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2020 – today
- 2024
[c29]Dipendra Misra, Akanksha Saran, Tengyang Xie, Alex Lamb, John Langford:
Towards Principled Representation Learning from Videos for Reinforcement Learning. ICLR 2024- [c28]Anurag Koul, Shivakanth Sujit, Shaoru Chen, Ben Evans, Lili Wu, Byron Xu, Rajan Chari, Riashat Islam, Raihan Seraj, Yonathan Efroni, Lekan P. Molu, Miroslav Dudík, John Langford, Alex Lamb:
PcLast: Discovering Plannable Continuous Latent States. ICML 2024 - [i43]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) - [i42]Dipendra Misra, Akanksha Saran, Tengyang Xie, Alex Lamb, John Langford:
Towards Principled Representation Learning from Videos for Reinforcement Learning. CoRR abs/2403.13765 (2024) - [i41]Lili Wu, Ben Evans, Riashat Islam
, Raihan Seraj, Yonathan Efroni, Alex Lamb:
Generalizing Multi-Step Inverse Models for Representation Learning to Finite-Memory POMDPs. CoRR abs/2404.14552 (2024) - [i40]Manan Tomar, Philippe Hansen-Estruch, Philip Bachman, Alex Lamb, John Langford, Matthew E. Taylor, Sergey Levine:
Video Occupancy Models. CoRR abs/2407.09533 (2024) - 2023
- [j4]Alex Lamb, Riashat Islam, Yonathan Efroni, Aniket Rajiv Didolkar, Dipendra Misra, Dylan J. Foster, Lekan P. Molu, Rajan Chari, Akshay Krishnamurthy, John Langford:
Guaranteed Discovery of Control-Endogenous Latent States with Multi-Step Inverse Models. Trans. Mach. Learn. Res. 2023 (2023) - [c27]Dianbo Liu, Alex Lamb, Xu Ji, Pascal Tikeng Notsawo Jr., Michael Mozer, Yoshua Bengio, Kenji Kawaguchi:
Adaptive Discrete Communication Bottlenecks with Dynamic Vector Quantization for Heterogeneous Representational Coarseness. AAAI 2023: 8825-8833 - [c26]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 - [c25]Savya Khosla, Chew Kin Whye, Jordan T. Ash, Cyril Zhang, Kenji Kawaguchi, Alex Lamb:
Understanding and Improving Neural Active Learning on Heteroskedastic Distributions. ECAI 2023: 1248-1255 - [c24]Riashat Islam, Manan Tomar, Alex Lamb, Yonathan Efroni, Hongyu Zang, Aniket Rajiv Didolkar, Dipendra Misra, Xin Li, Harm van Seijen, Remi Tachet des Combes, John Langford:
Principled Offline RL in the Presence of Rich Exogenous Information. ICML 2023: 14390-14421 - [i39]Sumukh K. Aithal, Anirudh Goyal, Alex Lamb, Yoshua Bengio, Michael Mozer:
Leveraging the Third Dimension in Contrastive Learning. CoRR abs/2301.11790 (2023) - [i38]Anurag Koul, Shivakanth Sujit, Shaoru Chen, Ben Evans, Lili Wu, Byron Xu, Rajan Chari, Riashat Islam, Raihan Seraj, Yonathan Efroni, Lekan P. Molu, Miro Dudík, John Langford, Alex Lamb:
PcLast: Discovering Plannable Continuous Latent States. CoRR abs/2311.03534 (2023) - 2022
- [j3]Vikas Verma, Kenji Kawaguchi, Alex Lamb, Juho Kannala, Arno Solin, Yoshua Bengio, David Lopez-Paz:
Interpolation consistency training for semi-supervised learning. Neural Networks 145: 90-106 (2022) - [j2]Alex Lamb, Vikas Verma, Kenji Kawaguchi, Alexander Matyasko, Savya Khosla, Juho Kannala, Yoshua Bengio:
Interpolated Adversarial Training: Achieving robust neural networks without sacrificing too much accuracy. Neural Networks 154: 218-233 (2022) - [c23]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 - [c22]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 - [c21]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 - [i37]Dianbo Liu, Alex Lamb, Xu Ji, Pascal Notsawo, Michael Mozer, Yoshua Bengio, Kenji Kawaguchi:
