7 citations · 9 across the 7 of their papers we have counts for
8 papers
ASAP: Agent-System Co-Design for Wall-Clock-Centered Auto HPO Research for ML Experiments
Taicheng Guo, Haomin Zhuang, Kehan Guo +4
Hyperparameter Optimization (HPO) is essential for maximizing machine learning model performance, and its core challenge is sample efficiency: finding strong configurations within…
AutoLLMResearch: Training Research Agents for Automating LLM Experiment Configuration - Learning from Cheap, Optimizing Expensive
Taicheng Guo, Nitesh V. Chawla, Olaf Wiest +1
Effectively configuring scalable large language model (LLM) experiments, spanning architecture design, hyperparameter tuning, and beyond, is crucial for advancing LLM research, as…
CrochetBench: Can Vision-Language Models Move from Describing to Doing in Crochet Domain?
Peiyu Li, Xiaobao Huang, Ting Hua +1
While multimodal large language models can describe visual content, their ability to generate executable procedures remains underexplored. CrochetBench presented in this paper eval…
Transaction Categorization with Relational Deep Learning in QuickBooks
Kaiwen Dong, Padmaja Jonnalagedda, Xiang Gao +5
Automatic transaction categorization is crucial for enhancing the customer experience in QuickBooks by providing accurate accounting and bookkeeping. The distinct challenges in thi…
ChemHGNN: A Hierarchical Hypergraph Neural Network for Reaction Virtual Screening and Discovery
Xiaobao Huang, Yihong Ma, Anjali Gurajapu +4
Reaction virtual screening and discovery are fundamental challenges in chemistry and materials science, where traditional graph neural networks (GNNs) struggle to model multi-react…
Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond
Kehan Guo, Yili Shen, Gisela Abigail Gonzalez-Montiel +8
The rapid advent of machine learning (ML) and artificial intelligence (AI) has catalyzed major transformations in chemistry, yet the application of these methods to spectroscopic a…
On the Trustworthiness of Generative Foundation Models: Guideline, Assessment, and Perspective
Yue Huang, Chujie Gao, Siyuan Wu +63
Generative Foundation Models (GenFMs) have emerged as transformative tools. However, their widespread adoption raises critical concerns regarding trustworthiness across dimensions.…
MolX: Enhancing Large Language Models for Molecular Understanding With A Multi-Modal Extension
Khiem Le, Zhichun Guo, Kaiwen Dong +8
Large Language Models (LLMs) with their strong task-handling capabilities have shown remarkable advancements across a spectrum of fields, moving beyond natural language understandi…