Summary of Wasserstein Wormhole: Scalable Optimal Transport Distance with Transformers, by Doron Haviv et al.
Wasserstein Wormhole: Scalable Optimal Transport Distance with Transformersby Doron Haviv, Russell Zhang Kunes, Thomas Dougherty,…
Wasserstein Wormhole: Scalable Optimal Transport Distance with Transformersby Doron Haviv, Russell Zhang Kunes, Thomas Dougherty,…
MCPNet: An Interpretable Classifier via Multi-Level Concept Prototypesby Bor-Shiun Wang, Chien-Yi Wang, Wei-Chen ChiuFirst submitted…
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LaTiM: Longitudinal representation learning in continuous-time models to predict disease progressionby Rachid Zeghlache, Pierre-Henri Conze,…
Studying the Impact of Latent Representations in Implicit Neural Networks for Scientific Continuous Field Reconstructionby…
Taming Transformers for Realistic Lidar Point Cloud Generationby Hamed Haghighi, Amir Samadi, Mehrdad Dianati, Valentina…
Bidirectional Long-Range Parser for Sequential Data Understandingby George Leotescu, Daniel Voinea, Alin-Ionut PopaFirst submitted to…
WorDepth: Variational Language Prior for Monocular Depth Estimationby Ziyao Zeng, Daniel Wang, Fengyu Yang, Hyoungseob…
Convergence Analysis of Flow Matching in Latent Space with Transformersby Yuling Jiao, Yanming Lai, Yang…
FraGNNet: A Deep Probabilistic Model for Mass Spectrum Predictionby Adamo Young, Fei Wang, David Wishart,…