Adaptive Discrete Communication Bottlenecks with Dynamic Vector Quantization. CoRR abs/2202.01334 (2022) - [i36]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) - [i35]Alex Lamb, Riashat Islam, Yonathan Efroni, Aniket Didolkar, Dipendra Misra, Dylan J. Foster, Lekan P. Molu, Rajan Chari, Akshay Krishnamurthy, John Langford:
Guaranteed Discovery of Controllable Latent States with Multi-Step Inverse Models. CoRR abs/2207.08229 (2022) - [i34]Alexia Jolicoeur-Martineau, Alex Lamb, Vikas Verma, Aniket Didolkar:
CNT (Conditioning on Noisy Targets): A new Algorithm for Leveraging Top-Down Feedback. CoRR abs/2210.09505 (2022) - [i33]Riashat Islam, Manan Tomar, Alex Lamb, Yonathan Efroni, Hongyu Zang, Aniket Didolkar, Dipendra Misra, Xin Li, Harm van Seijen, Remi Tachet des Combes, John Langford:
Agent-Controller Representations: Principled Offline RL with Rich Exogenous Information. CoRR abs/2211.00164 (2022) - [i32]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) - [i31]Savya Khosla, Chew Kin Whye, Jordan T. Ash, Cyril Zhang, Kenji Kawaguchi, Alex Lamb:
Neural Active Learning on Heteroskedastic Distributions. CoRR abs/2211.00928 (2022) - [i30]Shengpu Tang, Felipe Vieira Frujeri, Dipendra Misra, Alex Lamb, John Langford, Paul Mineiro, Sebastian Kochman:
Towards Data-Driven Offline Simulations for Online Reinforcement Learning. CoRR abs/2211.07614 (2022) - [i29]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
- [c20]Vikas Verma, Meng Qu, Kenji Kawaguchi, Alex Lamb, Yoshua Bengio, Juho Kannala, Jian Tang:
GraphMix: Improved Training of GNNs for Semi-Supervised Learning. AAAI 2021: 10024-10032 - [c19]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 - [c18]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 - [c17]Anirudh Goyal, Alex Lamb, Jordan Hoffmann, Shagun Sodhani, Sergey Levine, Yoshua Bengio, Bernhard Schölkopf:
Recurrent Independent Mechanisms. ICLR 2021 - [c16]Dianbo Liu, Alex Lamb, Kenji Kawaguchi, Anirudh Goyal, Chen Sun, Michael C. Mozer, Yoshua Bengio:
Discrete-Valued Neural Communication. NeurIPS 2021: 2109-2121 - [i28]Alex Lamb:
A Brief Introduction to Generative Models. CoRR abs/2103.00265 (2021) - [i27]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) - [i26]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) - [i25]Alex Lamb, Tarin Clanuwat, Siyu Han, Mikel Bober-Irizar, Asanobu Kitamoto:
Predicting the Ordering of Characters in Japanese Historical Documents. CoRR abs/2106.06786 (2021) - [i24]Dianbo Liu, Alex Lamb, Kenji Kawaguchi, Anirudh Goyal, Chen Sun, Michael Curtis Mozer, Yoshua Bengio:
Discrete-Valued Neural Communication. CoRR abs/2107.02367 (2021) - 2020
- [j1]Alex Lamb, Tarin Clanuwat, Asanobu Kitamoto:
KuroNet: Regularized Residual U-Nets for End-to-End Kuzushiji Character Recognition. SN Comput. Sci. 1(3): 177 (2020) - [c15]Yingtao Tian, Chikahiko Suzuki, Tarin Clanuwat, Mikel Bober-Irizar, Alex Lamb, Asanobu Kitamoto:
KaoKore: A Pre-modern Japanese Art Facial Expression Dataset. ICCC 2020: 415-422 - [c14]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 - [c13]Alex Lamb, Sherjil Ozair, Vikas Verma, David Ha:
SketchTransfer: A Challenging New Task for Exploring Detail-Invariance and the Abstractions Learned by Deep Networks. WACV 2020: 952-961 - [i23]Yingtao Tian, Chikahiko Suzuki, Tarin Clanuwat, Mikel Bober-Irizar, Alex Lamb, Asanobu Kitamoto:
KaoKore: A Pre-modern Japanese Art Facial Expression Dataset. CoRR abs/2002.08595 (2020) - [i22]Saeid Asgari Taghanaki, Mohammad Havaei, Alex Lamb, Aditya Sanghi, Ara Danielyan, Tonya Custis:
