MLPapersOfTheWeek

ML-Papers-of-the-Week

Highlighting top ML papers of the week. Top ML Papers of the Week (Jan 1-8):

Paper / Project	Link
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
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
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
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
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
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 Paper/Code/Data
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
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
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
10. A Succinct Summary of Reinforcement Learning -- A nice little overview of some important ideas in RL.	Paper