3 papers
cs.LG2026
Learning Neural Operators from Partial Observations via Latent Autoregressive Modeling
Jingren Hou, Hong Wang, Pengyu Xu +3
Real-world scientific applications frequently encounter incomplete observational data due to sensor limitations, geographic constraints, or measurement costs. Although neural opera…
cs.LG2025
Maximum Redundancy Pruning: A Principle-Driven Layerwise Sparsity Allocation for LLMs
Chang Gao, Kang Zhao, Runqi Wang +2
Large language models (LLMs) have demonstrated impressive capabilities, but their enormous size poses significant challenges for deployment in real-world applications. To address t…
cs.LG2025
Beyond 2:4: exploring V:N:M sparsity for efficient transformer inference on GPUs
Kang Zhao, Tao Yuan, Han Bao +6
To date, 2:4 sparsity has stood as the only sparse pattern that can be accelerated using sparse tensor cores on GPUs. In practice, 2:4 sparsity often possesses low actual speedups…