6 papers
Entropic Optimal Transport Eigenmaps for Nonlinear Alignment and Joint Embedding of High-Dimensional Datasets
Boris Landa, Yuval Kluger, Rong Ma
Embedding high-dimensional data into a low-dimensional space is an indispensable component of data analysis. In numerous applications, it is necessary to align and jointly embed mu…
Injecting Measurement Information Yields a Fast and Noise-Robust Diffusion-Based Inverse Problem Solver
Jonathan Patsenker, Henry Li, Myeongseob Ko +2
Diffusion models have been firmly established as principled zero-shot solvers for linear and nonlinear inverse problems, owing to their powerful image prior and iterative sampling…
Understanding and Enhancing Mask-Based Pretraining towards Universal Representations
Mingze Dong, Leda Wang, Yuval Kluger
Mask-based pretraining has become a cornerstone of modern large-scale models across language, vision, and recently biology. Despite its empirical success, its role and limits in le…
Euclidean Distance Deflation Under High-Dimensional Heteroskedastic Noise
Keyi Li, Yuval Kluger, Boris Landa
Pairwise Euclidean distance calculation is a fundamental step in many machine learning and data analysis algorithms. In real-world applications, however, these distances are freque…
Dual Diffusion for Unified Image Generation and Understanding
Zijie Li, Henry Li, Yichun Shi +4
Diffusion models have gained tremendous success in text-to-image generation, yet still lag behind with visual understanding tasks, an area dominated by autoregressive vision-langua…
Likelihood Training of Cascaded Diffusion Models via Hierarchical Volume-preserving Maps
Henry Li, Ronen Basri, Yuval Kluger
Cascaded models are multi-scale generative models with a marked capacity for producing perceptually impressive samples at high resolutions. In this work, we show that they can also…