MLPapersOfTheWeek

ML Papers of The Week

We ❤️ reading ML papers so we have created this repo to highlight the top ML papers for every week.

Top ML Papers of the Week (Jan 1-8)

Paper	Links
1) Muse: Text-To-Image Generation via Masked Generative Transformers -- 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, Project, Code, Tweet
2) VALL-E Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers -- 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.	Project, Tweet
3) Rethinking with Retrieval: Faithful Large Language Model Inference -- 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.	Paper, Tweet
4) SparseGPT: Massive Language Models Can Be Accurately Pruned In One-Shot -- Presents a technique for compressing large language models while not sacrificing performance; "pruned to at least 50% sparsity in one-shot, without any retraining."	Paper, Tweet
5) ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders -- ConvNeXt V2 is a performant model based on a fully convolutional masked autoencoder framework and other architectural improvements. CNNs are sticking back!	Paper, Code, Tweet
6) Large Language Models as Corporate Lobbyists -- 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.	Paper , Code, Tweet
7) Superposition, Memorization, and Double Descent -- 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.	Paper, Tweet
8) StitchNet: Composing Neural Networks from Pre-Trained Fragments -- StitchNet: Interesting idea to create new coherent neural networks by reusing pretrained fragments of existing NNs. Not straightforward but there is potential in terms of efficiently reusing learned knowledge in pre-trained networks for complex tasks.	Paper, Tweet
9) Iterated Decomposition: Improving Science Q&A by Supervising Reasoning Processes -- Proposes integrated decomposition, an approach to improve Science Q&A through a human-in-the-loop workflow for refining compositional LM programs.	Paper, Code Tweet
10) A Succinct Summary of Reinforcement Learning -- A nice little overview of some important ideas in RL.	Paper, Tweet

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We use a combination of AI-powered tools, analytics, and human curation to build the lists of papers.