Conference and Journal Publications:

▪ Recurrent Independent Mechanisms. Anirudh Goyal, Alex Lamb, Shagun Sodhani, Jordan Hoffmann, Sergey Levine, Yoshua Bengio, Bernhard Scholkopf. ICLR 2021 Spotlight Oral.

▪ Neural Function Modules with Sparse Arguments: A Dynamic Approach to IntegratingInformation across Layers. Alex Lamb, Anirudh Goyal, Agnieszka Słowik, Michael Mozer,Philippe Beaudoin, Yoshua Bengio. AISTATS 2021. 29.8% Acceptance Rate

▪ GraphMix: Regularized Training of Graph Neural Networks for Semi-Supervised Learning. Vikas Verma, Meng Qu, Alex Lamb, Yoshua Bengio, Juho Kannala, Jian Tang. AAAI 2021.

▪ Combining Top-Down and Bottom-Up Signals with Attention over Modules. Sarthak Mittal, Alex Lamb, Anirudh Goyal, Vikram Voleti, Murray Shanahan, Guillaume Lajoie, Michael Mozer, Yoshua Bengio. ICML 2020. 21.8% Acceptance Rate

▪ KuroNet: Regularized Residual U-Nets for End-to-End Kuzushiji Character Recognition. Alex Lamb, Tarin Clanuwat, Asanobu Kitamoto. Springer-Nature Computer Science 2020.

▪ KaoKore: A Pre-modern Japanese Art Facial Expression Dataset. Yingtao Tian, Chikahiko Suzuki, Tarin Clanuwat, Mikel Bober-Irizar, Alex Lamb, Asanobu Kitamoto. ICCC 2020.

▪ SketchTransfer: A New Dataset for Exploring Detail-Invariance and the Abstractions Learned by Deep Networks. Alex Lamb, Sherjil Ozair, Vikas Verma, David Ha. WACV 2020. 34.6% Acceptance Rate

▪ On Adversarial Mixup Resynthesis. Chris Beckham, Sina Honari, Alex Lamb, Vikas Verma, Farnoosh Ghadiri, R. Devon Hjelm, Christopher Pal. NeurIPS 2019. 21.2% Acceptance Rate

▪ State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations. Alex Lamb, Jonathan Binas, Anirudh Goyal, Sandeep Subramanian, Ioannis Mitliagkas, Denis Kazakov, Yoshua Bengio, Michael C Mozer. ICML 2019. Long Oral, 5.0% Acceptance Rate

▪ Manifold Mixup: Learning Better Representations by Interpolating Hidden States. Alex Lamb*, Vikas Verma*, Christopher Beckham, Amir Najafi, Aaron Courville, Ioannis Mitliagkas, David Lopez-Paz, Yoshua Bengio. ICML 2019. 22.6% Acceptance Rate

▪ Interpolated Adversarial Training: Achieving Robust Neural Networks without Sacrificing too much Accuracy. Alex Lamb*, Vikas Verma*, David Lopez-Paz. AiSec 2019. 23.8% Acceptance Rate

▪ KuroNet: Pre-Modern Japanese Kuzushiji Character Recognition with Deep Learning. Alex Lamb*, Tarin Clanuwat*, Asanobu Kitamoto. ICDAR 2019. Oral, 12.9% Acceptance Rate

▪ Interpolation Consistency Training for Semi-Supervised Learning. Vikas Verma, Alex Lamb, Juho Kannala, Yoshua Bengio, David Lopez-Paz. IJCAI 2019. 17.9% Acceptance Rate

▪ End-to-End Pre-Modern Japanese Character (Kuzushiji) Spotting with Deep Learning. Tarin Clanuwat, Alex Lamb, Asanobu Kitamoto. Information Processing Society of Japan Conference on Digital Humanities 2018. Best Paper Award (1/60 accepted papers)

▪ GibbsNet: Iterative Adversarial Inference for Deep Graphical Models. Alex Lamb, Devon Hjelm, Yaroslav Ganin, Joseph Paul Cohen, Aaron Courville, Yoshua Bengio. NeurIPS 2017

▪ Adversarially Learned Inference. Vincent Dumoulin, Ishmael Belghazi, Ben Poole, Alex Lamb, Martin Arjovsky, Olivier Mastropietro, Aaron Courville. ICLR 2017

▪ Professor Forcing: A New Algorithm for Training Recurrent Networks. Alex Lamb*, Anirudh Goyal*, Ying Zhang, Saizheng Zhang, Aaron Courville, Yoshua Bengio. NeurIPS 2016

▪ Separating Fact from Fear: Tracking Flu Infections on Twitter. Alex Lamb, Michael J. Paul, Mark Dredze. NAACL 2013

Pre-print and Workshop Papers:

▪ Deep Learning for Classical Japanese Literature. Tarin Clanuwat, Mikel Bober-Irizar, Asanobu Kitamoto, Alex Lamb, Kazuaki Yamamoto, David Ha. NeurIPS Creativity Workshop 2019.

▪ Fortified Networks: Improving the Robustness of Deep Networks by Modeling the Manifold of Hidden Representations. Alex Lamb, Jonathan Binas, Anirudh Goyal, Dzmitry Serdyuk, Sandeep Subramanian, Ioannis Mitliagkas, Yoshua Bengio. Arxiv.

▪ Learning Generative Models with Locally Disentangled Latent Factors. Alex Lamb*, Brady Neal*, Sherjil Ozair, Aaron Courville, Yoshua Bengio, Ioannis Mitliagkas. Arxiv.

▪ ACtuAL: Actor-Critic Under Adversarial Learning. Anirudh Goyal, Nan Rosemary Ke, Alex Lamb, Devon Hjelm, Chris Pal, Joelle Pineau, Yoshua Bengio. Arxiv.

▪ Demand Forecasting Via Direct Quantile Loss Optimization. Kari Torkkola, Ru He, Wen-Yu Hua, Alex Lamb, Murali Balakrishnan Narayanaswamy, Zhihao Cen. US Patent. P36059-US.

▪ Discriminative Regularization for Generative Models. Alex Lamb, Vincent Dumoulin, Aaron Courville. CVPR Deepvision Workshop 2016.

▪ Variance Reduction in SGD by Distributed Importance Sampling. Guillaume Alain, Alex Lamb, Chinnadhurai Sankar, Aaron Courville, Yoshua Bengio. ICLR Workshop 2016.

▪ Investigating Twitter as a Source for Studying Behavioral Responses to Epidemics. Alex Lamb, Michael J. Paul, Mark Dredze. AAAI Fall Symposium on Information Retrieval and Knowledge Discovery in Biomedical Text 2012.