From the 1 of 140 linked papers with an AI index.
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Sparse Coding with Multi-Layer Decoders using Variance Regularization
Katrina Evtimova, Yann LeCun
Sparse representations of images are useful in many computer vision applications. Sparse coding with an penalty and a learned linear dictionary requires regularization of the…
Learning in High Dimension Always Amounts to Extrapolation
Randall Balestriero, Jerome Pesenti, Yann LeCun
The notion of interpolation and extrapolation is fundamental in various fields from deep learning to function approximation. Interpolation occurs for a sample whenever this sam…
Understanding Dimensional Collapse in Contrastive Self-supervised Learning
Li Jing, Pascal Vincent, Yann LeCun +1
Self-supervised visual representation learning aims to learn useful representations without relying on human annotations. Joint embedding approach bases on maximizing the agreement…
Decoupled Contrastive Learning
Chun-Hsiao Yeh, Cheng-Yao Hong, Yen-Chi Hsu +3
Contrastive learning (CL) is one of the most successful paradigms for self-supervised learning (SSL). In a principled way, it considers two augmented "views" of the same image as p…
Compact and Optimal Deep Learning with Recurrent Parameter Generators
Jiayun Wang, Yubei Chen, Stella X. Yu +2
Deep learning has achieved tremendous success by training increasingly large models, which are then compressed for practical deployment. We propose a drastically different approach…
VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning
Adrien Bardes, Jean Ponce, Yann LeCun
Recent self-supervised methods for image representation learning are based on maximizing the agreement between embedding vectors from different views of the same image. A trivial s…