6 papers
Comparative Study of Anatomical and Learned Features in AI Models for Structural Brain MRI
Boyang Yu, Miquel Lopez Escoriza, Long Chen +3
In this work, we comprehensively evaluate three popular feature-extraction paradigms in AI-based neuroimaging modeling: (1) computation of anatomical surfaces and volumes, (2) supe…
Learning Sparse Latent Predictive Foundation Model for Multimodal Neuroimaging
Haoxu Huang, Long Chen, Jingyun Chen +8
Brain MRIs are routinely acquired as multiple complementary sequences with unique contrast weighting, including T1-weighed imaging (T1w) anatomic and fluid-sensitive T2-weighted (T…
SHANG++: Robust Stochastic Acceleration under Multiplicative Noise
Yaxin Yu, Long Chen, Minfu Feng
Under the multiplicative noise scaling (MNS) condition, original Nesterov acceleration is provably sensitive to noise and may diverge when gradient noise overwhelms the signal. In…
LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models
Long Chen, Xiaotian Song, Yanan Sun
Spiking Large Language Models (LLMs) have emerged as an energy-efficient alternative to conventional LLMs through their event-driven computation. To effectively obtain spiking LLMs…
FAS: Fast ANN-SNN Conversion for Spiking Large Language Models
Long Chen, Xiaotian Song, Andy Song +3
Spiking Large Language Models have been shown as a good alternative to LLMs in various scenarios. Existing methods for creating Spiking LLMs, i.e., direct training and ANN-SNN conv…
Automatic Detection of Alzheimer's Disease with Multi-Modal Fusion of Clinical MRI Scans
Long Chen, Liben Chen, Binfeng Xu +2
The aging population of the U.S. drives the prevalence of Alzheimer's disease. Brookmeyer et al. forecasts approximately 15 million Americans will have either clinical AD or mild c…