7 papers
Min Generalized Sliced Gromov Wasserstein: A Scalable Path to Gromov Wasserstein
Ashkan Shahbazi, Xinran Liu, Ping He +1
We propose min Generalized Sliced Gromov--Wasserstein (min-GSGW), a sliced formulation for the Gromov--Wasserstein (GW) problem using expressive generalized slicers. The key idea i…
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…
Vector-Quantized Soft Label Compression for Dataset Distillation
Ali Abbasi, Ashkan Shahbazi, Hamed Pirsiavash +1
Dataset distillation is an emerging technique for reducing the computational and storage costs of training machine learning models by synthesizing a small, informative subset of da…
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…
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…
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…