A Proof of Learning Rate Transfer under $mu$P

The Hidden Power of Normalization: Exponential Capacity Control in Deep Neural Networks

arXiv:2511.00958v1 Announce Type: cross Abstract: Normalization methods are fundamental components of modern deep neural networks (DNNs). Empirically, they are known to stabilize optimization dynamics and

FeNN-DMA: A RISC-V SoC for SNN acceleration

arXiv:2511.00732v1 Announce Type: cross Abstract: Spiking Neural Networks (SNNs) are a promising, energy-efficient alternative to standard Artificial Neural Networks (ANNs) and are particularly well-suited to

Parameter Interpolation Adversarial Training for Robust Image Classification

arXiv:2511.00836v1 Announce Type: cross Abstract: Though deep neural networks exhibit superior performance on various tasks, they are still plagued by adversarial examples. Adversarial training has

Region-Aware Reconstruction Strategy for Pre-training fMRI Foundation Model

arXiv:2511.00443v1 Announce Type: cross Abstract: The emergence of foundation models in neuroimaging is driven by the increasing availability of large-scale and heterogeneous brain imaging datasets.

FlashEVA: Accelerating LLM inference via Efficient Attention

arXiv:2511.00576v1 Announce Type: cross Abstract: Transformer models have revolutionized natural language processing, achieving state-of-the-art performance and demonstrating remarkable scalability. However, their memory demands, particularly due