58 citations · 72 across the 5 of their papers we have counts for
9 papers
Meta-Adversarial Inverse Reinforcement Learning for Decision-making Tasks
Pin Wang, Hanhan Li, Ching-Yao Chan
Learning from demonstrations has made great progress over the past few years. However, it is generally data hungry and task specific. In other words, it requires a large amount of…
Unsupervised Monocular Depth Learning in Dynamic Scenes
Hanhan Li, Ariel Gordon, Hang Zhao +2
We present a method for jointly training the estimation of depth, ego-motion, and a dense 3D translation field of objects relative to the scene, with monocular photometric consiste…
Fine-Grained Stochastic Architecture Search
Shraman Ray Chaudhuri, Elad Eban, Hanhan Li +2
State-of-the-art deep networks are often too large to deploy on mobile devices and embedded systems. Mobile neural architecture search (NAS) methods automate the design of small mo…
Adversarially Robust Frame Sampling with Bounded Irregularities
Hanhan Li, Pin Wang
In recent years, video analysis tools for automatically extracting meaningful information from videos are widely studied and deployed. Because most of them use deep neural networks…
Quadratic Q-network for Learning Continuous Control for Autonomous Vehicles
Pin Wang, Hanhan Li, Ching-Yao Chan
Reinforcement Learning algorithms have recently been proposed to learn time-sequential control policies in the field of autonomous driving. Direct applications of Reinforcement Lea…
Continuous Control for Automated Lane Change Behavior Based on Deep Deterministic Policy Gradient Algorithm
Pin Wang, Hanhan Li, Ching-Yao Chan
Lane change is a challenging task which requires delicate actions to ensure safety and comfort. Some recent studies have attempted to solve the lane-change control problem with Rei…