From the 1 of 6 linked papers with an AI index.
6 papers
Rethinking EEG-Based Disease Diagnosis: Decoupling Instance Representation Learning from Subject-Level Supervision
Zhiyuan Ma, Zeyuan Li, Zhiyi Lu +7
The paper introduces BridgeMIL, a two-stage method that first learns EEG instance representations without using inherited labels and then applies subject-level supervision via a mu…
HRM-Text: Efficient Pretraining Beyond Scaling
Guan Wang, Changling Liu, Chenyu Wang +6
The current pretraining paradigm for large language models relies on massive compute and internet-scale raw text, creating a significant barrier to foundational research. In contra…
An Evolutionary Algorithm with Probabilistic Annealing for Large-scale Sparse Multi-objective Optimization
Shuai Shao, Yuhao Sun, Xing Chen +3
Large-scale sparse multi-objective optimization problems (LSMOPs) are prevalent in real-world applications, where optimal solutions typically contain only a few nonzero variables,…
Signal-Adaptive Trust Regions for Gradient-Free Optimization of Recurrent Spiking Neural Networks
Jinhao Li, Yuhao Sun, Zhiyuan Ma +5
Recurrent spiking neural networks (RSNNs) are a promising substrate for energy-efficient control policies, but training them for high-dimensional, long-horizon reinforcement learni…
Hierarchical Reasoning Model
Guan Wang, Jin Li, Yuhao Sun +6
Reasoning, the process of devising and executing complex goal-oriented action sequences, remains a critical challenge in AI. Current large language models (LLMs) primarily employ C…
Capturing Aperiodic Temporal Dynamics of EEG Signals through Stochastic Fluctuation Modeling
Yuhao Sun, Zhiyuan Ma, Xinke Shen +3
Electrophysiological brain signals, such as electroencephalography (EEG), exhibit both periodic and aperiodic components, with the latter often modeled as 1/f noise and considered…