81 citations
- Université de MontréalCA11 papers
- McGill UniversityCA7 papers
- Google DeepMind (United Kingdom)GB3 papers
- Brain (Germany)DE2 papers
- Flatiron Health (United States)US2 papers
- Flatiron Institute2 papers
- Google (United States)US2 papers
- Peking UniversityCN2 papers
- Samsung (South Korea)KR2 papers
- Tencent (China)CN2 papers
- Ahlia UniversityBH1 paper
- Brown UniversityUS1 paper
11 papers · 1 filter
A Highly Adaptive Acoustic Model for Accurate Multi-Dialect Speech Recognition
Sanghyun Yoo, Inchul Song, Yoshua Bengio
Despite the success of deep learning in speech recognition, multi-dialect speech recognition remains a difficult problem. Although dialect-specific acoustic models are known to per…
Equivariant Networks for Pixelized Spheres
Mehran Shakerinava, Siamak Ravanbakhsh
Pixelizations of Platonic solids such as the cube and icosahedron have been widely used to represent spherical data, from climate records to Cosmic Microwave Background maps. Plato…
Decoupled Greedy Learning of CNNs for Synchronous and Asynchronous Distributed Learning
Eugene Belilovsky, Louis Leconte, Lucas Caccia +2
A commonly cited inefficiency of neural network training using back-propagation is the update locking problem: each layer must wait for the signal to propagate through the full net…
Randomized Exploration for Reinforcement Learning with General Value Function Approximation
Haque Ishfaq, Qiwen Cui, Viet Nguyen +5
We propose a model-free reinforcement learning algorithm inspired by the popular randomized least squares value iteration (RLSVI) algorithm as well as the optimism principle. Unlik…
Variance Penalized On-Policy and Off-Policy Actor-Critic
Arushi Jain, Gandharv Patil, Ayush Jain +2
Reinforcement learning algorithms are typically geared towards optimizing the expected return of an agent. However, in many practical applications, low variance in the return is de…
Revisiting Fundamentals of Experience Replay
William Fedus, Prajit Ramachandran, Rishabh Agarwal +4
Experience replay is central to off-policy algorithms in deep reinforcement learning (RL), but there remain significant gaps in our understanding. We therefore present a systematic…