5 citations · 6 across the 4 of their papers we have counts for
5 papers
EEGAgentBench: Benchmarking LLM Agents on Short- and Long-Horizon EEG Analysis
Huyu Wu, Weining Weng, Yuchen Liu +2
Electroencephalography (EEG) analysis is evolving from short-segment classification toward long-horizon interpretation that demands iterative evidence accumulation, multi-step reas…
A Study on Thinking Patterns of Large Reasoning Models in Code Generation
Kevin Halim, Sin G. Teo, Ruitao Feng +4
Currently, many large language models (LLMs) are utilized for software engineering tasks such as code generation. The emergence of more advanced models known as large reasoning mod…
A Survey on Unlearnable Data
Jiahao Li, Yiqiang Chen, Yunbing Xing +2
Unlearnable data (ULD) has emerged as an innovative defense technique to prevent machine learning models from learning meaningful patterns from specific data, thus protecting data…
Ten Challenging Problems in Federated Foundation Models
Tao Fan, Hanlin Gu, Xuemei Cao +30
Federated Foundation Models (FedFMs) represent a distributed learning paradigm that fuses general competences of foundation models as well as privacy-preserving capabilities of fed…
Self-supervised Learning for Electroencephalogram: A Systematic Survey
Weining Weng, Yang Gu, Shuai Guo +4
Electroencephalogram (EEG) is a non-invasive technique to record bioelectrical signals. Integrating supervised deep learning techniques with EEG signals has recently facilitated au…