27 citations · 30 across the 10 of their papers we have counts for
7 papers · 1 filter
Praxist: From Experimental Artifacts to Solution Lineages
Jin Li, Ahmed Murtadha, Zhiyu Wang +14
Autonomous R&D agents now write, run, and improve executable artifacts under automated evaluation-but largely as laboratory instruments: shown on curated benchmarks, with gains tha…
Riemannian Attention Mechanisms for Transformers: A Theoretical Framework and Architecture Design
Sen Song
All Transformer-based large language models compute attention via the Euclidean inner product, an architectural choice that Dong et al. (2021) proved causes representational rank t…
EEG-JEPA: Structured Latent Prediction for EEG Foundation Models
Jinhao Li, Zhiyuan Ma, Xueqiao Han +8
Electroencephalography (EEG) foundation models aim to learn reusable representations from large-scale unlabeled recordings. A common pretraining strategy is masked waveform reconst…
Rethinking EEG-Based Disease Diagnosis: Decoupling Instance Representation Learning from Subject-Level Supervision
Zhiyuan Ma, Zeyuan Li, Zhiyi Lu +7
EEG-based disease diagnosis requires one prediction per subject, yet common pipelines segment recordings into short instances, inherit the subject label for every instance, and tra…
Why Attend to Everything? Focus is the Key
Hengshuai Yao, Xing Chen, Ahmed Murtadha +8
Standard attention scales quadratically with sequence length. Efficient attention methods reduce this O(n^2) cost, but when retrofitted into pretrained models, they often degrade p…
Training-Driven Representational Geometry Modularization Predicts Brain Alignment in Language Models
Yixuan Liu, Zhiyuan Ma, Likai Tang +5
How large language models (LLMs) align with the neural representation and computation of human language is a central question in cognitive science. Using representational geometry…
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…