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no code implementations • EACL (LTEDI) 2021 • Olawale Onabola, Zhuang Ma, Xie Yang, Benjamin Akera, Ibraheem Abdulrahman, Jia Xue, Dianbo Liu, Yoshua Bengio

In this work, we present hBERT, where we modify certain layers of the pretrained BERT model with the new Hopfield Layer.

1 code implementation • ICML 2020 • Sai Krishna Gottipati, Boris Sattarov, Sufeng. Niu, Hao-Ran Wei, Yashaswi Pathak, Shengchao Liu, Simon Blackburn, Karam Thomas, Connor Coley, Jian Tang, Sarath Chandar, Yoshua Bengio

In this work, we propose a novel reinforcement learning (RL) setup for drug discovery that addresses this challenge by embedding the concept of synthetic accessibility directly into the de novo compound design system.

no code implementations • 3 Feb 2023 • Ling Pan, Nikolay Malkin, Dinghuai Zhang, Yoshua Bengio

Generative Flow Networks or GFlowNets are related to Monte-Carlo Markov chain methods (as they sample from a distribution specified by an energy function), reinforcement learning (as they learn a policy to sample composed objects through a sequence of steps), generative models (as they learn to represent and sample from a distribution) and amortized variational methods (as they can be used to learn to approximate and sample from an otherwise intractable posterior, given a prior and a likelihood).

no code implementations • 1 Feb 2023 • Moksh Jain, Tristan Deleu, Jason Hartford, Cheng-Hao Liu, Alex Hernandez-Garcia, Yoshua Bengio

However, in order to truly leverage large-scale data sets and high-throughput experimental setups, machine learning methods will need to be further improved and better integrated in the scientific discovery pipeline.

1 code implementation • 1 Feb 2023 • Alexander Tong, Nikolay Malkin, Guillaume Huguet, Yanlei Zhang, Jarrid Rector-Brooks, Kilian Fatras, Guy Wolf, Yoshua Bengio

CFM features a stable regression objective like that used to train the stochastic flow in diffusion models but enjoys the efficient inference of deterministic flow models.

1 code implementation • 30 Jan 2023 • Salem Lahlou, Tristan Deleu, Pablo Lemos, Dinghuai Zhang, Alexandra Volokhova, Alex Hernández-García, Léna Néhale Ezzine, Yoshua Bengio, Nikolay Malkin

Generative flow networks (GFlowNets) are amortized variational inference algorithms that are trained to sample from unnormalized target distributions over compositional objects.

no code implementations • 27 Jan 2023 • Sumukh Aithal, Anirudh Goyal, Alex Lamb, Yoshua Bengio, Michael Mozer

We evaluate these two approaches on three different SSL methods -- BYOL, SimSiam, and SwAV -- using ImageNette (10 class subset of ImageNet), ImageNet-100 and ImageNet-1k datasets.

no code implementations • 21 Jan 2023 • Xu Tan, Tao Qin, Jiang Bian, Tie-Yan Liu, Yoshua Bengio

Regeneration learning extends the concept of representation learning to data generation tasks, and can be regarded as a counterpart of traditional representation learning, since 1) regeneration learning handles the abstraction (Y') of the target data Y for data generation while traditional representation learning handles the abstraction (X') of source data X for data understanding; 2) both the processes of Y'-->Y in regeneration learning and X-->X' in representation learning can be learned in a self-supervised way (e. g., pre-training); 3) both the mappings from X to Y' in regeneration learning and from X' to Y in representation learning are simpler than the direct mapping from X to Y.

no code implementations • 27 Dec 2022 • Vikas Verma, Sarthak Mittal, Wai Hoh Tang, Hieu Pham, Juho Kannala, Yoshua Bengio, Arno Solin, Kenji Kawaguchi

Mixup is a popular data augmentation technique for training deep neural networks where additional samples are generated by linearly interpolating pairs of inputs and their labels.

no code implementations • 26 Nov 2022 • Sébastien Lachapelle, Tristan Deleu, Divyat Mahajan, Ioannis Mitliagkas, Yoshua Bengio, Simon Lacoste-Julien, Quentin Bertrand

Although disentangled representations are often said to be beneficial for downstream tasks, current empirical and theoretical understanding is limited.

no code implementations • 22 Nov 2022 • Alexandre Duval, Victor Schmidt, Santiago Miret, Yoshua Bengio, Alex Hernández-García, David Rolnick

Catalyst materials play a crucial role in the electrochemical reactions involved in a great number of industrial processes key to this transition, such as renewable energy storage and electrofuel synthesis.

no code implementations • 15 Nov 2022 • Leo Feng, Hossein Hajimirsadeghi, Yoshua Bengio, Mohamed Osama Ahmed

We demonstrate that LBANPs can trade-off the computational cost and performance according to the number of latent vectors.

no code implementations • 11 Nov 2022 • Sékou-Oumar Kaba, Arnab Kumar Mondal, Yan Zhang, Yoshua Bengio, Siamak Ravanbakhsh

Symmetry-based neural networks often constrain the architecture in order to achieve invariance or equivariance to a group of transformations.

no code implementations • 7 Nov 2022 • Alexandre Adam, Adam Coogan, Nikolay Malkin, Ronan Legin, Laurence Perreault-Levasseur, Yashar Hezaveh, Yoshua Bengio

Inferring accurate posteriors for high-dimensional representations of the brightness of gravitationally-lensed sources is a major challenge, in part due to the difficulties of accurately quantifying the priors.

no code implementations • 4 Nov 2022 • Nasim Rahaman, Martin Weiss, Frederik Träuble, Francesco Locatello, Alexandre Lacoste, Yoshua Bengio, Chris Pal, Li Erran Li, Bernhard Schölkopf

Geospatial Information Systems are used by researchers and Humanitarian Assistance and Disaster Response (HADR) practitioners to support a wide variety of important applications.

no code implementations • 4 Nov 2022 • Mizu Nishikawa-Toomey, Tristan Deleu, Jithendaraa Subramanian, Yoshua Bengio, Laurent Charlin

We extend the method of Bayesian causal structure learning using GFlowNets to learn not only the posterior distribution over the structure, but also the parameters of a linear-Gaussian model.

no code implementations • 1 Nov 2022 • Riashat Islam, Hongyu Zang, Anirudh Goyal, Alex Lamb, Kenji Kawaguchi, Xin Li, Romain Laroche, Yoshua Bengio, Remi Tachet des Combes

Goal-conditioned reinforcement learning (RL) is a promising direction for training agents that are capable of solving multiple tasks and reach a diverse set of objectives.

no code implementations • 1 Nov 2022 • Chanakya Ekbote, Moksh Jain, Payel Das, Yoshua Bengio

We hypothesize that this can lead to incompatibility between the inductive optimization biases in training $R$ and in training the GFlowNet, potentially leading to worse samples and slow adaptation to changes in the distribution.

no code implementations • 31 Oct 2022 • Sharut Gupta, Kartik Ahuja, Mohammad Havaei, Niladri Chatterjee, Yoshua Bengio

Federated learning aims to train predictive models for data that is distributed across clients, under the orchestration of a server.

