13 citations · 50 across the 14 of their papers we have counts for
6 papers · 1 filter
Physics-Driven Turbulence Image Restoration with Stochastic Refinement
Ajay Jaiswal, Xingguang Zhang, Stanley H. Chan +1
Image distortion by atmospheric turbulence is a stochastic degradation, which is a critical problem in long-range optical imaging systems. A number of research has been conducted d…
FarSight: A Physics-Driven Whole-Body Biometric System at Large Distance and Altitude
Feng Liu, Ryan Ashbaugh, Nicholas Chimitt +17
Whole-body biometric recognition is an important area of research due to its vast applications in law enforcement, border security, and surveillance. This paper presents the end-to…
The Emergence of Essential Sparsity in Large Pre-trained Models: The Weights that Matter
Ajay Jaiswal, Shiwei Liu, Tianlong Chen +1
Large pre-trained transformers are show-stealer in modern-day deep learning, and it becomes crucial to comprehend the parsimonious patterns that exist within them as they grow in s…
Outline, Then Details: Syntactically Guided Coarse-To-Fine Code Generation
Wenqing Zheng, S P Sharan, Ajay Kumar Jaiswal +4
For a complicated algorithm, its implementation by a human programmer usually starts with outlining a rough control flow followed by iterative enrichments, eventually yielding care…
Sparsity May Cry: Let Us Fail (Current) Sparse Neural Networks Together!
Shiwei Liu, Tianlong Chen, Zhenyu Zhang +4
Sparse Neural Networks (SNNs) have received voluminous attention predominantly due to growing computational and memory footprints of consistently exploding parameter count in large…
Sparse MoE as the New Dropout: Scaling Dense and Self-Slimmable Transformers
Tianlong Chen, Zhenyu Zhang, Ajay Jaiswal +2
Despite their remarkable achievement, gigantic transformers encounter significant drawbacks, including exorbitant computational and memory footprints during training, as well as se…