6 papers
ASAP: Amortized Doubly-Stochastic Attention via Sliced Dual Projection
Huy Tran, Max Milkert, David Hyde
Doubly-stochastic attention has emerged as a transport-based alternative to row-softmax attention, with recent Transformer variants using it to reduce attention sinks and rank coll…
Fused Partial Gromov-Wasserstein for Structured Objects
Yikun Bai, Shuang Wang, Huy Tran +3
Structured data, such as graphs, is vital in machine learning due to its capacity to capture complex relationships and interactions. In recent years, the Fused Gromov-Wasserstein (…
Understanding Learning with Sliced-Wasserstein Requires Rethinking Informative Slices
Huy Tran, Yikun Bai, Ashkan Shahbazi +2
The practical applications of Wasserstein distances (WDs) are constrained by their sample and computational complexities. Sliced-Wasserstein distances (SWDs) provide a workaround b…
Stereographic Spherical Sliced Wasserstein Distances
Huy Tran, Yikun Bai, Abihith Kothapalli +4
Comparing spherical probability distributions is of great interest in various fields, including geology, medical domains, computer vision, and deep representation learning. The uti…
Partial Transport for Point-Cloud Registration
Yikun Bai, Huy Tran, Steven B. Damelin +1
Point cloud registration plays a crucial role in various fields, including robotics, computer graphics, and medical imaging. This process involves determining spatial relationships…
PT: Partial Transport Distances
Xinran Liu, Yikun Bai, Huy Tran +3
Optimal transport and its related problems, including optimal partial transport, have proven to be valuable tools in machine learning for computing meaningful distances between pro…