How to: Reports&Papers
|
No.
|
Report&Paper
|
Description
|
|:---- |:-------------------------------------------------------------------------------------------------------------- |:---------------------------------------------------- |
| 1    | [GPT-4 Technical Report](https://cdn.openai.com/papers/gpt-4.pdf)         | GPT-4 Technical Report                              |
| 2    | [mli/paper-reading](https://github.com/mli/paper-reading)                 | Deep learning classics and new papers are read carefully paragraph by paragraph.                        |
| 3   | [labmlai/annotated_deep_learning_paper_implementations](https://github.com/labmlai/annotated_deep_learning_paper_implementations)| A collection of simple PyTorch implementations of neural networks and related algorithms, which are documented with explanations |
| 4    | [Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models](https://arxiv.org/pdf/2303.04671.pdf) | Talking, Drawing and Editing with Visual Foundation Models |
| 5    |       [OpenAI Research ](https://openai.com/research)                                     |                          The latest research report and papers from OpenAI                       |
| 6    | [Make-A-Video: Text-to-Video Generation without Text-Video Data](https://arxiv.org/pdf/2209.14792.pdf)|Meta's Text-to-Video Generation|
| 7    | [eDiff-I: Text-to-Image Diffusion Models with Ensemble of Expert Denoisers](https://arxiv.org/pdf/2211.01324.pdf)| Nvidia eDiff-I - New generation of generative AI content creation tool |
| 8    | [Training an Assistant-style Chatbot with Large Scale Data Distillation from GPT-3.5-Turbo ](https://s3.amazonaws.com/static.nomic.ai/gpt4all/2023_GPT4All_Technical_Report.pdf)| 2023 GPT4All Technical Report |
| 9    | [Segment Anything](https://arxiv.org/abs/2304.02643)| Meta Segment Anything |
| 10   | [LLaMA: Open and Efficient Foundation Language Models](https://arxiv.org/abs/2302.13971)| LLaMA: a collection of foundation language models ranging from 7B to 65B parameters|
|  11  | [papers-we-love/papers-we-love](https://github.com/papers-we-love/papers-we-love) |Papers from the computer science community to read and discuss|
|  12  | [CVPR 2023 papers](https://github.com/SkalskiP/top-cvpr-2023-papers) |The most exciting and influential CVPR 2023 papers|
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