3 citations · 7 across the 10 of their papers we have counts for
6 papers · 1 filter
Synergistic Development of Perovskite Memristors and Algorithms for Robust Analog Computing
Nanyang Ye, Qiao Sun, Yifei Wang +10
Analog computing using non-volatile memristors has emerged as a promising solution for energy-efficient deep learning. New materials, like perovskites-based memristors are recently…
Integrating Amortized Inference with Diffusion Models for Learning Clean Distribution from Corrupted Images
Yifei Wang, Weimin Bai, Weijian Luo +2
Diffusion models (DMs) have emerged as powerful generative models for solving inverse problems, offering a good approximation of prior distributions of real-world image data. Typic…
On the Role of Discrete Tokenization in Visual Representation Learning
Tianqi Du, Yifei Wang, Yisen Wang
In the realm of self-supervised learning (SSL), masked image modeling (MIM) has gained popularity alongside contrastive learning methods. MIM involves reconstructing masked regions…
An Expectation-Maximization Algorithm for Training Clean Diffusion Models from Corrupted Observations
Weimin Bai, Yifei Wang, Wenzheng Chen +1
Diffusion models excel in solving imaging inverse problems due to their ability to model complex image priors. However, their reliance on large, clean datasets for training limits…
Look Ahead or Look Around? A Theoretical Comparison Between Autoregressive and Masked Pretraining
Qi Zhang, Tianqi Du, Haotian Huang +2
In recent years, the rise of generative self-supervised learning (SSL) paradigms has exhibited impressive performance across visual, language, and multi-modal domains. While the va…
Do Generated Data Always Help Contrastive Learning?
Yifei Wang, Jizhe Zhang, Yisen Wang
Contrastive Learning (CL) has emerged as one of the most successful paradigms for unsupervised visual representation learning, yet it often depends on intensive manual data augment…