10 papers
EEG Benchmarking Needs a Task Specification Layer: NeuroDoc for Rulebook-Guided, Executable Benchmark Construction
Chengxuan Qin, Zhige Chen, Shu Peng +9
Electroencephalography (EEG) foundation models increasingly rely on multi-dataset training and evaluation, yet public EEG datasets still lack a shared task specification layer that…
Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping
Hangming Zhang, Zheng Li, Chenxiang Ma +4
Spiking neural networks (SNNs) offer advantages in computational efficiency via event-driven computing, compared to traditional artificial neural networks (ANNs). While direct trai…
Towards Automated Knowledge Transfer in Evolutionary Multitasking via Large Language Models
Xuebin Lyu, Yuxiao Huang, XueFeng Chen +3
Evolutionary multi-task optimization (EMTO) is an advanced optimization paradigm that improves search efficiency by enabling knowledge transfer across multiple tasks solved in para…
Diversity-Aware Policy Optimization for Large Language Model Reasoning
Jian Yao, Ran Cheng, Xingyu Wu +2
The reasoning capabilities of large language models (LLMs) have advanced rapidly, particularly following the release of DeepSeek R1, which has inspired a surge of research into dat…
HEAR: An EEG Foundation Model with Heterogeneous Electrode Adaptive Representation
Zhige Chen, Chengxuan Qin, Wenlong You +5
Electroencephalography (EEG) is an essential technique for neuroscience research and brain-computer interface (BCI) applications. Recently, large-scale EEG foundation models have b…
Efficient Training of Spiking Neural Networks by Spike-aware Data Pruning
Chenxiang Ma, Xinyi Chen, Yujie Wu +2
Spiking neural networks (SNNs), recognized as an energy-efficient alternative to traditional artificial neural networks (ANNs), have advanced rapidly through the scaling of models…