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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…
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…
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…
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…
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…