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Sherjil Ozair

Alumni

Publications

The Journey is the Reward: Unsupervised Learning of Influential Trajectories
Jonathan Binas
Sherjil Ozair
Yoshua Bengio
Unsupervised exploration and representation learning become increasingly important when learning in diverse and sparse environments. The inf… (voir plus)ormation-theoretic principle of empowerment formalizes an unsupervised exploration objective through an agent trying to maximize its influence on the future states of its environment. Previous approaches carry certain limitations in that they either do not employ closed-loop feedback or do not have an internal state. As a consequence, a privileged final state is taken as an influence measure, rather than the full trajectory. We provide a model-free method which takes into account the whole trajectory while still offering the benefits of option-based approaches. We successfully apply our approach to settings with large action spaces, where discovery of meaningful action sequences is particularly difficult.
2019-05-21
ArXiv (prépublication)
arxiv.org
arxiv.org
Unsupervised State Representation Learning in Atari
Ankesh Anand
Evan Racah
Sherjil Ozair
Yoshua Bengio
Marc-Alexandre Côté
R Devon Hjelm
State representation learning, or the ability to capture latent generative factors of an environment, is crucial for building intelligent ag… (voir plus)ents that can perform a wide variety of tasks. Learning such representations without supervision from rewards is a challenging open problem. We introduce a method that learns state representations by maximizing mutual information across spatially and temporally distinct features of a neural encoder of the observations. We also introduce a new benchmark based on Atari 2600 games where we evaluate representations based on how well they capture the ground truth state variables. We believe this new framework for evaluating representation learning models will be crucial for future representation learning research. Finally, we compare our technique with other state-of-the-art generative and contrastive representation learning methods. The code associated with this work is available at this https URL
2018-12-31
Advances in Neural Information Processing Systems 32 (NeurIPS 2019) (publié)
arxiv.org
arxiv.org
Wasserstein Dependency Measure for Representation Learning
Sherjil Ozair
Corey Lynch
Yoshua Bengio
Aäron van den Oord
Sergey Levine
Pierre Sermanet
Mutual information maximization has emerged as a powerful learning objective for unsupervised representation learning obtaining state-of-the… (voir plus)-art performance in applications such as object recognition, speech recognition, and reinforcement learning. However, such approaches are fundamentally limited since a tight lower bound of mutual information requires sample size exponential in the mutual information. This limits the applicability of these approaches for prediction tasks with high mutual information, such as in video understanding or reinforcement learning. In these settings, such techniques are prone to overfit, both in theory and in practice, and capture only a few of the relevant factors of variation. This leads to incomplete representations that are not optimal for downstream tasks. In this work, we empirically demonstrate that mutual information-based representation learning approaches do fail to learn complete representations on a number of designed and real-world tasks. To mitigate these problems we introduce the Wasserstein dependency measure, which learns more complete representations by using the Wasserstein distance instead of the KL divergence in the mutual information estimator. We show that a practical approximation to this theoretically motivated solution, constructed using Lipschitz constraint techniques from the GAN literature, achieves substantially improved results on tasks where incomplete representations are a major challenge.
2018-12-31
Advances in Neural Information Processing Systems 32 (NeurIPS 2019) (publié)
arxiv.org
arxiv.org
Mutual Information Neural Estimation
Mohamed Ishmael Belghazi
Aristide Baratin
Sai Rajeswar
Sherjil Ozair
Yoshua Bengio
R Devon Hjelm
Aaron Courville
We argue that the estimation of mutual information between high dimensional continuous random variables can be achieved by gradient descent … (voir plus)over neural networks. We present a Mutual Information Neural Estimator (MINE) that is linearly scalable in dimensionality as well as in sample size, trainable through back-prop, and strongly consistent. We present a handful of applications on which MINE can be used to minimize or maximize mutual information. We apply MINE to improve adversarially trained generative models. We also use MINE to implement Information Bottleneck, applying it to supervised classification; our results demonstrate substantial improvement in flexibility and performance in these settings.
2018-07-02
Proceedings of the 35th International Conference on Machine Learning (publié)
doi.org
proceedings.mlr.press
Learning Generative Models with Locally Disentangled Latent Factors
Brady Neal
Alex Lamb
Sherjil Ozair
Devon Hjelm
Aaron Courville
Yoshua Bengio
Ioannis Mitliagkas
2018-02-14
(publié)
openreview.net
openreview.net
Generative Adversarial Nets
Moez Krichen
Ian J. Goodfellow
Jean Pouget-Abadie
Mehdi Mirza
Bing Xu
David Warde-Farley
Sherjil Ozair
Aaron Courville
Yoshua Bengio
Generative Adversarial Networks (GANs) are very popular frameworks for generating high-quality data, and are immensely used in both the acad… (voir plus)emia and industry in many domains. Arguably, their most substantial impact has been in the area of computer vision, where they achieve state-of-the-art image generation. This chapter gives an introduction to GANs, by discussing their principle mechanism and presenting some of their inherent problems during training and evaluation. We focus on these three issues: (1) mode collapse, (2) vanishing gradients, and (3) generation of low-quality images. We then list some architecture-variant and loss-variant GANs that remedy the above challenges. Lastly, we present two utilization examples of GANs for real-world applications: Data augmentation and face images generation.
2014-06-09
Communications of the ACM (publié)
doi.org
arxiv.org