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@@ -8,7 +8,7 @@ Highlighting top ML papers of the week.
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| **Paper / Project** | **Link** |
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| ------------- | ------------- |
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-| 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](https://arxiv.org/abs/2301.00704) , [Project](https://muse-model.github.io/)|
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+| 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](https://arxiv.org/abs/2301.00704) , [Project](https://muse-model.github.io/), [Code](https://github.com/lucidrains/muse-maskgit-pytorch)|
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| 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](https://valle-demo.github.io/) |
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| 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](https://arxiv.org/abs/2301.00303) |
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| 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](https://arxiv.org/pdf/2301.00774.pdf) |
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