1 citations · 2 across the 4 of their papers we have counts for
4 papers
TextBFGS: A Case-Based Reasoning Approach to Code Optimization via Error-Operator Retrieval
Zizheng Zhang, Yuyang Liao, Chen Chen +8
Iterative code generation with Large Language Models (LLMs) can be viewed as an optimization process guided by textual feedback. However, existing LLM self-correction methods predo…
MedFeat: Model-Aware and Explainability-Driven Feature Engineering with LLMs for Clinical Tabular Prediction
Zizheng Zhang, Yiming Li, Justin Xu +6
In clinical tabular prediction, classical machine learning models with feature engineering often outperform neural methods. LLMs are increasingly used to automate this process, act…
A Machine Learning and Explainable AI Framework Tailored for Unbalanced Experimental Catalyst Discovery
Parastoo Semnani, Mihail Bogojeski, Florian Bley +7
The successful application of machine learning (ML) in catalyst design relies on high-quality and diverse data to ensure effective generalization to novel compositions, thereby aid…
Noise-Aware Speech Separation with Contrastive Learning
Zizheng Zhang, Chen Chen, Hsin-Hung Chen +3
Recently, speech separation (SS) task has achieved remarkable progress driven by deep learning technique. However, it is still challenging to separate target speech from noisy mixt…