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ML-Papers-of-the-Week
Highlighting top ML papers of the week. Top ML Papers of the Week (Jan 1-8):
- 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. - Paper: https://arxiv.org/abs/2301.00704
- 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: - https://valle-demo.github.io/
- 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. - https://arxiv.org/abs/2301.00303
- Presents a technique for compressing large language models while not sacrificing performance; "pruned to at least 50% sparsity in one-shot, without any retraining." - https://arxiv.org/pdf/2301.00774.pdf
- ConvNeXt V2 is a performant model based on a fully convolutional masked autoencoder framework and other architectural improvements. CNNs are sticking back! - https://arxiv.org/abs/2301.00808
- 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. - https://arxiv.org/abs/2301.01181
- 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. - https://transformer-circuits.pub/2023/toy-double-descent/index.html
- StitchNet is a novel paradigm to create new coherent neural networks by reusing pretrained fragments of existing NNs. - https://arxiv.org/abs/2301.01947
- Proposes integrated decomposition, an approach to improve Science Q&A through a human-in-the-loop workflow for refining compositional LM programs. - https://arxiv.org/abs/2301.01751
- A Succinct Summary of Reinforcement Learning. A nice little overview of some important ideas in RL. - https://arxiv.org/abs/2301.01379