118 citations · 360 across the 17 of their papers we have counts for
17 papers
PaCo: Parameter-Compositional Multi-Task Reinforcement Learning
Lingfeng Sun, Haichao Zhang, Wei Xu +1
The purpose of multi-task reinforcement learning (MTRL) is to train a single policy that can be applied to a set of different tasks. Sharing parameters allows us to take advantage…
Generative Planning for Temporally Coordinated Exploration in Reinforcement Learning
Haichao Zhang, Wei Xu, Haonan Yu
Standard model-free reinforcement learning algorithms optimize a policy that generates the action to be taken in the current time step in order to maximize expected future return.…
Fine-Grained Trajectory-based Travel Time Estimation for Multi-city Scenarios Based on Deep Meta-Learning
Chenxing Wang, Fang Zhao, Haichao Zhang +3
Travel Time Estimation (TTE) is indispensable in intelligent transportation system (ITS). It is significant to achieve the fine-grained Trajectory-based Travel Time Estimation (TTT…
Towards Safe Reinforcement Learning with a Safety Editor Policy
Haonan Yu, Wei Xu, Haichao Zhang
We consider the safe reinforcement learning (RL) problem of maximizing utility with extremely low constraint violation rates. Assuming no prior knowledge or pre-training of the env…
TAAC: Temporally Abstract Actor-Critic for Continuous Control
Haonan Yu, Wei Xu, Haichao Zhang
We present temporally abstract actor-critic (TAAC), a simple but effective off-policy RL algorithm that incorporates closed-loop temporal abstraction into the actor-critic framewor…
MetaView: Few-shot Active Object Recognition
Wei Wei, Haonan Yu, Haichao Zhang +2
In robot sensing scenarios, instead of passively utilizing human captured views, an agent should be able to actively choose informative viewpoints of a 3D object as discriminative…
Joint Adversarial Training: Incorporating both Spatial and Pixel Attacks
Haichao Zhang, Jianyu Wang
Conventional adversarial training methods using attacks that manipulate the pixel value directly and individually, leading to models that are less robust in face of spatial transfo…
Towards Adversarially Robust Object Detection
Haichao Zhang, Jianyu Wang
Object detection is an important vision task and has emerged as an indispensable component in many vision system, rendering its robustness as an increasingly important performance…
Defense Against Adversarial Attacks Using Feature Scattering-based Adversarial Training
Haichao Zhang, Jianyu Wang
We introduce a feature scattering-based adversarial training approach for improving model robustness against adversarial attacks. Conventional adversarial training approaches lever…
Improving Sequence-to-Sequence Learning via Optimal Transport
Liqun Chen, Yizhe Zhang, Ruiyi Zhang +7
Sequence-to-sequence models are commonly trained via maximum likelihood estimation (MLE). However, standard MLE training considers a word-level objective, predicting the next word…
Bilateral Adversarial Training: Towards Fast Training of More Robust Models Against Adversarial Attacks
Jianyu Wang, Haichao Zhang
In this paper, we study fast training of adversarially robust models. From the analyses of the state-of-the-art defense method, i.e., the multi-step adversarial training, we hypoth…
Interactive Language Acquisition with One-shot Visual Concept Learning through a Conversational Game
Haichao Zhang, Haonan Yu, Wei Xu
Building intelligent agents that can communicate with and learn from humans in natural language is of great value. Supervised language learning is limited by the ability of capturi…
Enhance Visual Recognition under Adverse Conditions via Deep Networks
Ding Liu, Bowen Cheng, Zhangyang Wang +2
Visual recognition under adverse conditions is a very important and challenging problem of high practical value, due to the ubiquitous existence of quality distortions during image…
Listen, Interact and Talk: Learning to Speak via Interaction
Haichao Zhang, Haonan Yu, Wei Xu
One of the long-term goals of artificial intelligence is to build an agent that can communicate intelligently with human in natural language. Most existing work on natural language…
Non-Uniform Blind Deblurring with a Spatially-Adaptive Sparse Prior
Haichao Zhang, David Wipf
Typical blur from camera shake often deviates from the standard uniform convolutional script, in part because of problematic rotations which create greater blurring away from some…
Revisiting Bayesian Blind Deconvolution
David Wipf, Haichao Zhang
Blind deconvolution involves the estimation of a sharp signal or image given only a blurry observation. Because this problem is fundamentally ill-posed, strong priors on both the s…
Image Super-Resolution via Sparse Bayesian Modeling of Natural Images
Haichao Zhang, David Wipf, Yanning Zhang
Image super-resolution (SR) is one of the long-standing and active topics in image processing community. A large body of works for image super resolution formulate the problem with…