89 citations · 388 across the 81 of their papers we have counts for
12 papers · 1 filter
LUNA: Linear Universal Neural Attention with Generalization Guarantees
Ashkan Shahbazi, Ping He, Ali Abbasi +6
Scaling attention faces a critical bottleneck: the quadratic computational cost of softmax attention, which limits its application in long-sequence domains. Whil…
Low-Rank Prehab: Preparing Neural Networks for SVD Compression
Haoran Qin, Shansita Sharma, Ali Abbasi +2
Low-rank approximation methods such as singular value decomposition (SVD) and its variants (e.g., Fisher-weighted SVD, Activation SVD) have recently emerged as effective tools for…
Efficient Transferable Optimal Transport via Min-Sliced Transport Plans
Xinran Liu, Elaheh Akbari, Rocio Diaz Martin +2
Optimal Transport (OT) offers a powerful framework for finding correspondences between distributions and addressing matching and alignment problems in various areas of computer vis…
EMPEROR: Efficient Moment-Preserving Representation of Distributions
Xinran Liu, Shansita D. Sharma, Soheil Kolouri
We introduce EMPEROR (Efficient Moment-Preserving Representation of Distributions), a mathematically rigorous and computationally efficient framework for representing high-dimensio…
LOTFormer: Doubly-Stochastic Linear Attention via Low-Rank Optimal Transport
Ashkan Shahbazi, Chayne Thrash, Yikun Bai +3
Transformers have proven highly effective across modalities, but standard softmax attention scales quadratically with sequence length, limiting long context modeling. Linear attent…
OT-MeanFlow3D: Bridging Optimal Transport and Meanflow for Efficient 3D Point Cloud Generation
Elaheh Akbari, Shansita Sharma, Ping He +5
Flow-matching models have recently emerged as a powerful framework for continuous generative modeling, including 3D point cloud synthesis. However, their deployment is limited by t…