Quality follows upgrading
Tag: cs.PF
arXiv:2604.01489v1 Announce Type: new Abstract: High-performance GPU kernels are critical to modern machine learning systems, yet developing efficient implementations remains a challenging, expert-driven process due …
arXiv:2603.11340v1 Announce Type: new Abstract: In this paper, we present a novel black-box online controller that uses only end-to-end measurements over short segments, without internal …
arXiv:2603.10026v1 Announce Type: cross Abstract: Operator fusion, as a key performance optimization technique in the deployment of AI models, significantly improves execution efficiency and has …
arXiv:2603.08960v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models deliver high quality at low training FLOPs, but this efficiency often vanishes at inference. We identify a …
arXiv:2603.02510v1 Announce Type: new Abstract: The transition from sequential to parallel computing is essential for modern high-performance applications but is hindered by the steep learning …
arXiv:2603.00326v1 Announce Type: new Abstract: Classification using sparse oblique random forests provides guarantees on uncertainty and confidence while controlling for specific error types. However, they …
arXiv:2602.23556v1 Announce Type: new Abstract: Large-scale Graph Neural Networks (GNNs) are typically trained by sampling a vertex's neighbors to a fixed distance. Because large input …
