152 citations · 182 across the 8 of their papers we have counts for
9 papers
Generative Adversarial Networks
Gilad Cohen, Raja Giryes
Generative Adversarial Networks (GANs) are very popular frameworks for generating high-quality data, and are immensely used in both the academia and industry in many domains. Argua…
Procedural Generalization by Planning with Self-Supervised World Models
Ankesh Anand, Jacob Walker, Yazhe Li +5
One of the key promises of model-based reinforcement learning is the ability to generalize using an internal model of the world to make predictions in novel environments and tasks.…
Vector Quantized Models for Planning
Sherjil Ozair, Yazhe Li, Ali Razavi +3
Recent developments in the field of model-based RL have proven successful in a range of environments, especially ones where planning is essential. However, such successes have been…
Pretrained Encoders are All You Need
Mina Khan, P Srivatsa, Advait Rane +4
Data-efficiency and generalization are key challenges in deep learning and deep reinforcement learning as many models are trained on large-scale, domain-specific, and expensive-to-…
SketchTransfer: A Challenging New Task for Exploring Detail-Invariance and the Abstractions Learned by Deep Networks
Alex Lamb, Sherjil Ozair, Vikas Verma +1
Deep networks have achieved excellent results in perceptual tasks, yet their ability to generalize to variations not seen during training has come under increasing scrutiny. In thi…
Unsupervised State Representation Learning in Atari
Ankesh Anand, Evan Racah, Sherjil Ozair +3
State representation learning, or the ability to capture latent generative factors of an environment, is crucial for building intelligent agents that can perform a wide variety of…