appliedml42

@appliedml42

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https://www.youtube.com/watch?v=sBmcprlAPb0

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Reversible Vision Transformers https://arxiv.org/abs/2302.04869v1 https://i.imgur.com/XJlssKp.png

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Bag of Tricks for Training Data Extraction from Language Models abs:https://arxiv.org/abs/2302.04460v1 https://i.imgur.com/MT3TcVP.png

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https://arxiv.org/abs/2302.01676v1 This paper proposes MERLIN, a novel multimodal deep learning framework designed to predict the NFT selling prices. MERLIN exploits only NFT images and textual descriptions. https://i.imgur.com/1PmQQ3M.png

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This is an amazing read. Cramming: Training a Language Model on a Single GPU in One Day abs: https://arxiv.org/abs/2212.14034 🔥summary thread from Lucas https://twitter.com/giffmana/status/1608568387583737856?s=61&t=qPnqsJlJqse2GDklDp4hww

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If anybody is looking to get working knowledge of Stable Diffusion. I would recommend checking out this series. This was super helpful for me when I was learning the basics of SD. https://github.com/huggingface/diffusion-models-class

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Dreamix: Video Diffusion Models are General Video Editors https://dreamix-video-editing.github.io/

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Language Quantized AutoEncoders: Towards Unsupervised Text-Image Alignment https://arxiv.org/abs/2302.00902v1 https://i.imgur.com/y5DbdyX.png

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An amazing explanation of Attention Mechanism by the OG Karpathy. https://youtu.be/kCc8FmEb1nY

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abs: https://arxiv.org/abs/2302.00003v1 https://i.imgur.com/fHV1TXd.png

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abs: https://arxiv.org/abs/2302.00083v1 TLDR: This paper proposes an alternative approach to Retrieval-Augmented Language Modeling (RALM) called In-Context RALM, which prepends relevant documents from a grounding corpus to the input of an unchanged language model. https://i.imgur.com/JBFZWcN.png

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