no code implementations • 24 Oct 2022 • 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

These methods face two important challenges: (a) the posterior distribution over masks can be highly multi-modal which can be difficult to approximate with standard variational inference and (b) it is not trivial to fully utilize sample-dependent information and correlation among dropout masks to improve posterior estimation.

no code implementations • 23 Oct 2022 • Moksh Jain, Sharath Chandra Raparthy, Alex Hernandez-Garcia, Jarrid Rector-Brooks, Yoshua Bengio, Santiago Miret, Emmanuel Bengio

Through a series of experiments on synthetic and benchmark tasks, we empirically demonstrate that MOGFNs outperform existing methods in terms of Hypervolume, R2-distance and candidate diversity.

no code implementations • 15 Oct 2022 • Anthony Zador, Blake Richards, Bence Ölveczky, Sean Escola, Yoshua Bengio, Kwabena Boahen, Matthew Botvinick, Dmitri Chklovskii, Anne Churchland, Claudia Clopath, James DiCarlo, Surya Ganguli, Jeff Hawkins, Konrad Koerding, Alexei Koulakov, Yann Lecun, Timothy Lillicrap, Adam Marblestone, Bruno Olshausen, Alexandre Pouget, Cristina Savin, Terrence Sejnowski, Eero Simoncelli, Sara Solla, David Sussillo, Andreas S. Tolias, Doris Tsao

Neuroscience has long been an important driver of progress in artificial intelligence (AI).

no code implementations • 14 Oct 2022 • Nasim Rahaman, Martin Weiss, Francesco Locatello, Chris Pal, Yoshua Bengio, Bernhard Schölkopf, Li Erran Li, Nicolas Ballas

Recent work has seen the development of general purpose neural architectures that can be trained to perform tasks across diverse data modalities.

1 code implementation • 12 Oct 2022 • Chen Sun, Wannan Yang, Benjamin Alsbury-Nealy, Thomas Jiralerspong, Yoshua Bengio, Blake Richards

This takes advantage of the fact that it is easier to retrospectively identify the small set of steps that success is contingent upon than it is to prospectively predict reward at every step taken in the environment.

1 code implementation • 11 Oct 2022 • Oussama Boussif, Dan Assouline, Loubna Benabbou, Yoshua Bengio

The computational complexity of classical numerical methods for solving Partial Differential Equations (PDE) scales significantly as the resolution increases.

no code implementations • 11 Oct 2022 • Ruixiang Zhang, Tong Che, Boris Ivanovic, Renhao Wang, Marco Pavone, Yoshua Bengio, Liam Paull

Humans are remarkably good at understanding and reasoning about complex visual scenes.

no code implementations • 7 Oct 2022 • Ling Pan, Dinghuai Zhang, Aaron Courville, Longbo Huang, Yoshua Bengio

We specify intermediate rewards by intrinsic motivation to tackle the exploration problem in sparse reward environments.

1 code implementation • 4 Oct 2022 • Dianbo Liu, Vedant Shah, Oussama Boussif, Cristian Meo, Anirudh Goyal, Tianmin Shu, Michael Mozer, Nicolas Heess, Yoshua Bengio

We formalize the notions of coordination level and heterogeneity level of an environment and present HECOGrid, a suite of multi-agent RL environments that facilitates empirical evaluation of different MARL approaches across different levels of coordination and environmental heterogeneity by providing a quantitative control over coordination and heterogeneity levels of the environment.

Multi-agent Reinforcement Learning
reinforcement-learning
**+1**

no code implementations • 3 Oct 2022 • Dinghuai Zhang, Aaron Courville, Yoshua Bengio, Qinqing Zheng, Amy Zhang, Ricky T. Q. Chen

While the maximum entropy (MaxEnt) reinforcement learning (RL) framework -- often touted for its exploration and robustness capabilities -- is usually motivated from a probabilistic perspective, the use of deep probabilistic models has not gained much traction in practice due to their inherent complexity.

no code implementations • 2 Oct 2022 • Nikolay Malkin, Salem Lahlou, Tristan Deleu, Xu Ji, Edward Hu, Katie Everett, Dinghuai Zhang, Yoshua Bengio

This paper builds bridges between two families of probabilistic algorithms: (hierarchical) variational inference (VI), which is typically used to model distributions over continuous spaces, and generative flow networks (GFlowNets), which have been used for distributions over discrete structures such as graphs.

no code implementations • 1 Oct 2022 • Jiaye Teng, Chuan Wen, Dinghuai Zhang, Yoshua Bengio, Yang Gao, Yang Yuan

Conformal prediction is a distribution-free technique for establishing valid prediction intervals.

1 code implementation • 26 Sep 2022 • Kanika Madan, Jarrid Rector-Brooks, Maksym Korablyov, Emmanuel Bengio, Moksh Jain, Andrei Nica, Tom Bosc, Yoshua Bengio, Nikolay Malkin

Generative flow networks (GFlowNets) are a family of algorithms for training a sequential sampler of discrete objects under an unnormalized target density and have been successfully used for various probabilistic modeling tasks.

no code implementations • 24 Sep 2022 • Kartik Ahuja, Yixin Wang, Divyat Mahajan, Yoshua Bengio

Most existing works focus on identifiable representation learning with observational data, relying on distributional assumptions on latent (causal) factors.

no code implementations • 18 Sep 2022 • Bonaventure F. P. Dossou, Dianbo Liu, Xu Ji, Moksh Jain, Almer M. van der Sloot, Roger Palou, Michael Tyers, Yoshua Bengio

As antibiotic-resistant bacterial strains are rapidly spreading worldwide, infections caused by these strains are emerging as a global crisis causing the death of millions of people every year.

no code implementations • 13 Sep 2022 • Leo Feng, Padideh Nouri, Aneri Muni, Yoshua Bengio, Pierre-Luc Bacon

The problem can be framed as a global optimization problem where the objective is an expensive black-box function such that we can query large batches restricted with a limitation of a low number of rounds.

no code implementations • 6 Sep 2022 • Dinghuai Zhang, Ricky T. Q. Chen, Nikolay Malkin, Yoshua Bengio

Our framework provides a means for unifying training and inference algorithms, and provides a route to shine a unifying light over many generative models.

1 code implementation • 15 Aug 2022 • Tianyu Zhang, Andrew Williams, Soham Phade, Sunil Srinivasa, Yang Zhang, Prateek Gupta, Yoshua Bengio, Stephan Zheng

To facilitate this research, here we introduce RICE-N, a multi-region integrated assessment model that simulates the global climate and economy, and which can be used to design and evaluate the strategic outcomes for different negotiation and agreement frameworks.

no code implementations • 10 Aug 2022 • Siba Moussa, Michael Kilgour, Clara Jans, Alex Hernandez-Garcia, Miroslava Cuperlovic-Culf, Yoshua Bengio, Lena Simine

Inverse design of short single-stranded RNA and DNA sequences (aptamers) is the task of finding sequences that satisfy a set of desired criteria.

