1 citations · 1 across the 8 of their papers we have counts for
10 papers
Sliced Wasserstein Sketches with Error Certificates
Elaheh Akbari, James Liu, Keaton Hamm +1
Compact representations that support geometrically meaningful comparisons are valuable for learning from probability distributions. We study optimal, free-weight -atom quantizat…
Vertical Sliced Wasserstein Distances for Meta-Measures
Elaheh Akbari, Ahmadreza Moradipari, Kyungtae Han +4
Many modern learning problems require comparing probability distributions whose individual observations are themselves probability measures, naturally giving rise to meta-measures…
SE(3)-MeanFlow: Few-Step Protein Backbone Generation on Lie Groups
Yikun Bai, Binghang Lu, Yikai Liu +7
Generative modeling of protein backbones promises the de novo design of proteins with prescribed structural and functional properties. Existing diffusion and flow-matching models p…
SurgFormer: Scalable Learning of Organ Deformation with Resection Support and Real-Time Inference
Ashkan Shahbazi, Elaheh Akbari, Kyvia Pereira +7
We introduce SurgFormer, a multiresolution gated transformer for data driven soft tissue simulation on volumetric meshes. High fidelity biomechanical solvers are often too costly f…
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…
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…
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 generative modeling, including 3D point cloud synthesis. However, their deployment is limited by the need for…
Neural-Augmented Kelvinlet for Real-Time Soft Tissue Deformation Modeling
Ashkan Shahbazi, Kyvia Pereira, Jon S. Heiselman +9
Accurate and efficient modeling of soft-tissue interactions is fundamental for advancing surgical simulation, surgical robotics, and model-based surgical automation. To achieve rea…
ESPFormer: Doubly-Stochastic Attention with Expected Sliced Transport Plans
Ashkan Shahbazi, Elaheh Akbari, Darian Salehi +3
While self-attention has been instrumental in the success of Transformers, it can lead to over-concentration on a few tokens during training, resulting in suboptimal information fl…
CovarNav: Machine Unlearning via Model Inversion and Covariance Navigation
Ali Abbasi, Chayne Thrash, Elaheh Akbari +2
The rapid progress of AI, combined with its unprecedented public adoption and the propensity of large neural networks to memorize training data, has given rise to significant data…