7 citations · 9 across the 4 of their papers we have counts for
5 papers · 1 filter
Learning from Visual Observation via Offline Pretrained State-to-Go Transformer
Bohan Zhou, Ke Li, Jiechuan Jiang +1
Learning from visual observation (LfVO), aiming at recovering policies from only visual observation data, is promising yet a challenging problem. Existing LfVO approaches either on…
Better Knowledge Retention through Metric Learning
Ke Li, Shichong Peng, Kailas Vodrahalli +1
In continual learning, new categories may be introduced over time, and an ideal learning system should perform well on both the original categories and the new categories. While de…
Trajectory Normalized Gradients for Distributed Optimization
Jianqiao Wangni, Ke Li, Jianbo Shi +1
Recently, researchers proposed various low-precision gradient compression, for efficient communication in large-scale distributed optimization. Based on these work, we try to reduc…
On the Implicit Assumptions of GANs
Ke Li, Jitendra Malik
Generative adversarial nets (GANs) have generated a lot of excitement. Despite their popularity, they exhibit a number of well-documented issues in practice, which apparently contr…
Learning to Optimize
Ke Li, Jitendra Malik
Algorithm design is a laborious process and often requires many iterations of ideation and validation. In this paper, we explore automating algorithm design and present a method to…