1 code implementation • 8 Aug 2022 • Jose Gallego-Posada, Juan Ramirez, Akram Erraqabi, Yoshua Bengio, Simon Lacoste-Julien

The performance of trained neural networks is robust to harsh levels of pruning.

no code implementations • 22 Jul 2022 • Frederik Träuble, Anirudh Goyal, Nasim Rahaman, Michael Mozer, Kenji Kawaguchi, Yoshua Bengio, Bernhard Schölkopf

We theoretically investigate the ability of the proposed model to minimize the effect of the distribution shifts and show that such a discrete bottleneck with (key, value) pairs reduces the complexity of the hypothesis class.

no code implementations • 30 Jun 2022 • Prateek Gupta, Elias B. Khalil, Didier Chetélat, Maxime Gasse, Yoshua Bengio, Andrea Lodi, M. Pawan Kumar

Given that B&B results in a tree of sub-MILPs, we ask (a) whether there are strong dependencies exhibited by the target heuristic among the neighboring nodes of the B&B tree, and (b) if so, whether we can incorporate them in our training procedure.

no code implementations • 26 Jun 2022 • Yezhen Wang, Tong Che, Bo Li, Kaitao Song, Hengzhi Pei, Yoshua Bengio, Dongsheng Li

Autoregressive generative models are commonly used, especially for those tasks involving sequential data.

1 code implementation • 9 Jun 2022 • Giancarlo Kerg, Sarthak Mittal, David Rolnick, Yoshua Bengio, Blake Richards, Guillaume Lajoie

Recent work has explored how forcing relational representations to remain distinct from sensory representations, as it seems to be the case in the brain, can help artificial systems.

no code implementations • 9 Jun 2022 • Nino Scherrer, Anirudh Goyal, Stefan Bauer, Yoshua Bengio, Nan Rosemary Ke

Our analysis shows that the modular neural causal models outperform other models on both zero and few-shot adaptation in low data regimes and offer robust generalization.

1 code implementation • 7 Jun 2022 • Dinghuai Zhang, Hongyang Zhang, Aaron Courville, Yoshua Bengio, Pradeep Ravikumar, Arun Sai Suggala

Consequently, an emerging line of work has focused on learning an ensemble of neural networks to defend against adversarial attacks.

1 code implementation • 6 Jun 2022 • Sarthak Mittal, Yoshua Bengio, Guillaume Lajoie

Inspired from human cognition, machine learning systems are gradually revealing advantages of sparser and more modular architectures.

1 code implementation • 2 Jun 2022 • Kartik Ahuja, Jason Hartford, Yoshua Bengio

We show that if the perturbations are applied only on mutually exclusive blocks of latents, we identify the latents up to those blocks.

no code implementations • 30 May 2022 • Benjamin Scellier, Siddhartha Mishra, Yoshua Bengio, Yann Ollivier

This work establishes that a physical system can perform statistical learning without gradient computations, via an Agnostic Equilibrium Propagation (Aeqprop) procedure that combines energy minimization, homeostatic control, and nudging towards the correct response.

no code implementations • 30 May 2022 • Aniket Didolkar, Kshitij Gupta, Anirudh Goyal, Nitesh B. Gundavarapu, Alex Lamb, Nan Rosemary Ke, Yoshua Bengio

A slow stream that is recurrent in nature aims to learn a specialized and compressed representation, by forcing chunks of $K$ time steps into a single representation which is divided into multiple vectors.

no code implementations • 23 May 2022 • Sharut Gupta, Kartik Ahuja, Mohammad Havaei, Niladri Chatterjee, Yoshua Bengio

Federated learning aims to train predictive models for data that is distributed across clients, under the orchestration of a server.

no code implementations • 21 May 2022 • Dianbo Liu, Vedant Shah, Oussama Boussif, Cristian Meo, Anirudh Goyal, Tianmin Shu, Michael Mozer, Nicolas Heess, Yoshua Bengio

In Multi-Agent Reinforcement Learning (MARL), specialized channels are often introduced that allow agents to communicate directly with one another.

Intelligent Communication
Multi-agent Reinforcement Learning
**+2**

1 code implementation • 19 May 2022 • Mike He Zhu, Léna Néhale Ezzine, Dianbo Liu, Yoshua Bengio

Federated learning is a distributed machine learning approach which enables a shared server model to learn by aggregating the locally-computed parameter updates with the training data from spatially-distributed client silos.

no code implementations • 6 May 2022 • Sanghyun Yoo, Inchul Song, Yoshua Bengio

In this paper, we propose a novel acoustic modeling technique for accurate multi-dialect speech recognition with a single AM.

no code implementations • 21 Mar 2022 • Akram Erraqabi, Marlos C. Machado, Mingde Zhao, Sainbayar Sukhbaatar, Alessandro Lazaric, Ludovic Denoyer, Yoshua Bengio

In reinforcement learning, the graph Laplacian has proved to be a valuable tool in the task-agnostic setting, with applications ranging from skill discovery to reward shaping.

no code implementations • 3 Mar 2022 • Francois St-Hilaire, Dung Do Vu, Antoine Frau, Nathan Burns, Farid Faraji, Joseph Potochny, Stephane Robert, Arnaud Roussel, Selene Zheng, Taylor Glazier, Junfel Vincent Romano, Robert Belfer, Muhammad Shayan, Ariella Smofsky, Tommy Delarosbil, Seulmin Ahn, Simon Eden-Walker, Kritika Sony, Ansona Onyi Ching, Sabina Elkins, Anush Stepanyan, Adela Matajova, Victor Chen, Hossein Sahraei, Robert Larson, Nadia Markova, Andrew Barkett, Laurent Charlin, Yoshua Bengio, Iulian Vlad Serban, Ekaterina Kochmar

AI-powered learning can provide millions of learners with a highly personalized, active and practical learning experience, which is key to successful learning.

no code implementations • ICLR 2022 • Tristan Deleu, David Kanaa, Leo Feng, Giancarlo Kerg, Yoshua Bengio, Guillaume Lajoie, Pierre-Luc Bacon

Drawing inspiration from gradient-based meta-learning methods with infinitely small gradient steps, we introduce Continuous-Time Meta-Learning (COMLN), a meta-learning algorithm where adaptation follows the dynamics of a gradient vector field.

1 code implementation • 2 Mar 2022 • Moksh Jain, Emmanuel Bengio, Alex-Hernandez Garcia, Jarrid Rector-Brooks, Bonaventure F. P. Dossou, Chanakya Ekbote, Jie Fu, Tianyu Zhang, Micheal Kilgour, Dinghuai Zhang, Lena Simine, Payel Das, Yoshua Bengio

In this work, we propose an active learning algorithm leveraging epistemic uncertainty estimation and the recently proposed GFlowNets as a generator of diverse candidate solutions, with the objective to obtain a diverse batch of useful (as defined by some utility function, for example, the predicted anti-microbial activity of a peptide) and informative candidates after each round.

1 code implementation • 28 Feb 2022 • Edoardo M. Ponti, Alessandro Sordoni, Yoshua Bengio, Siva Reddy

By jointly learning these and a task-skill allocation matrix, the network for each task is instantiated as the average of the parameters of active skills.

