Anirudh Goyal, Alexander Neitz, Manuel Wüthrich, Yoshua Bengio, Bernhard Schölkopf, Stefan Bauer:
CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning. CoRR abs/2010.04296 (2020) - [i26]Alex Lamb, Anirudh Goyal, Agnieszka Slowik, Michael Mozer, Philippe Beaudoin, Yoshua Bengio:
Neural Function Modules with Sparse Arguments: A Dynamic Approach to Integrating Information across Layers. CoRR abs/2010.08012 (2020) - [i25]Anirudh Goyal, Yoshua Bengio:
Inductive Biases for Deep Learning of Higher-Level Cognition. CoRR abs/2011.15091 (2020)
2010 – 2019
- 2019
- [c12]Anirudh Goyal, Philemon Brakel, William Fedus, Soumye Singhal, Timothy P. Lillicrap, Sergey Levine, Hugo Larochelle, Yoshua Bengio:
Recall Traces: Backtracking Models for Efficient Reinforcement Learning. ICLR (Poster) 2019 - [c11]Anirudh Goyal, Riashat Islam, Daniel Strouse, Zafarali Ahmed, Hugo Larochelle, Matthew M. Botvinick, Yoshua Bengio, Sergey Levine:
InfoBot: Transfer and Exploration via the Information Bottleneck. ICLR (Poster) 2019 - [c10]Nan Rosemary Ke, Amanpreet Singh, Ahmed Touati, Anirudh Goyal, Yoshua Bengio, Devi Parikh, Dhruv Batra:
Modeling the Long Term Future in Model-Based Reinforcement Learning. ICLR (Poster) 2019 - [c9]Alex Lamb, Jonathan Binas, Anirudh Goyal, Sandeep Subramanian, Ioannis Mitliagkas, Yoshua Bengio, Michael Mozer:
State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations. ICML 2019: 3622-3631 - [i24]Rithesh Kumar, Anirudh Goyal, Aaron C. Courville, Yoshua Bengio:
Maximum Entropy Generators for Energy-Based Models. CoRR abs/1901.08508 (2019) - [i23]Anirudh Goyal, Riashat Islam, Daniel Strouse, Zafarali Ahmed, Matthew M. Botvinick, Hugo Larochelle, Sergey Levine, Yoshua Bengio:
InfoBot: Transfer and Exploration via the Information Bottleneck. CoRR abs/1901.10902 (2019) - [i22]Yoshua Bengio, Tristan Deleu, Nasim Rahaman, Nan Rosemary Ke, Sébastien Lachapelle, Olexa Bilaniuk, Anirudh Goyal, Christopher J. Pal:
A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms. CoRR abs/1901.10912 (2019) - [i21]Dhaval Adjodah, Dan Calacci, Abhimanyu Dubey, Anirudh Goyal, Peter M. Krafft, Esteban Moro, Alex Pentland:
Communication Topologies Between Learning Agents in Deep Reinforcement Learning. CoRR abs/1902.06740 (2019) - [i20]Nan Rosemary Ke, Amanpreet Singh, Ahmed Touati, Anirudh Goyal, Yoshua Bengio, Devi Parikh, Dhruv Batra:
Learning Dynamics Model in Reinforcement Learning by Incorporating the Long Term Future. CoRR abs/1903.01599 (2019) - [i19]Alex Lamb, Jonathan Binas, Anirudh Goyal, Sandeep Subramanian, Ioannis Mitliagkas, Denis Kazakov, Yoshua Bengio, Michael C. Mozer:
State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations. CoRR abs/1905.11382 (2019) - [i18]Shagun Sodhani, Anirudh Goyal, Tristan Deleu, Yoshua Bengio, Sergey Levine, Jian Tang:
