3 papers
cs.LG2025
SAGE: Streaming Agreement-Driven Gradient Sketches for Representative Subset Selection
Ashish Jha, Salman Ahmadi-Asl
Training modern neural networks on large datasets is computationally and energy intensive. We present SAGE, a streaming data-subset selection method that maintains a compact Freque…
cs.LG2025
GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling
Ashish Jha, Anh huy Phan, Razan Dibo +1
Training modern neural networks on large datasets is computationally and environmentally costly. We introduce GRAFT, a scalable in-training subset selection method that (i) extract…
cs.CV2025
Lightweight Attribute Localizing Models for Pedestrian Attribute Recognition
Ashish Jha, Dimitrii Ermilov, Konstantin Sobolev +8
Pedestrian Attribute Recognition (PAR) focuses on identifying various attributes in pedestrian images, with key applications in person retrieval, suspect re-identification, and sof…