3 papers
cs.LG2026
OptINC: Optical In-Network-Computing for Scalable Distributed Learning
Sijie Fei, Grace Li Zhang, Bing Li +1
Distributed learning is widely used for training large models on large datasets by distributing parts of the model or dataset across multiple devices and aggregating the computed r…
cs.CV2026
Beyond Dataset Distillation: Lossless Dataset Concentration via Diffusion-Assisted Distribution Alignment
Tongfei Liu, Yufan Liu, Bing Li +1
The high cost and accessibility problem associated with large datasets hinder the development of large-scale visual recognition systems. Dataset Distillation addresses these proble…
cs.CV2026
Amped: Adaptive Multi-stage Non-edge Pruning for Edge Detection
Yuhan Gao, Xinqing Li, Xin He +4
Edge detection is a fundamental image analysis task that underpins numerous high-level vision applications. Recent advances in Transformer architectures have significantly improved…