activity
20172022
most citedRevisiting the Master-Slave Architecture in Multi-Agent Deep Reinforcement Learning

46 citations · 65 across the 3 of their papers we have counts for

collaborators

7 papers

cs.CV20221 cited

HARP: Autoregressive Latent Video Prediction with High-Fidelity Image Generator

Younggyo Seo, Kimin Lee, Fangchen Liu +2

Video prediction is an important yet challenging problem; burdened with the tasks of generating future frames and learning environment dynamics. Recently, autoregressive latent vid…

cs.CV2020

SAPIEN: A SimulAted Part-based Interactive ENvironment

Fanbo Xiang, Yuzhe Qin, Kaichun Mo +11

Building home assistant robots has long been a pursuit for vision and robotics researchers. To achieve this task, a simulated environment with physically realistic simulation, suff…

cs.LG201918 cited

State Alignment-based Imitation Learning

Fangchen Liu, Zhan Ling, Tongzhou Mu +1

Consider an imitation learning problem that the imitator and the expert have different dynamics models. Most of the current imitation learning methods fail because they focus on im…

cs.LG2019

Mapping State Space using Landmarks for Universal Goal Reaching

Zhiao Huang, Fangchen Liu, Hao Su

An agent that has well understood the environment should be able to apply its skills for any given goals, leading to the fundamental problem of learning the Universal Value Functio…

cs.CV2018

Adversarial Defense by Stratified Convolutional Sparse Coding

Bo Sun, Nian-hsuan Tsai, Fangchen Liu +2

We propose an adversarial defense method that achieves state-of-the-art performance among attack-agnostic adversarial defense methods while also maintaining robustness to input res…

cs.CV2018

BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning

Fisher Yu, Haofeng Chen, Xin Wang +5

Datasets drive vision progress, yet existing driving datasets are impoverished in terms of visual content and supported tasks to study multitask learning for autonomous driving. Re…