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# ML-Papers-of-the-Week
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Highlighting top ML papers of the week.
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+**Top ML Papers of the Week (Jan 1-8):**
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+1. GoogleAI introduces Muse, a new text-to-image generation model based on masked generative transformers; significantly more efficient than other diffusion models like Imagen and DALLE-2.
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+ - Paper: https://arxiv.org/abs/2301.00704
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+2. Microsoft introduces VALL-E, a text-to-audio model that performs state-of-the-art zero-shot performance; the text-to-speech synthesis task is treated as a conditional language modeling task:
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+ - https://valle-demo.github.io/
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+3. A new paper shows the potential of enhancing LLMs by retrieving relevant external knowledge based on decomposed reasoning steps obtained through chain-of-thought prompting.
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+ - https://arxiv.org/abs/2301.00303
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+4. Presents a technique for compressing large language models while not sacrificing performance; "pruned to at least 50% sparsity in one-shot, without any retraining."
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+ - https://arxiv.org/pdf/2301.00774.pdf
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+5. ConvNeXt V2 is a performant model based on a fully convolutional masked autoencoder framework and other architectural improvements. CNNs are sticking back!
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+ - https://arxiv.org/abs/2301.00808
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+6. With more capabilities, we are starting to see a wider range of applications with LLMs. This paper utilized large language models for conducting corporate lobbying activities.
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+ - https://arxiv.org/abs/2301.01181
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+7. This work aims to better understand how deep learning models overfit or memorize examples; interesting phenomena observed; important work toward a mechanistic theory of memorization.
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+ - https://transformer-circuits.pub/2023/toy-double-descent/index.html
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+8. StitchNet is a novel paradigm to create new coherent neural networks by reusing pretrained fragments of existing NNs.
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+ - https://arxiv.org/abs/2301.01947
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+9. Proposes integrated decomposition, an approach to improve Science Q&A through a human-in-the-loop workflow for refining compositional LM programs.
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+ - https://arxiv.org/abs/2301.01751
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+10. A Succinct Summary of Reinforcement Learning. A nice little overview of some important ideas in RL.
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+ - https://arxiv.org/abs/2301.01379
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angysaravia