most citedInteraction-Aware Trajectory Prediction and Planning for Autonomous Vehicles in Forced Merge Scenarios

3 citations · 5 across the 2 of their papers we have counts for

collaborators

8 papers

cs.LG2023

Dream to Adapt: Meta Reinforcement Learning by Latent Context Imagination and MDP Imagination

Lu Wen, Songan Zhang, H. Eric Tseng +1

Meta reinforcement learning (Meta RL) has been amply explored to quickly learn an unseen task by transferring previously learned knowledge from similar tasks. However, most state-o…

cs.AI2023

Decision-Making for Autonomous Vehicles with Interaction-Aware Behavioral Prediction and Social-Attention Neural Network

Xiao Li, Kaiwen Liu, H. Eric Tseng +2

Autonomous vehicles need to accomplish their tasks while interacting with human drivers in traffic. It is thus crucial to equip autonomous vehicles with artificial reasoning to bet…

quant-ph2023

Quantum Federated Learning With Quantum Networks

Tyler Wang, Huan-Hsin Tseng, Shinjae Yoo

A major concern of deep learning models is the large amount of data that is required to build and train them, much of which is reliant on sensitive and personally identifiable info…

quant-ph20231 cited

Federated Quantum Machine Learning with Differential Privacy

Rod Rofougaran, Shinjae Yoo, Huan-Hsin Tseng +1

The preservation of privacy is a critical concern in the implementation of artificial intelligence on sensitive training data. There are several techniques to preserve data privacy…

cs.AI2023

Interaction-Aware Decision-Making for Autonomous Vehicles in Forced Merging Scenario Leveraging Social Psychology Factors

Xiao Li, Kaiwen Liu, H. Eric Tseng +2

Understanding the intention of vehicles in the surrounding traffic is crucial for an autonomous vehicle to successfully accomplish its driving tasks in complex traffic scenarios su…

cs.CV20232 cited

INSURE: An Information Theory Inspired Disentanglement and Purification Model for Domain Generalization

Xi Yu, Huan-Hsin Tseng, Shinjae Yoo +2

Domain Generalization (DG) aims to learn a generalizable model on the unseen target domain by only training on the multiple observed source domains. Although a variety of DG method…