Jigsaw-VAE: Towards Balancing Features in Variational Autoencoders. CoRR abs/2005.05496 (2020) - [i21]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) - [i20]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) - [i19]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)
2010 – 2019
- 2019
- [c12]Alex Lamb, Vikas Verma, Juho Kannala, Yoshua Bengio:
Interpolated Adversarial Training: Achieving Robust Neural Networks Without Sacrificing Too Much Accuracy. AISec@CCS 2019: 95-103 - [c11]Tarin Clanuwat, Alex Lamb, Asanobu Kitamoto:
KuroNet: Pre-Modern Japanese Kuzushiji Character Recognition with Deep Learning. ICDAR 2019: 607-614 - [c10]Christopher Beckham, Sina Honari, Alex Lamb, Vikas Verma, Farnoosh Ghadiri, R. Devon Hjelm, Christopher J. Pal:
Adversarial Mixup Resynthesizers. DGS@ICLR 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 - [c8]Vikas Verma, Alex Lamb, Christopher Beckham, Amir Najafi, Ioannis Mitliagkas, David Lopez-Paz, Yoshua Bengio:
Manifold Mixup: Better Representations by Interpolating Hidden States. ICML 2019: 6438-6447 - [c7]Vikas Verma, Alex Lamb, Juho Kannala, Yoshua Bengio, David Lopez-Paz:
Interpolation Consistency Training for Semi-supervised Learning. IJCAI 2019: 3635-3641 - [c6]Christopher Beckham, Sina Honari, Vikas Verma, Alex Lamb, Farnoosh Ghadiri, R. Devon Hjelm, Yoshua Bengio, Chris Pal:
On Adversarial Mixup Resynthesis. NeurIPS 2019: 4348-4359 - [i18]Christopher Beckham, Sina Honari, Alex Lamb, Vikas Verma, Farnoosh Ghadiri, R. Devon Hjelm, Christopher J. Pal:
Adversarial Mixup Resynthesizers. CoRR abs/1903.02709 (2019) - [i17]Vikas Verma, Alex Lamb, Juho Kannala, Yoshua Bengio, David Lopez-Paz:
Interpolation Consistency Training for Semi-Supervised Learning. CoRR abs/1903.03825 (2019) - [i16]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) - [i15]Alex Lamb, Vikas Verma, Juho Kannala, Yoshua Bengio:
Interpolated Adversarial Training: Achieving Robust Neural Networks without Sacrificing Accuracy. CoRR abs/1906.06784 (2019) - [i14]Anirudh Goyal, Alex Lamb, Jordan Hoffmann, Shagun Sodhani, Sergey Levine, Yoshua Bengio, Bernhard Schölkopf:
Recurrent Independent Mechanisms. CoRR abs/1909.10893 (2019) - [i13]Vikas Verma, Meng Qu, Alex Lamb, Yoshua Bengio, Juho Kannala, Jian Tang:
GraphMix: Regularized Training of Graph Neural Networks for Semi-Supervised Learning. CoRR abs/1909.11715 (2019) - [i12]Tarin Clanuwat, Alex Lamb, Asanobu Kitamoto:
KuroNet: Pre-Modern Japanese Kuzushiji Character Recognition with Deep Learning. CoRR abs/1910.09433 (2019) - [i11]Alex Lamb, Sherjil Ozair, Vikas Verma, David Ha:
SketchTransfer: A Challenging New Task for Exploring Detail-Invariance and the Abstractions Learned by Deep Networks. CoRR abs/1912.11570 (2019) - 2018
- [i10]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) - [i9]Vikas Verma, Alex Lamb, Christopher Beckham, Aaron C. Courville, Ioannis Mitliagkas, Yoshua Bengio:
Manifold Mixup: Encouraging Meaningful On-Manifold Interpolation as a Regularizer. CoRR abs/1806.05236 (2018) - [i8]Tarin Clanuwat, Mikel Bober-Irizar, Asanobu Kitamoto, Alex Lamb, Kazuaki Yamamoto, David Ha:
Deep Learning for Classical Japanese Literature. CoRR abs/1812.01718 (2018) - 2017
- [c5]Vincent Dumoulin, Ishmael Belghazi, Ben Poole, Alex Lamb, Martín Arjovsky, Olivier Mastropietro, Aaron C. Courville:
Adversarially Learned Inference. ICLR (Poster) 2017 - [c4]Alex Lamb, R. Devon Hjelm, Yaroslav Ganin, Joseph Paul Cohen, Aaron C. Courville, Yoshua Bengio:
