Yilun Xu | DeepAI

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∙ 06/26/2023

Restart Sampling for Improving Generative Processes

Generative processes that involve solving differential equations, such a...

0 Yilun Xu, et al. ∙

∙ 04/05/2023

GenPhys: From Physical Processes to Generative Models

Since diffusion models (DM) and the more recent Poisson flow generative ...

0 Ziming Liu, et al. ∙

∙ 02/08/2023

PFGM++: Unlocking the Potential of Physics-Inspired Generative Models

We introduce a new family of physics-inspired generative models termed P...

0 Yilun Xu, et al. ∙

∙ 02/01/2023

Stable Target Field for Reduced Variance Score Estimation in Diffusion Models

Diffusion models generate samples by reversing a fixed forward diffusion...

0 Yilun Xu, et al. ∙

∙ 09/22/2022

Poisson Flow Generative Models

We propose a new "Poisson flow" generative model (PFGM) that maps a unif...

7 Yilun Xu, et al. ∙

∙ 01/27/2022

Controlling Directions Orthogonal to a Classifier

We propose to identify directions invariant to a given classifier so tha...

0 Yilun Xu, et al. ∙

∙ 11/14/2021

Color Mapping Functions For HDR Panorama Imaging: Weighted Histogram Averaging

It is challenging to stitch multiple images with different exposures due...

0 Yilun Xu, et al. ∙

∙ 11/14/2021

Deep Joint Demosaicing and High Dynamic Range Imaging within a Single Shot

Spatially varying exposure (SVE) is a promising choice for high-dynamic-...

12 Yilun Xu, et al. ∙

∙ 10/19/2021

Learning Representations that Support Robust Transfer of Predictors

Ensuring generalization to unseen environments remains a challenge. Doma...

0 Yilun Xu, et al. ∙

∙ 06/05/2021

Can Subnetwork Structure be the Key to Out-of-Distribution Generalization?

Can models with particular structure avoid being biased towards spurious...

0 Dinghuai Zhang, et al. ∙

∙ 07/14/2020

TCGM: An Information-Theoretic Framework for Semi-Supervised Multi-Modality Learning

Fusing data from multiple modalities provides more information to train ...

18 Xinwei Sun, et al. ∙

∙ 02/25/2020

A Theory of Usable Information Under Computational Constraints

We propose a new framework for reasoning about information in complex sy...

8 Yilun Xu, et al. ∙

∙ 09/08/2019

L_DMI: An Information-theoretic Noise-robust Loss Function

Accurately annotating large scale dataset is notoriously expensive both ...

16 Yilun Xu, et al. ∙

∙ 05/31/2019

Max-MIG: an Information Theoretic Approach for Joint Learning from Crowds

Eliciting labels from crowds is a potential way to obtain large labeled ...

1 Peng Cao, et al. ∙

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