970 citations · 2.8k across the 29 of their papers we have counts for
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Restart Sampling for Improving Generative Processes
Yilun Xu, Mingyang Deng, Xiang Cheng +3
Generative processes that involve solving differential equations, such as diffusion models, frequently necessitate balancing speed and quality. ODE-based samplers are fast but plat…
PFGM++: Unlocking the Potential of Physics-Inspired Generative Models
Yilun Xu, Ziming Liu, Yonglong Tian +3
We introduce a new family of physics-inspired generative models termed PFGM++ that unifies diffusion models and Poisson Flow Generative Models (PFGM). These models realize generati…
Does Learning from Decentralized Non-IID Unlabeled Data Benefit from Self Supervision?
Lirui Wang, Kaiqing Zhang, Yunzhu Li +2
Decentralized learning has been advocated and widely deployed to make efficient use of distributed datasets, with an extensive focus on supervised learning (SL) problems. Unfortuna…
Addressing Feature Suppression in Unsupervised Visual Representations
Tianhong Li, Lijie Fan, Yuan Yuan +5
Contrastive learning is one of the fastest growing research areas in machine learning due to its ability to learn useful representations without labeled data. However, contrastive…
Supervised Contrastive Learning
Prannay Khosla, Piotr Teterwak, Chen Wang +6
Contrastive learning applied to self-supervised representation learning has seen a resurgence in recent years, leading to state of the art performance in the unsupervised training…
Contrastive Representation Distillation
Yonglong Tian, Dilip Krishnan, Phillip Isola
Often we wish to transfer representational knowledge from one neural network to another. Examples include distilling a large network into a smaller one, transferring knowledge from…
Representation Learning on Graphs with Jumping Knowledge Networks
Keyulu Xu, Chengtao Li, Yonglong Tian +3
Recent deep learning approaches for representation learning on graphs follow a neighborhood aggregation procedure. We analyze some important properties of these models, and propose…