133 citations · 378 across the 13 of their papers we have counts for
13 papers
Agentopia: Long-Term Life Simulation and Learning in Agent Societies
Xintao Wang, Sirui Zheng, Hongqiu Wu +10
Humans learn from social life. Simulating this process with LLM-powered agents represents a promising research direction, raising a natural question: whether LLMs can learn from su…
DGRec: Graph Neural Network for Recommendation with Diversified Embedding Generation
Liangwei Yang, Shengjie Wang, Yunzhe Tao +4
Graph Neural Network (GNN) based recommender systems have been attracting more and more attention in recent years due to their excellent performance in accuracy. Representing user-…
A Template-guided Hybrid Pointer Network for Knowledge-basedTask-oriented Dialogue Systems
Dingmin Wang, Ziyao Chen, Wanwei He +3
Most existing neural network based task-oriented dialogue systems follow encoder-decoder paradigm, where the decoder purely depends on the source texts to generate a sequence of wo…
REPAINT: Knowledge Transfer in Deep Reinforcement Learning
Yunzhe Tao, Sahika Genc, Jonathan Chung +2
Accelerating learning processes for complex tasks by leveraging previously learned tasks has been one of the most challenging problems in reinforcement learning, especially when th…
Zero-Shot Reinforcement Learning with Deep Attention Convolutional Neural Networks
Sahika Genc, Sunil Mallya, Sravan Bodapati +2
Simulation-to-simulation and simulation-to-real world transfer of neural network models have been a difficult problem. To close the reality gap, prior methods to simulation-to-real…
DeepRacer: Educational Autonomous Racing Platform for Experimentation with Sim2Real Reinforcement Learning
Bharathan Balaji, Sunil Mallya, Sahika Genc +10
DeepRacer is a platform for end-to-end experimentation with RL and can be used to systematically investigate the key challenges in developing intelligent control systems. Using the…
FineText: Text Classification via Attention-based Language Model Fine-tuning
Yunzhe Tao, Saurabh Gupta, Satyapriya Krishna +3
Training deep neural networks from scratch on natural language processing (NLP) tasks requires significant amount of manually labeled text corpus and substantial time to converge,…
Using a machine learning approach to determine the space group of a structure from the atomic pair distribution function (PDF)
Chia-Hao Liu, Yunzhe Tao, Daniel Hsu +2
We present a method for predicting the space group of a structure given a calculated or measured atomic pair distribution function (PDF) from that structure. The method utilizes ma…
Stochastic Training of Residual Networks: a Differential Equation Viewpoint
Qi Sun, Yunzhe Tao, Qiang Du
During the last few years, significant attention has been paid to the stochastic training of artificial neural networks, which is known as an effective regularization approach that…
Explaining Deep Learning Models - A Bayesian Non-parametric Approach
Wenbo Guo, Sui Huang, Yunzhe Tao +2
Understanding and interpreting how machine learning (ML) models make decisions have been a big challenge. While recent research has proposed various technical approaches to provide…
Hierarchical Attention-Based Recurrent Highway Networks for Time Series Prediction
Yunzhe Tao, Lin Ma, Weizhong Zhang +3
Time series prediction has been studied in a variety of domains. However, it is still challenging to predict future series given historical observations and past exogenous data. Ex…
Nonlocal Neural Networks, Nonlocal Diffusion and Nonlocal Modeling
Yunzhe Tao, Qi Sun, Qiang Du +1
Nonlocal neural networks have been proposed and shown to be effective in several computer vision tasks, where the nonlocal operations can directly capture long-range dependencies i…
A Reinforced Topic-Aware Convolutional Sequence-to-Sequence Model for Abstractive Text Summarization
Li Wang, Junlin Yao, Yunzhe Tao +3
In this paper, we propose a deep learning approach to tackle the automatic summarization tasks by incorporating topic information into the convolutional sequence-to-sequence (ConvS…