1 code implementation • 28 Feb 2022 • Tristan Deleu, António Góis, Chris Emezue, Mansi Rankawat, Simon Lacoste-Julien, Stefan Bauer, Yoshua Bengio

In Bayesian structure learning, we are interested in inferring a distribution over the directed acyclic graph (DAG) structure of Bayesian networks, from data.

1 code implementation • 7 Feb 2022 • Paul Bertin, Jarrid Rector-Brooks, Deepak Sharma, Thomas Gaudelet, Andrew Anighoro, Torsten Gross, Francisco Martinez-Pena, Eileen L. Tang, Suraj M S, Cristian Regep, Jeremy Hayter, Maksym Korablyov, Nicholas Valiante, Almer van der Sloot, Mike Tyers, Charles Roberts, Michael M. Bronstein, Luke L. Lairson, Jake P. Taylor-King, Yoshua Bengio

For large libraries of small molecules, exhaustive combinatorial chemical screens become infeasible to perform when considering a range of disease models, assay conditions, and dose ranges.

1 code implementation • 3 Feb 2022 • Dinghuai Zhang, Nikolay Malkin, Zhen Liu, Alexandra Volokhova, Aaron Courville, Yoshua Bengio

We present energy-based generative flow networks (EB-GFN), a novel probabilistic modeling algorithm for high-dimensional discrete data.

no code implementations • 2 Feb 2022 • Dianbo Liu, Alex Lamb, Xu Ji, Pascal Notsawo, Mike Mozer, Yoshua Bengio, Kenji Kawaguchi

Vector Quantization (VQ) is a method for discretizing latent representations and has become a major part of the deep learning toolkit.

1 code implementation • 31 Jan 2022 • Nikolay Malkin, Moksh Jain, Emmanuel Bengio, Chen Sun, Yoshua Bengio

Generative flow networks (GFlowNets) are a method for learning a stochastic policy for generating compositional objects, such as graphs or strings, from a given unnormalized density by sequences of actions, where many possible action sequences may lead to the same object.

1 code implementation • 31 Jan 2022 • Maxence Ernoult, Fabrice Normandin, Abhinav Moudgil, Sean Spinney, Eugene Belilovsky, Irina Rish, Blake Richards, Yoshua Bengio

As such, it is important to explore learning algorithms that come with strong theoretical guarantees and can match the performance of backpropagation (BP) on complex tasks.

1 code implementation • 27 Jan 2022 • Ramnath Kumar, Tristan Deleu, Yoshua Bengio

Recent studies show that task distribution plays a vital role in the meta-learner's performance.

1 code implementation • 27 Jan 2022 • Ramnath Kumar, Tristan Deleu, Yoshua Bengio

We present a learning mechanism for reinforcement learning of closely related skills parameterized via a skill embedding space.

1 code implementation • 27 Dec 2021 • Enoch Tetteh, Joseph Viviano, Yoshua Bengio, David Krueger, Joseph Paul Cohen

Learning models that generalize under different distribution shifts in medical imaging has been a long-standing research challenge.

1 code implementation • 6 Dec 2021 • Mohammad Pezeshki, Amartya Mitra, Yoshua Bengio, Guillaume Lajoie

A key challenge in building theoretical foundations for deep learning is the complex optimization dynamics of neural networks, resulting from the high-dimensional interactions between the large number of network parameters.

no code implementations • 17 Nov 2021 • Yoshua Bengio, Salem Lahlou, Tristan Deleu, Edward J. Hu, Mo Tiwari, Emmanuel Bengio

Generative Flow Networks (GFlowNets) have been introduced as a method to sample a diverse set of candidates in an active learning context, with a training objective that makes them approximately sample in proportion to a given reward function.

no code implementations • ICLR 2022 • Kartik Ahuja, Jason Hartford, Yoshua Bengio

These results suggest that by exploiting inductive biases on mechanisms, it is possible to design a range of new identifiable representation learning approaches.

no code implementations • 28 Oct 2021 • Nicholas Roy, Ingmar Posner, Tim Barfoot, Philippe Beaudoin, Yoshua Bengio, Jeannette Bohg, Oliver Brock, Isabelle Depatie, Dieter Fox, Dan Koditschek, Tomas Lozano-Perez, Vikash Mansinghka, Christopher Pal, Blake Richards, Dorsa Sadigh, Stefan Schaal, Gaurav Sukhatme, Denis Therien, Marc Toussaint, Michiel Van de Panne

Machine learning has long since become a keystone technology, accelerating science and applications in a broad range of domains.

1 code implementation • ICLR 2022 • Max Morrison, Rithesh Kumar, Kundan Kumar, Prem Seetharaman, Aaron Courville, Yoshua Bengio

We show that simple pitch and periodicity conditioning is insufficient for reducing this error relative to using autoregression.

3 code implementations • ICLR 2022 • Sarthak Mittal, Sharath Chandra Raparthy, Irina Rish, Yoshua Bengio, Guillaume Lajoie

Through our qualitative analysis, we demonstrate that Compositional Attention leads to dynamic specialization based on the type of retrieval needed.

1 code implementation • ICLR 2022 • Vijay Prakash Dwivedi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio, Xavier Bresson

An approach to tackle this issue is to introduce Positional Encoding (PE) of nodes, and inject it into the input layer, like in Transformers.

Ranked #6 on Graph Regression on ZINC-500k

no code implementations • NeurIPS 2021 • Nasim Rahaman, Muhammad Waleed Gondal, Shruti Joshi, Peter Gehler, Yoshua Bengio, Francesco Locatello, Bernhard Schölkopf

Modern neural network architectures can leverage large amounts of data to generalize well within the training distribution.

2 code implementations • ICLR 2022 • Victor Schmidt, Alexandra Sasha Luccioni, Mélisande Teng, Tianyu Zhang, Alexia Reynaud, Sunand Raghupathi, Gautier Cosne, Adrien Juraver, Vahe Vardanyan, Alex Hernandez-Garcia, Yoshua Bengio

Climate change is a major threat to humanity, and the actions required to prevent its catastrophic consequences include changes in both policy-making and individual behaviour.

no code implementations • ICLR 2022 • Dinghuai Zhang, Jie Fu, Yoshua Bengio, Aaron Courville

Black-box optimization formulations for biological sequence design have drawn recent attention due to their promising potential impact on the pharmaceutical industry.

no code implementations • 29 Sep 2021 • Xiao Jing, Zhenwei Zhu, Hongliang Li, Xin Pei, Yoshua Bengio, Tong Che, Hongyong Song

One of the greatest challenges of reinforcement learning is efficient exploration, especially when training signals are sparse or deceptive.

1 code implementation • 6 Sep 2021 • Nino Scherrer, Olexa Bilaniuk, Yashas Annadani, Anirudh Goyal, Patrick Schwab, Bernhard Schölkopf, Michael C. Mozer, Yoshua Bengio, Stefan Bauer, Nan Rosemary Ke

Discovering causal structures from data is a challenging inference problem of fundamental importance in all areas of science.

no code implementations • NeurIPS 2021 • Dianbo Liu, Alex Lamb, Kenji Kawaguchi, Anirudh Goyal, Chen Sun, Michael Curtis Mozer, Yoshua Bengio

Deep learning has advanced from fully connected architectures to structured models organized into components, e. g., the transformer composed of positional elements, modular architectures divided into slots, and graph neural nets made up of nodes.

