111 citations · 274 across the 8 of their papers we have counts for
11 papers
LLM: Multi-view Molecular Representation Learning with Large Language Models
Jiaxin Ju, Yizhen Zheng, Huan Yee Koh +2
Accurate molecular property prediction is a critical challenge with wide-ranging applications in chemistry, materials science, and drug discovery. Molecular representation methods,…
ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models
Zhuo Chen, Yizhen Zheng, Huan Yee Koh +4
Molecular Relational Learning (MRL) aims to understand interactions between molecular pairs, playing a critical role in advancing biochemical research. With the recent development…
Collaborative Expert LLMs Guided Multi-Objective Molecular Optimization
Jiajun Yu, Yizhen Zheng, Huan Yee Koh +3
Molecular optimization is a crucial yet complex and time-intensive process that often acts as a bottleneck for drug development. Traditional methods rely heavily on trial and error…
Large Language Models in Drug Discovery and Development: From Disease Mechanisms to Clinical Trials
Yizhen Zheng, Huan Yee Koh, Maddie Yang +5
The integration of Large Language Models (LLMs) into the drug discovery and development field marks a significant paradigm shift, offering novel methodologies for understanding dis…
Graph Spatiotemporal Process for Multivariate Time Series Anomaly Detection with Missing Values
Yu Zheng, Huan Yee Koh, Ming Jin +6
The detection of anomalies in multivariate time series data is crucial for various practical applications, including smart power grids, traffic flow forecasting, and industrial pro…
Large Language Models for Scientific Synthesis, Inference and Explanation
Yizhen Zheng, Huan Yee Koh, Jiaxin Ju +4
Large language models are a form of artificial intelligence systems whose primary knowledge consists of the statistical patterns, semantic relationships, and syntactical structures…
Correlation-aware Spatial-Temporal Graph Learning for Multivariate Time-series Anomaly Detection
Yu Zheng, Huan Yee Koh, Ming Jin +5
Multivariate time-series anomaly detection is critically important in many applications, including retail, transportation, power grid, and water treatment plants. Existing approach…
A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection
Ming Jin, Huan Yee Koh, Qingsong Wen +5
Time series are the primary data type used to record dynamic system measurements and generated in great volume by both physical sensors and online processes (virtual sensors). Time…
How Far are We from Robust Long Abstractive Summarization?
Huan Yee Koh, Jiaxin Ju, He Zhang +2
Abstractive summarization has made tremendous progress in recent years. In this work, we perform fine-grained human annotations to evaluate long document abstractive summarization…
An Empirical Survey on Long Document Summarization: Datasets, Models and Metrics
Huan Yee Koh, Jiaxin Ju, Ming Liu +1
Long documents such as academic articles and business reports have been the standard format to detail out important issues and complicated subjects that require extra attention. An…
Leveraging Information Bottleneck for Scientific Document Summarization
Jiaxin Ju, Ming Liu, Huan Yee Koh +3
This paper presents an unsupervised extractive approach to summarize scientific long documents based on the Information Bottleneck principle. Inspired by previous work which uses t…