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The return of patch-based self-supervision! No ResNets but from one of the authors of the initial paper. Now with ViT, very simple self-supervised (SSL) pre-training shines again. It outperforms the contrastive learning counterparts and is simpler than BEiT, as its pixel-based (no tokenisation) needed. No need for special augmentation considerations like BYOL. i1k=87.8%


The "Decision Transformer: Reinforcement Learning via Sequence Modeling" : by Chen L. et al explaining how to use transformer to replace dynamic programming for context length reference.


An explanatory article on how can MLP is still useful comparing to modern machine learning architectures without convolutions, nor attention.


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