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@@ -23,15 +23,15 @@ Highlighting top ML papers of the week.
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- https://arxiv.org/abs/2301.01379
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# **ML-Papers-of-the-Week**
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-***Highlighting top ML papers of the week.***
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+**Highlighting top ML papers of the week.**
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**Top ML Papers of the Week (Jan 1-8):**
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| **Paper** | **Link** |
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| ------------- | :---: |
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-| 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) |
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-| 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/ |
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-| Content Cell | Content Cell |
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-| Content Cell | Content Cell |
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+| 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) |
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+| 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/ |
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+| 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 |
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+| 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 |
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| Content Cell | Content Cell |
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| Content Cell | Content Cell |
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| Content Cell | Content Cell |
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