1 code implementation • 2 Jul 2021 • Nan Rosemary Ke, Aniket Didolkar, Sarthak Mittal, Anirudh Goyal, Guillaume Lajoie, Stefan Bauer, Danilo Rezende, Yoshua Bengio, Michael Mozer, Christopher Pal

A central goal for AI and causality is thus the joint discovery of abstract representations and causal structure.

2 code implementations • NeurIPS 2021 • Kevin Xia, Kai-Zhan Lee, Yoshua Bengio, Elias Bareinboim

Given this property, one may be tempted to surmise that a collection of neural nets is capable of learning any SCM by training on data generated by that SCM.

1 code implementation • 14 Jun 2021 • Yashas Annadani, Jonas Rothfuss, Alexandre Lacoste, Nino Scherrer, Anirudh Goyal, Yoshua Bengio, Stefan Bauer

However, a crucial aspect to acting intelligently upon the knowledge about causal structure which has been inferred from finite data demands reasoning about its uncertainty.

no code implementations • ICML Workshop URL 2021 • Akram Erraqabi, Mingde Zhao, Marlos C. Machado, Yoshua Bengio, Sainbayar Sukhbaatar, Ludovic Denoyer, Alessandro Lazaric

In this work, we introduce a method that explicitly couples representation learning with exploration when the agent is not provided with a uniform prior over the state space.

2 code implementations • NeurIPS 2021 • Kartik Ahuja, Ethan Caballero, Dinghuai Zhang, Jean-Christophe Gagnon-Audet, Yoshua Bengio, Ioannis Mitliagkas, Irina Rish

To answer these questions, we revisit the fundamental assumptions in linear regression tasks, where invariance-based approaches were shown to provably generalize OOD.

2 code implementations • NeurIPS 2021 • Emmanuel Bengio, Moksh Jain, Maksym Korablyov, Doina Precup, Yoshua Bengio

Using insights from Temporal Difference learning, we propose GFlowNet, based on a view of the generative process as a flow network, making it possible to handle the tricky case where different trajectories can yield the same final state, e. g., there are many ways to sequentially add atoms to generate some molecular graph.

3 code implementations • 8 Jun 2021 • Mirco Ravanelli, Titouan Parcollet, Peter Plantinga, Aku Rouhe, Samuele Cornell, Loren Lugosch, Cem Subakan, Nauman Dawalatabad, Abdelwahab Heba, Jianyuan Zhong, Ju-chieh Chou, Sung-Lin Yeh, Szu-Wei Fu, Chien-Feng Liao, Elena Rastorgueva, François Grondin, William Aris, Hwidong Na, Yan Gao, Renato de Mori, Yoshua Bengio

SpeechBrain is an open-source and all-in-one speech toolkit.

1 code implementation • NeurIPS 2021 • Mingde Zhao, Zhen Liu, Sitao Luan, Shuyuan Zhang, Doina Precup, Yoshua Bengio

We present an end-to-end, model-based deep reinforcement learning agent which dynamically attends to relevant parts of its state during planning.

Model-based Reinforcement Learning
Out-of-Distribution Generalization
**+2**

no code implementations • 18 May 2021 • Kanika Madan, Nan Rosemary Ke, Anirudh Goyal, Bernhard Schölkopf, Yoshua Bengio

To study these ideas, we propose a particular training framework in which we assume that the pieces of knowledge an agent needs and its reward function are stationary and can be re-used across tasks.

1 code implementation • 15 May 2021 • Minkai Xu, Wujie Wang, Shitong Luo, Chence Shi, Yoshua Bengio, Rafael Gomez-Bombarelli, Jian Tang

Specifically, the molecular graph is first encoded in a latent space, and then the 3D structures are generated by solving a principled bilevel optimization program.

no code implementations • 15 Apr 2021 • Francois St-Hilaire, Nathan Burns, Robert Belfer, Muhammad Shayan, Ariella Smofsky, Dung Do Vu, Antoine Frau, Joseph Potochny, Farid Faraji, Vincent Pavero, Neroli Ko, Ansona Onyi Ching, Sabina Elkins, Anush Stepanyan, Adela Matajova, Laurent Charlin, Yoshua Bengio, Iulian Vlad Serban, Ekaterina Kochmar

Personalization and active learning are key aspects to successful learning.

no code implementations • 6 Apr 2021 • Olawale Onabola, Zhuang Ma, Yang Xie, Benjamin Akera, Abdulrahman Ibraheem, Jia Xue, Dianbo Liu, Yoshua Bengio

In this work, we present hBERT, where we modify certain layers of the pretrained BERT model with the new Hopfield Layer.

no code implementations • NeurIPS 2021 • Anirudh Goyal, Aniket Didolkar, Nan Rosemary Ke, Charles Blundell, Philippe Beaudoin, Nicolas Heess, Michael Mozer, Yoshua Bengio

First, GNNs do not predispose interactions to be sparse, as relationships among independent entities are likely to be.

1 code implementation • ICLR 2022 • Anirudh Goyal, Aniket Didolkar, Alex Lamb, Kartikeya Badola, Nan Rosemary Ke, Nasim Rahaman, Jonathan Binas, Charles Blundell, Michael Mozer, Yoshua Bengio

We explore the use of such a communication channel in the context of deep learning for modeling the structure of complex environments.

1 code implementation • Proceedings of Machine Learning Research 1:1–13 2021 • Margaux Luck*, Tristan Sylvain*, Joseph Paul Cohen, Heloise Cardinal, Andrea Lodi, Yoshua Bengio

Survival analysis is a type of semi-supervised task where the target output (the survival time) is often right-censored.

no code implementations • 27 Feb 2021 • Alex Lamb, Di He, Anirudh Goyal, Guolin Ke, Chien-Feng Liao, Mirco Ravanelli, Yoshua Bengio

In this work we explore a way in which the Transformer architecture is deficient: it represents each position with a large monolithic hidden representation and a single set of parameters which are applied over the entire hidden representation.

no code implementations • 22 Feb 2021 • Bernhard Schölkopf, Francesco Locatello, Stefan Bauer, Nan Rosemary Ke, Nal Kalchbrenner, Anirudh Goyal, Yoshua Bengio

The two fields of machine learning and graphical causality arose and developed separately.

3 code implementations • ICLR 2021 • Minkai Xu, Shitong Luo, Yoshua Bengio, Jian Peng, Jian Tang

Inspired by the recent progress in deep generative models, in this paper, we propose a novel probabilistic framework to generate valid and diverse conformations given a molecular graph.

1 code implementation • 16 Feb 2021 • Salem Lahlou, Moksh Jain, Hadi Nekoei, Victor Ion Butoi, Paul Bertin, Jarrid Rector-Brooks, Maksym Korablyov, Yoshua Bengio

Epistemic Uncertainty is a measure of the lack of knowledge of a learner which diminishes with more evidence.