GibbsNet: Iterative Adversarial Inference for Deep Graphical Models. NIPS 2017: 5089-5098 - [i7]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) - [i6]Alex Lamb, R. Devon Hjelm, Yaroslav Ganin, Joseph Paul Cohen, Aaron C. Courville, Yoshua Bengio:
GibbsNet: Iterative Adversarial Inference for Deep Graphical Models. CoRR abs/1712.04120 (2017) - 2016
- [c3]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 - [i5]Alex Lamb, Vincent Dumoulin, Aaron C. Courville:
Discriminative Regularization for Generative Models. CoRR abs/1602.03220 (2016) - [i4]Rami Al-Rfou, Guillaume Alain, Amjad Almahairi, Christof Angermüller, Dzmitry Bahdanau, Nicolas Ballas, Frédéric Bastien, Justin Bayer, Anatoly Belikov, Alexander Belopolsky, Yoshua Bengio, Arnaud Bergeron, James Bergstra, Valentin Bisson, Josh Bleecher Snyder, Nicolas Bouchard, Nicolas Boulanger-Lewandowski, Xavier Bouthillier, Alexandre de Brébisson, Olivier Breuleux, Pierre Luc Carrier, Kyunghyun Cho, Jan Chorowski, Paul F. Christiano, Tim Cooijmans, Marc-Alexandre Côté, Myriam Côté, Aaron C. Courville, Yann N. Dauphin, Olivier Delalleau, Julien Demouth, Guillaume Desjardins, Sander Dieleman, Laurent Dinh, Melanie Ducoffe, Vincent Dumoulin, Samira Ebrahimi Kahou, Dumitru Erhan, Ziye Fan, Orhan Firat, Mathieu Germain, Xavier Glorot, Ian J. Goodfellow, Matthew Graham, Çaglar Gülçehre, Philippe Hamel, Iban Harlouchet, Jean-Philippe Heng, Balázs Hidasi, Sina Honari, Arjun Jain, Sébastien Jean, Kai Jia, Mikhail Korobov, Vivek Kulkarni, Alex Lamb, Pascal Lamblin, Eric Larsen, César Laurent, Sean Lee, Simon Lefrançois, Simon Lemieux, Nicholas Léonard, Zhouhan Lin, Jesse A. Livezey, Cory Lorenz, Jeremiah Lowin, Qianli Ma, Pierre-Antoine Manzagol, Olivier Mastropietro, Robert McGibbon, Roland Memisevic, Bart van Merriënboer, Vincent Michalski, Mehdi Mirza, Alberto Orlandi, Christopher Joseph Pal, Razvan Pascanu, Mohammad Pezeshki, Colin Raffel, Daniel Renshaw, Matthew Rocklin, Adriana Romero, Markus Roth, Peter Sadowski, John Salvatier, François Savard, Jan Schlüter, John Schulman, Gabriel Schwartz, Iulian Vlad Serban, Dmitriy Serdyuk, Samira Shabanian, Étienne Simon, Sigurd Spieckermann, S. Ramana Subramanyam, Jakub Sygnowski, Jérémie Tanguay, Gijs van Tulder, Joseph P. Turian, Sebastian Urban, Pascal Vincent, Francesco Visin, Harm de Vries, David Warde-Farley, Dustin J. Webb, Matthew Willson, Kelvin Xu, Lijun Xue, Li Yao, Saizheng Zhang, Ying Zhang:
Theano: A Python framework for fast computation of mathematical expressions. CoRR abs/1605.02688 (2016) - [i3]Vincent Dumoulin, Ishmael Belghazi, Ben Poole, Alex Lamb, Martín Arjovsky, Olivier Mastropietro, Aaron C. Courville:
Adversarially Learned Inference. CoRR abs/1606.00704 (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]Guillaume Alain, Alex Lamb, Chinnadhurai Sankar, Aaron C. Courville, Yoshua Bengio:
Variance Reduction in SGD by Distributed Importance Sampling. CoRR abs/1511.06481 (2015) - 2013
- [c2]Alex Lamb, Michael J. Paul, Mark Dredze:
Separating Fact from Fear: Tracking Flu Infections on Twitter. HLT-NAACL 2013: 789-795 - 2012
- [c1]Alex Lamb, Michael J. Paul, Mark Dredze:
Investigating Twitter as a Source for Studying Behavioral Responses to Epidemics. AAAI Fall Symposium: Information Retrieval and Knowledge Discovery in Biomedical Text 2012
Coauthor Index
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