3 citations · 7 across the 10 of their papers we have counts for
6 papers · 1 filter
Masked Auto-Regressive Variational Acceleration: Fast Inference Makes Practical Reinforcement Learning
Yuxuan Gu, Weimin Bai, Yifei Wang +2
Masked auto-regressive diffusion models (MAR) benefit from the expressive modeling ability of diffusion models and the flexibility of masked auto-regressive ordering. However, vani…
Uni-Instruct: One-step Diffusion Model through Unified Diffusion Divergence Instruction
Yifei Wang, Weimin Bai, Colin Zhang +3
In this paper, we unify more than 10 existing one-step diffusion distillation approaches, such as Diff-Instruct, DMD, SIM, SiD, -distill, etc, inside a theory-driven framework w…
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…
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…
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…