1 code implementation • 7 Feb 2021 • Tristan Deleu, Yoshua Bengio

The parameters of a neural network are naturally organized in groups, some of which might not contribute to its overall performance.

no code implementations • 14 Jan 2021 • Axel Laborieux, Maxence Ernoult, Benjamin Scellier, Yoshua Bengio, Julie Grollier, Damien Querlioz

Equilibrium Propagation (EP) is a biologically-inspired counterpart of Backpropagation Through Time (BPTT) which, owing to its strong theoretical guarantees and the locality in space of its learning rule, fosters the design of energy-efficient hardware dedicated to learning.

no code implementations • ICLR 2021 • Nasim Rahaman, Anirudh Goyal, Muhammad Waleed Gondal, Manuel Wuthrich, Stefan Bauer, Yash Sharma, Yoshua Bengio, Bernhard Schölkopf

Capturing the structure of a data-generating process by means of appropriate inductive biases can help in learning models that generalise well and are robust to changes in the input distribution.

no code implementations • 1 Jan 2021 • Nan Rosemary Ke, Olexa Bilaniuk, Anirudh Goyal, Stefan Bauer, Bernhard Schölkopf, Michael Curtis Mozer, Hugo Larochelle, Christopher Pal, Yoshua Bengio

Promising results have driven a recent surge of interest in continuous optimization methods for Bayesian network structure learning from observational data.

no code implementations • ICLR 2021 • Kanika Madan, Nan Rosemary Ke, Anirudh Goyal, Bernhard Schölkopf, Yoshua Bengio

Decomposing knowledge into interchangeable pieces promises a generalization advantage when there are changes in distribution.

no code implementations • 1 Jan 2021 • Anthony Ortiz, Kris Sankaran, Olac Fuentes, Christopher Kiekintveld, Pascal Vincent, Yoshua Bengio, Doina Precup

In this work we tackle the problem of out-of-distribution generalization through conditional computation.

no code implementations • ICLR 2021 • Faruk Ahmed, Yoshua Bengio, Harm van Seijen, Aaron Courville

We consider situations where the presence of dominant simpler correlations with the target variable in a training set can cause an SGD-trained neural network to be less reliant on more persistently-correlating complex features.

no code implementations • ICLR 2021 • Anirudh Goyal, Alex Lamb, Phanideep Gampa, Philippe Beaudoin, Charles Blundell, Sergey Levine, Yoshua Bengio, Michael Curtis Mozer

To use a video game as an illustration, two enemies of the same type will share schemata but will have separate object files to encode their distinct state (e. g., health, position).

no code implementations • 1 Jan 2021 • Devansh Arpit, Huan Wang, Caiming Xiong, Richard Socher, Yoshua Bengio

Disjoint Manifold Separation: Neural Bayes allows us to formulate an objective which can optimally label samples from disjoint manifolds present in the support of a continuous distribution.

1 code implementation • ICCV 2021 • Yuwei Cheng, Jiannan Zhu, Mengxin Jiang, Jie Fu, Changsong Pang, Peidong Wang, Kris Sankaran, Olawale Onabola, Yimin Liu, Dianbo Liu, Yoshua Bengio

To promote the practical application for autonomous floating wastes cleaning, we present FloW, the first dataset for floating waste detection in inland water areas.

1 code implementation • 9 Dec 2020 • Shimaa Baraka, Benjamin Akera, Bibek Aryal, Tenzing Sherpa, Finu Shresta, Anthony Ortiz, Kris Sankaran, Juan Lavista Ferres, Mir Matin, Yoshua Bengio

Glacier mapping is key to ecological monitoring in the hkh region.

no code implementations • NeurIPS 2020 • Giancarlo Kerg, Bhargav Kanuparthi, Anirudh Goyal Alias Parth Goyal, Kyle Goyette, Yoshua Bengio, Guillaume Lajoie

Attention and self-attention mechanisms, are now central to state-of-the-art deep learning on sequential tasks.

no code implementations • 30 Nov 2020 • Anirudh Goyal, Yoshua Bengio

A fascinating hypothesis is that human and animal intelligence could be explained by a few principles (rather than an encyclopedic list of heuristics).

no code implementations • 25 Nov 2020 • Cheng-Hao Liu, Maksym Korablyov, Stanisław Jastrzębski, Paweł Włodarczyk-Pruszyński, Yoshua Bengio, Marwin H. S. Segler

A natural idea to mitigate this problem is to bias the search process towards more easily synthesizable molecules using a proxy for synthetic accessibility.

2 code implementations • NeurIPS 2021 • Mohammad Pezeshki, Sékou-Oumar Kaba, Yoshua Bengio, Aaron Courville, Doina Precup, Guillaume Lajoie

We identify and formalize a fundamental gradient descent phenomenon resulting in a learning proclivity in over-parameterized neural networks.

Ranked #1 on Out-of-Distribution Generalization on ImageNet-W

no code implementations • 30 Oct 2020 • Prateek Gupta, Tegan Maharaj, Martin Weiss, Nasim Rahaman, Hannah Alsdurf, Abhinav Sharma, Nanor Minoyan, Soren Harnois-Leblanc, Victor Schmidt, Pierre-Luc St. Charles, Tristan Deleu, Andrew Williams, Akshay Patel, Meng Qu, Olexa Bilaniuk, Gaétan Marceau Caron, Pierre Luc Carrier, Satya Ortiz-Gagné, Marc-Andre Rousseau, David Buckeridge, Joumana Ghosn, Yang Zhang, Bernhard Schölkopf, Jian Tang, Irina Rish, Christopher Pal, Joanna Merckx, Eilif B. Muller, Yoshua Bengio

The rapid global spread of COVID-19 has led to an unprecedented demand for effective methods to mitigate the spread of the disease, and various digital contact tracing (DCT) methods have emerged as a component of the solution.

1 code implementation • ICLR 2021 • Yoshua Bengio, Prateek Gupta, Tegan Maharaj, Nasim Rahaman, Martin Weiss, Tristan Deleu, Eilif Muller, Meng Qu, Victor Schmidt, Pierre-Luc St-Charles, Hannah Alsdurf, Olexa Bilanuik, David Buckeridge, Gáetan Marceau Caron, Pierre-Luc Carrier, Joumana Ghosn, Satya Ortiz-Gagne, Chris Pal, Irina Rish, Bernhard Schölkopf, Abhinav Sharma, Jian Tang, Andrew Williams

Predictions are used to provide personalized recommendations to the individual via an app, as well as to send anonymized messages to the individual's contacts, who use this information to better predict their own infectiousness, an approach we call proactive contact tracing (PCT).

no code implementations • 22 Oct 2020 • Rithesh Kumar, Kundan Kumar, Vicki Anand, Yoshua Bengio, Aaron Courville

In this paper, we propose NU-GAN, a new method for resampling audio from lower to higher sampling rates (upsampling).

no code implementations • 20 Oct 2020 • Tristan Sylvain, Francis Dutil, Tess Berthier, Lisa Di Jorio, Margaux Luck, Devon Hjelm, Yoshua Bengio

In hospitals, data are siloed to specific information systems that make the same information available under different modalities such as the different medical imaging exams the patient undergoes (CT scans, MRI, PET, Ultrasound, etc.)

no code implementations • 15 Oct 2020 • Alex Lamb, Anirudh Goyal, Agnieszka Słowik, Michael Mozer, Philippe Beaudoin, Yoshua Bengio

Feed-forward neural networks consist of a sequence of layers, in which each layer performs some processing on the information from the previous layer.

no code implementations • ICLR 2021 • Ossama Ahmed, Frederik Träuble, Anirudh Goyal, Alexander Neitz, Yoshua Bengio, Bernhard Schölkopf, Manuel Wüthrich, Stefan Bauer

To facilitate research addressing this problem, we propose CausalWorld, a benchmark for causal structure and transfer learning in a robotic manipulation environment.