Learning Powerful Policies by Using Consistent Dynamics Model. CoRR abs/1906.04355 (2019) - [i17]Anirudh Goyal, Shagun Sodhani, Jonathan Binas, Xue Bin Peng, Sergey Levine, Yoshua Bengio:
Reinforcement Learning with Competitive Ensembles of Information-Constrained Primitives. CoRR abs/1906.10667 (2019) - [i16]Nasim Rahaman, Steffen Wolf, Anirudh Goyal, Roman Remme, Yoshua Bengio:
Learning the Arrow of Time. CoRR abs/1907.01285 (2019) - [i15]Anirudh Goyal, Alex Lamb, Jordan Hoffmann, Shagun Sodhani, Sergey Levine, Yoshua Bengio, Bernhard Schölkopf:
Recurrent Independent Mechanisms. CoRR abs/1909.10893 (2019) - [i14]Nan Rosemary Ke, Olexa Bilaniuk, Anirudh Goyal, Stefan Bauer, Hugo Larochelle, Chris Pal, Yoshua Bengio:
Learning Neural Causal Models from Unknown Interventions. CoRR abs/1910.01075 (2019) - [i13]Samarth Sinha, Han Zhang, Anirudh Goyal, Yoshua Bengio, Hugo Larochelle, Augustus Odena:
Small-GAN: Speeding Up GAN Training Using Core-sets. CoRR abs/1910.13540 (2019) - 2018
- [c8]Benjamin Scellier, Anirudh Goyal, Jonathan Binas, Thomas Mesnard, Yoshua Bengio:
Extending the Framework of Equilibrium Propagation to General Dynamics. ICLR (Workshop) 2018 - [c7]Nan Rosemary Ke, Anirudh Goyal, Olexa Bilaniuk, Jonathan Binas, Michael C. Mozer, Chris Pal, Yoshua Bengio:
Sparse Attentive Backtracking: Temporal Credit Assignment Through Reminding. NeurIPS 2018: 7651-7662 - [i12]Anirudh Goyal, Philemon Brakel, William Fedus, Timothy P. Lillicrap, Sergey Levine, Hugo Larochelle, Yoshua Bengio:
Recall Traces: Backtracking Models for Efficient Reinforcement Learning. CoRR abs/1804.00379 (2018) - [i11]Alex Lamb, Jonathan Binas, Anirudh Goyal, Dmitriy Serdyuk, Sandeep Subramanian, Ioannis Mitliagkas, Yoshua Bengio:
Fortified Networks: Improving the Robustness of Deep Networks by Modeling the Manifold of Hidden Representations. CoRR abs/1804.02485 (2018) - [i10]Benjamin Scellier, Anirudh Goyal, Jonathan Binas, Thomas Mesnard, Yoshua Bengio:
Generalization of Equilibrium Propagation to Vector Field Dynamics. CoRR abs/1808.04873 (2018) - [i9]Nan Rosemary Ke, Anirudh Goyal, Olexa Bilaniuk, Jonathan Binas, Michael C. Mozer, Chris Pal, Yoshua Bengio:
Sparse Attentive Backtracking: Temporal CreditAssignment Through Reminding. CoRR abs/1809.03702 (2018) - 2017
- [c6]Dzmitry Bahdanau, Philemon Brakel, Kelvin Xu, Anirudh Goyal, Ryan Lowe, Joelle Pineau, Aaron C. Courville, Yoshua Bengio:
An Actor-Critic Algorithm for Sequence Prediction. ICLR (Poster) 2017 - [c5]David Krueger, Tegan Maharaj, János Kramár, Mohammad Pezeshki, Nicolas Ballas, Nan Rosemary Ke, Anirudh Goyal, Yoshua Bengio, Aaron C. Courville, Christopher J. Pal:
Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations. ICLR (Poster) 2017 - [c4]Anirudh Goyal, Nan Rosemary Ke, Surya Ganguli, Yoshua Bengio:
