MLPapersOfTheWeek

ML-Papers-of-the-Week

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

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. - Paper: https://arxiv.org/abs/2301.00704
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: - https://valle-demo.github.io/
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. - https://arxiv.org/abs/2301.00303
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." - https://arxiv.org/pdf/2301.00774.pdf
5. 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
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. - https://arxiv.org/abs/2301.01181
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. - https://transformer-circuits.pub/2023/toy-double-descent/index.html
8. StitchNet is a novel paradigm to create new coherent neural networks by reusing pretrained fragments of existing NNs. - https://arxiv.org/abs/2301.01947
9. 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
10. A Succinct Summary of Reinforcement Learning. A nice little overview of some important ideas in RL. - https://arxiv.org/abs/2301.01379

ML-Papers-of-the-Week

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

Paper	Link
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.	Paper Project
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:	Project
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.	Paper
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."	Paper
5. ConvNeXt V2 is a performant model based on a fully convolutional masked autoencoder framework and other architectural improvements. CNNs are sticking back!	Paper
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.	Paper
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.	Paper
8. 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. 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.	Content Cell