2 code implementations • ICLR 2021 • Meng Qu, Junkun Chen, Louis-Pascal Xhonneux, Yoshua Bengio, Jian Tang

Then in the E-step, we select a set of high-quality rules from all generated rules with both the rule generator and reasoning predictor via posterior inference; and in the M-step, the rule generator is updated with the rules selected in the E-step.

no code implementations • 26 Aug 2020 • Taesup Kim, Sungwoong Kim, Yoshua Bengio

It approximates sparsely connected networks by explicitly defining multiple branches to simultaneously learn representations with different visual concepts or properties.

1 code implementation • 14 Aug 2020 • Lucas Willems, Salem Lahlou, Yoshua Bengio

Recent automatic curriculum learning algorithms, and in particular Teacher-Student algorithms, rely on the notion of learning progress, making the assumption that the good next tasks are the ones on which the learner is making the fastest progress or digress.

no code implementations • 29 Jul 2020 • Yoshua Bengio

We show that a particular form of target propagation, i. e., relying on learned inverses of each layer, which is differential, i. e., where the target is a small perturbation of the forward propagation, gives rise to an update rule which corresponds to an approximate Gauss-Newton gradient-based optimization, without requiring the manipulation or inversion of large matrices.

1 code implementation • 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) 2020 • Md Rifat Arefin, Vincent Michalski, Pierre-Luc St-Charles, Alfredo Kalaitzis, Sookyung Kim, Samira E. Kahou, Yoshua Bengio

High-resolution satellite imagery is critical for various earth observation applications related to environment monitoring, geoscience, forecasting, and land use analysis.

3 code implementations • 24 Jul 2020 • David Yu-Tung Hui, Maxime Chevalier-Boisvert, Dzmitry Bahdanau, Yoshua Bengio

This increases reinforcement learning sample efficiency by up to 3 times and improves imitation learning performance on the hardest level from 77 % to 90. 4 %.

no code implementations • 13 Jul 2020 • Nasim Rahaman, Anirudh Goyal, Muhammad Waleed Gondal, Manuel Wuthrich, Stefan Bauer, Yash Sharma, Yoshua Bengio, Bernhard Schölkopf

Capturing the structure of a data-generating process by means of appropriate inductive biases can help in learning models that generalize well and are robust to changes in the input distribution.

2 code implementations • ICML 2020 • William Fedus, Prajit Ramachandran, Rishabh Agarwal, Yoshua Bengio, Hugo Larochelle, Mark Rowland, Will Dabney

Experience replay is central to off-policy algorithms in deep reinforcement learning (RL), but there remain significant gaps in our understanding.

no code implementations • ACL 2020 • Jacob Russin, Jason Jo, R O{'}Reilly, all, Yoshua Bengio

Standard methods in deep learning for natural language processing fail to capture the compositional structure of human language that allows for systematic generalization outside of the training distribution.

1 code implementation • ICML 2020 • Sarthak Mittal, Alex Lamb, Anirudh Goyal, Vikram Voleti, Murray Shanahan, Guillaume Lajoie, Michael Mozer, Yoshua Bengio

To effectively utilize the wealth of potential top-down information available, and to prevent the cacophony of intermixed signals in a bidirectional architecture, mechanisms are needed to restrict information flow.

no code implementations • 29 Jun 2020 • Anirudh Goyal, Alex Lamb, Phanideep Gampa, Philippe Beaudoin, Sergey Levine, Charles Blundell, Yoshua Bengio, Michael Mozer

To use a video game as an illustration, two enemies of the same type will share schemata but will have separate object files to encode their distinct state (e. g., health, position).

1 code implementation • NeurIPS 2020 • Prateek Gupta, Maxime Gasse, Elias B. Khalil, M. Pawan Kumar, Andrea Lodi, Yoshua Bengio

First, in a more realistic setting where only a CPU is available, is the GNN model still competitive?

no code implementations • 23 Jun 2020 • Matthew Amodio, Rim Assouel, Victor Schmidt, Tristan Sylvain, Smita Krishnaswamy, Yoshua Bengio

Unsupervised image-to-image translation consists of learning a pair of mappings between two domains without known pairwise correspondences between points.

no code implementations • 23 Jun 2020 • Bo Li, Yezhen Wang, Tong Che, Shanghang Zhang, Sicheng Zhao, Pengfei Xu, Wei Zhou, Yoshua Bengio, Kurt Keutzer

In this paper, in order to devise robust DA algorithms, we first systematically analyze the limitations of DM based methods, and then build new benchmarks with more realistic domain shifts to evaluate the well-accepted DM methods.

1 code implementation • 22 Jun 2020 • Yihe Dong, Will Sawin, Yoshua Bengio

Hypergraphs provide a natural representation for many real world datasets.

no code implementations • 16 Jun 2020 • Giancarlo Kerg, Bhargav Kanuparthi, Anirudh Goyal, Kyle Goyette, Yoshua Bengio, Guillaume Lajoie

Attention and self-attention mechanisms, are now central to state-of-the-art deep learning on sequential tasks.

1 code implementation • 12 Jun 2020 • Khurram Javed, Martha White, Yoshua Bengio

One solution for achieving strong generalization is to incorporate causal structures in the models; such structures constrain learning by ignoring correlations that contradict them.

1 code implementation • 6 Jun 2020 • Axel Laborieux, Maxence Ernoult, Benjamin Scellier, Yoshua Bengio, Julie Grollier, Damien Querlioz

In this work, we show that a bias in the gradient estimate of EP, inherent in the use of finite nudging, is responsible for this phenomenon and that cancelling it allows training deep ConvNets by EP.

no code implementations • 2 Jun 2020 • Jack Kendall, Ross Pantone, Kalpana Manickavasagam, Yoshua Bengio, Benjamin Scellier

We introduce a principled method to train end-to-end analog neural networks by stochastic gradient descent.

6 code implementations • 24 May 2020 • Joseph Paul Cohen, Lan Dao, Paul Morrison, Karsten Roth, Yoshua Bengio, Beiyi Shen, Almas Abbasi, Mahsa Hoshmand-Kochi, Marzyeh Ghassemi, Haifang Li, Tim Q Duong

In this study, we present a severity score prediction model for COVID-19 pneumonia for frontal chest X-ray images.

