1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.LG2026
LongSpike: Fractional Order Spiking State Space Models for Efficient Long Sequence Learning
Xinrui He, Qiyu Kang, Xuhao Li +1
Spiking Neural Networks (SNNs) are well-regarded for their biological plausibility and energy efficiency in processing sequential data. However, dominant SNN architectures typicall…
cs.LG2025
Beyond Sunk Costs: Boosting LLM Pre-training Efficiency via Orthogonal Growth of Mixture-of-Experts
Ruizhe Wang, Yucheng Ding, Xiao Liu +5
As the computational demands for pre-training Large Language Models (LLMs) continue to surge, the need for efficient training paradigms becomes critical. Despite the vast resources…
cs.LG2025★ 1 cited
Optimizing Large Language Model Training Using FP4 Quantization
Ruizhe Wang, Yeyun Gong, Xiao Liu +5
The growing computational demands of training large language models (LLMs) necessitate more efficient methods. Quantized training presents a promising solution by enabling low-bit…