|
|
@@ -23,24 +23,3 @@ Highlighting top ML papers of the week.
|
|
|
- https://arxiv.org/abs/2301.01379
|
|
|
|
|
|
|
|
|
- 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
|
|
|
-
|
angysaravia