1.8k citations · 3.3k across the 8 of their papers we have counts for
Showing cs.LGShow all
3 papers · 1 filter
cs.LG2018★ 11 cited
Learning and Planning with a Semantic Model
Yi Wu, Yuxin Wu, Aviv Tamar +3
Building deep reinforcement learning agents that can generalize and adapt to unseen environments remains a fundamental challenge for AI. This paper describes progresses on this cha…
cs.LG2018★ 249 cited
Building Generalizable Agents with a Realistic and Rich 3D Environment
Yi Wu, Yuxin Wu, Georgia Gkioxari +1
Teaching an agent to navigate in an unseen 3D environment is a challenging task, even in the event of simulated environments. To generalize to unseen environments, an agent needs t…
cs.LG2016★ 64 cited
Effective Quantization Methods for Recurrent Neural Networks
Qinyao He, He Wen, Shuchang Zhou +4
Reducing bit-widths of weights, activations, and gradients of a Neural Network can shrink its storage size and memory usage, and also allow for faster training and inference by exp…