34 citations · 59 across the 9 of their papers we have counts for
6 papers · 1 filter
Prompt2Model: Generating Deployable Models from Natural Language Instructions
Vijay Viswanathan, Chenyang Zhao, Amanda Bertsch +2
Large language models (LLMs) enable system builders today to create competent NLP systems through prompting, where they only need to describe the task in natural language and provi…
Enabling Intelligent Interactions between an Agent and an LLM: A Reinforcement Learning Approach
Bin Hu, Chenyang Zhao, Pu Zhang +4
Large language models (LLMs) encode a vast amount of world knowledge acquired from massive text datasets. Recent studies have demonstrated that LLMs can assist an embodied agent in…
CLIP3Dstyler: Language Guided 3D Arbitrary Neural Style Transfer
Ming Gao, YanWu Xu, Yang Zhao +3
In this paper, we propose a novel language-guided 3D arbitrary neural style transfer method (CLIP3Dstyler). We aim at stylizing any 3D scene with an arbitrary style from a text des…
On Context Distribution Shift in Task Representation Learning for Offline Meta RL
Chenyang Zhao, Zihao Zhou, Bin Liu
Offline Meta Reinforcement Learning (OMRL) aims to learn transferable knowledge from offline datasets to enhance the learning process for new target tasks. Context-based Reinforcem…
Replicating Complex Dialogue Policy of Humans via Offline Imitation Learning with Supervised Regularization
Zhoujian Sun, Chenyang Zhao, Zhengxing Huang +1
Policy learning (PL) is a module of a task-oriented dialogue system that trains an agent to make actions in each dialogue turn. Imitating human action is a fundamental problem of P…
ODAM: Gradient-based instance-specific visual explanations for object detection
Chenyang Zhao, Antoni B. Chan
We propose the gradient-weighted Object Detector Activation Maps (ODAM), a visualized explanation technique for interpreting the predictions of object detectors. Utilizing the grad…