Variational Walkback: Learning a Transition Operator as a Stochastic Recurrent Net. NIPS 2017: 4392-4402 - [c3]Anirudh Goyal, Alessandro Sordoni, Marc-Alexandre Côté, Nan Rosemary Ke, Yoshua Bengio:
Z-Forcing: Training Stochastic Recurrent Networks. NIPS 2017: 6713-6723 - [i8]Anirudh Goyal, Nan Rosemary Ke, Surya Ganguli, Yoshua Bengio:
Variational Walkback: Learning a Transition Operator as a Stochastic Recurrent Net. CoRR abs/1711.02282 (2017) - [i7]Nan Rosemary Ke, Anirudh Goyal, Olexa Bilaniuk, Jonathan Binas, Laurent Charlin, Chris Pal, Yoshua Bengio:
Sparse Attentive Backtracking: Long-Range Credit Assignment in Recurrent Networks. CoRR abs/1711.02326 (2017) - [i6]Anirudh Goyal, Nan Rosemary Ke, Alex Lamb, R. Devon Hjelm, Chris Pal, Joelle Pineau, Yoshua Bengio:
ACtuAL: Actor-Critic Under Adversarial Learning. CoRR abs/1711.04755 (2017) - [i5]Anirudh Goyal, Alessandro Sordoni, Marc-Alexandre Côté, Nan Rosemary Ke, Yoshua Bengio:
Z-Forcing: Training Stochastic Recurrent Networks. CoRR abs/1711.05411 (2017) - 2016
- [c2]Anirudh Goyal, Alex Lamb, Ying Zhang, Saizheng Zhang, Aaron C. Courville, Yoshua Bengio:
Professor Forcing: A New Algorithm for Training Recurrent Networks. NIPS 2016: 4601-4609 - [i4]David Krueger, Tegan Maharaj, János Kramár, Mohammad Pezeshki, Nicolas Ballas, Nan Rosemary Ke, Anirudh Goyal, Yoshua Bengio, Hugo Larochelle, Aaron C. Courville, Chris Pal:
Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations. CoRR abs/1606.01305 (2016) - [i3]Dzmitry Bahdanau, Philemon Brakel, Kelvin Xu, Anirudh Goyal, Ryan Lowe, Joelle Pineau, Aaron C. Courville, Yoshua Bengio:
An Actor-Critic Algorithm for Sequence Prediction. CoRR abs/1607.07086 (2016) - [i2]Alex Lamb, Anirudh Goyal, Ying Zhang, Saizheng Zhang, Aaron C. Courville, Yoshua Bengio:
Professor Forcing: A New Algorithm for Training Recurrent Networks. CoRR abs/1610.09038 (2016) - 2015
- [i1]Anirudh Goyal, Marius Leordeanu:
Stories in the Eye: Contextual Visual Interactions for Efficient Video to Language Translation. CoRR abs/1511.06674 (2015) - 2014
- [c1]Mohak Sukhwani, Suriya Singh, Anirudh Goyal, Aseem Behl, Pritish Mohapatra, Brijendra Kumar Bharti, C. V. Jawahar:
Monocular vision based road marking recognition for driver assistance and safety. ICVES 2014: 11-16
Coauthor Index
[c49] [i74] [i71] [i69] [j2] [c44] [c43] [c41] [i65] [i63] [i62] [i58] [c39] [c36] [c35] [i54] [i53] [i52] [i51] [i50] [i49] [i48] [j1] [c33] [c32] [c31] [c30] [c29] [c28] [c24] [c23] [c22] [i46] [i45] [i44] [i43] [i42] [i40] [i39] [i38] [i37] [c20] [c19] [c18] [c17] [c16] [c15] [c14] [i34] [i32] [i31] [i29] [i28] [i27] [i26] [i25] [c12] [c11] [c10] [c9] [i24] [i23] [i22] [i20] [i19] [i18] [i17] [i16] [i15] [i14] [i13] [c8] [c7] [i12] [i11] [i10] [i9] [c6] [c5] [c4] [c3] [i8] [i7] [i6] [i5] [c2] [i4] [i3] [i2]
Aniket Didolkar
aka: Aniket Rajiv Didolkar
aka: Aniket Rajiv Didolkar
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