173 citations · 197 across the 7 of their papers we have counts for
7 papers
Efficient Action Robust Reinforcement Learning with Probabilistic Policy Execution Uncertainty
Guanlin Liu, Zhihan Zhou, Han Liu +1
Robust reinforcement learning (RL) aims to find a policy that optimizes the worst-case performance in the face of uncertainties. In this paper, we focus on action robust RL with th…
DNABERT-2: Efficient Foundation Model and Benchmark For Multi-Species Genome
Zhihan Zhou, Yanrong Ji, Weijian Li +3
Decoding the linguistic intricacies of the genome is a crucial problem in biology, and pre-trained foundational models such as DNABERT and Nucleotide Transformer have made signific…
KGML-xDTD: A Knowledge Graph-based Machine Learning Framework for Drug Treatment Prediction and Mechanism Description
Chunyu Ma, Zhihan Zhou, Han Liu +1
Background: Computational drug repurposing is a cost- and time-efficient approach that aims to identify new therapeutic targets or diseases (indications) of existing drugs/compound…
Learning Dialogue Representations from Consecutive Utterances
Zhihan Zhou, Dejiao Zhang, Wei Xiao +4
Learning high-quality dialogue representations is essential for solving a variety of dialogue-oriented tasks, especially considering that dialogue systems often suffer from data sc…
Trade the Event: Corporate Events Detection for News-Based Event-Driven Trading
Zhihan Zhou, Liqian Ma, Han Liu
In this paper, we introduce an event-driven trading strategy that predicts stock movements by detecting corporate events from news articles. Unlike existing models that utilize tex…
Few-shot Slot Tagging with Collapsed Dependency Transfer and Label-enhanced Task-adaptive Projection Network
Yutai Hou, Wanxiang Che, Yongkui Lai +4
In this paper, we explore the slot tagging with only a few labeled support sentences (a.k.a. few-shot). Few-shot slot tagging faces a unique challenge compared to the other few-sho…
Few-Shot Sequence Labeling with Label Dependency Transfer and Pair-wise Embedding
Yutai Hou, Zhihan Zhou, Yijia Liu +4
While few-shot classification has been widely explored with similarity based methods, few-shot sequence labeling poses a unique challenge as it also calls for modeling the label de…