1 code implementation • 18 May 2020 • Rémi Le Priol, Reza Babanezhad Harikandeh, Yoshua Bengio, Simon Lacoste-Julien

When the intervention is on the effect variable, we characterize the relative adaptation speed.

no code implementations • 18 May 2020 • Hannah Alsdurf, Edmond Belliveau, Yoshua Bengio, Tristan Deleu, Prateek Gupta, Daphne Ippolito, Richard Janda, Max Jarvie, Tyler Kolody, Sekoul Krastev, Tegan Maharaj, Robert Obryk, Dan Pilat, Valerie Pisano, Benjamin Prud'homme, Meng Qu, Nasim Rahaman, Irina Rish, Jean-Francois Rousseau, Abhinav Sharma, Brooke Struck, Jian Tang, Martin Weiss, Yun William Yu

Manual contact tracing of Covid-19 cases has significant challenges that limit the ability of public health authorities to minimize community infections.

1 code implementation • ACL 2020 • Wenyu Du, Zhouhan Lin, Yikang Shen, Timothy J. O'Donnell, Yoshua Bengio, Yue Zhang

It is commonly believed that knowledge of syntactic structure should improve language modeling.

no code implementations • 6 May 2020 • Iulian Vlad Serban, Varun Gupta, Ekaterina Kochmar, Dung D. Vu, Robert Belfer, Joelle Pineau, Aaron Courville, Laurent Charlin, Yoshua Bengio

We present Korbit, a large-scale, open-domain, mixed-interface, dialogue-based intelligent tutoring system (ITS).

no code implementations • ICLR 2020 • Nasim Rahaman, Steffen Wolf, Anirudh Goyal, Roman Remme, Yoshua Bengio

We humans have an innate understanding of the asymmetric progression of time, which we use to efficiently and safely perceive and manipulate our environment.

no code implementations • 29 Apr 2020 • Maxence Ernoult, Julie Grollier, Damien Querlioz, Yoshua Bengio, Benjamin Scellier

However, in existing implementations of EP, the learning rule is not local in time: the weight update is performed after the dynamics of the second phase have converged and requires information of the first phase that is no longer available physically.

no code implementations • 29 Apr 2020 • Maxence Ernoult, Julie Grollier, Damien Querlioz, Yoshua Bengio, Benjamin Scellier

On the other hand, the biological plausibility of EP is limited by the fact that its learning rule is not local in time: the synapse update is performed after the dynamics of the second phase have converged and requires information of the first phase that is no longer available physically.

2 code implementations • ECCV 2020 • Timo Milbich, Karsten Roth, Homanga Bharadhwaj, Samarth Sinha, Yoshua Bengio, Björn Ommer, Joseph Paul Cohen

Visual Similarity plays an important role in many computer vision applications.

Ranked #10 on Metric Learning on CUB-200-2011 (using extra training data)

1 code implementation • 26 Apr 2020 • Sai Krishna Gottipati, Boris Sattarov, Sufeng. Niu, Yashaswi Pathak, Hao-Ran Wei, Shengchao Liu, Karam M. J. Thomas, Simon Blackburn, Connor W. Coley, Jian Tang, Sarath Chandar, Yoshua Bengio

Over the last decade, there has been significant progress in the field of machine learning for de novo drug design, particularly in deep generative models.

1 code implementation • ICLR 2020 • Anirudh Goyal, Yoshua Bengio, Matthew Botvinick, Sergey Levine

This is typically the case when we have a standard conditioning input, such as a state observation, and a "privileged" input, which might correspond to the goal of a task, the output of a costly planning algorithm, or communication with another agent.

2 code implementations • EMNLP 2020 • Yonatan Bisk, Ari Holtzman, Jesse Thomason, Jacob Andreas, Yoshua Bengio, Joyce Chai, Mirella Lapata, Angeliki Lazaridou, Jonathan May, Aleksandr Nisnevich, Nicolas Pinto, Joseph Turian

Language understanding research is held back by a failure to relate language to the physical world it describes and to the social interactions it facilitates.

no code implementations • 15 Apr 2020 • Miles Brundage, Shahar Avin, Jasmine Wang, Haydn Belfield, Gretchen Krueger, Gillian Hadfield, Heidy Khlaaf, Jingying Yang, Helen Toner, Ruth Fong, Tegan Maharaj, Pang Wei Koh, Sara Hooker, Jade Leung, Andrew Trask, Emma Bluemke, Jonathan Lebensbold, Cullen O'Keefe, Mark Koren, Théo Ryffel, JB Rubinovitz, Tamay Besiroglu, Federica Carugati, Jack Clark, Peter Eckersley, Sarah de Haas, Maritza Johnson, Ben Laurie, Alex Ingerman, Igor Krawczuk, Amanda Askell, Rosario Cammarota, Andrew Lohn, David Krueger, Charlotte Stix, Peter Henderson, Logan Graham, Carina Prunkl, Bianca Martin, Elizabeth Seger, Noa Zilberman, Seán Ó hÉigeartaigh, Frens Kroeger, Girish Sastry, Rebecca Kagan, Adrian Weller, Brian Tse, Elizabeth Barnes, Allan Dafoe, Paul Scharre, Ariel Herbert-Voss, Martijn Rasser, Shagun Sodhani, Carrick Flynn, Thomas Krendl Gilbert, Lisa Dyer, Saif Khan, Yoshua Bengio, Markus Anderljung

With the recent wave of progress in artificial intelligence (AI) has come a growing awareness of the large-scale impacts of AI systems, and recognition that existing regulations and norms in industry and academia are insufficient to ensure responsible AI development.

Computers and Society

no code implementations • 16 Mar 2020 • Tristan Sylvain, Pengchuan Zhang, Yoshua Bengio, R. Devon Hjelm, Shikhar Sharma

In this paper, we start with the idea that a model must be able to understand individual objects and relationships between objects in order to generate complex scenes well.

Ranked #1 on Layout-to-Image Generation on COCO-Stuff 256x256

3 code implementations • NeurIPS 2020 • Tong Che, Ruixiang Zhang, Jascha Sohl-Dickstein, Hugo Larochelle, Liam Paull, Yuan Cao, Yoshua Bengio

To make that practical, we show that sampling from this modified density can be achieved by sampling in latent space according to an energy-based model induced by the sum of the latent prior log-density and the discriminator output score.

no code implementations • 9 Mar 2020 • Qicheng Lao, Xiang Jiang, Mohammad Havaei, Yoshua Bengio

Learning in non-stationary environments is one of the biggest challenges in machine learning.

14 code implementations • 2 Mar 2020 • Vijay Prakash Dwivedi, Chaitanya K. Joshi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio, Xavier Bresson

In the last few years, graph neural networks (GNNs) have become the standard toolkit for analyzing and learning from data on graphs.

Ranked #1 on Link Prediction on COLLAB

1 code implementation • 28 Feb 2020 • William Fedus, Dibya Ghosh, John D. Martin, Marc G. Bellemare, Yoshua Bengio, Hugo Larochelle

Our study provides a clear empirical link between catastrophic interference and sample efficiency in reinforcement learning.

1 code implementation • 20 Feb 2020 • Devansh Arpit, Huan Wang, Caiming Xiong, Richard Socher, Yoshua Bengio

Disjoint Manifold Labeling: Neural Bayes allows us to formulate an objective which can optimally label samples from disjoint manifolds present in the support of a continuous distribution.