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| | 2017 | [YOLOv2](http://arxiv.org/abs/1612.08242v1) | |[![citation](https://img.shields.io/badge/dynamic/json?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F7d39d69b23424446f0400ef603b2e3e22d0309d6%3Ffields%3DcitationCount)](https://www.semanticscholar.org/paper/7d39d69b23424446f0400ef603b2e3e22d0309d6) |
| | 2018 | [YOLOv3](http://arxiv.org/abs/1804.02767v1) | |[![citation](https://img.shields.io/badge/dynamic/json?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2Fe4845fb1e624965d4f036d7fd32e8dcdd2408148%3Ffields%3DcitationCount)](https://www.semanticscholar.org/paper/e4845fb1e624965d4f036d7fd32e8dcdd2408148) |
| | 2019 | [CenterNet](https://arxiv.org/pdf/1904.07850.pdf) | Anchor free |[![citation](https://img.shields.io/badge/dynamic/json?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F6a2e2fd1b5bb11224daef98b3fb6d029f68a73f2%3Ffields%3DcitationCount)](https://www.semanticscholar.org/paper/Objects-as-Points-Zhou-Wang/6a2e2fd1b5bb11224daef98b3fb6d029f68a73f2) |
| | 2020 | [DETR](https://arxiv.org/pdf/2005.12872.pdf) | Transformer |[![citation](https://img.shields.io/badge/dynamic/json?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F962dc29fdc3fbdc5930a10aba114050b82fe5a3e%3Ffields%3DcitationCount)](https://www.semanticscholar.org/paper/End-to-End-Object-Detection-with-Transformers-Carion-Massa/962dc29fdc3fbdc5930a10aba114050b82fe5a3e) |
| ✅ | 2020 | [DETR](https://arxiv.org/pdf/2005.12872.pdf) | Transformer |[![citation](https://img.shields.io/badge/dynamic/json?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F962dc29fdc3fbdc5930a10aba114050b82fe5a3e%3Ffields%3DcitationCount)](https://www.semanticscholar.org/paper/End-to-End-Object-Detection-with-Transformers-Carion-Massa/962dc29fdc3fbdc5930a10aba114050b82fe5a3e) |
<a name="contrastive_learning"></a>
@@ -146,9 +146,9 @@
| 已录制 | 年份 | 名字 | 简介 | 引用 |
| ------ | ---- | ------------------------------------------------------------ | -------------------- | ------------------------------------------------------------ |
| ✅ | 2014 | [参数服务器](https://www.usenix.org/system/files/conference/osdi14/osdi14-paper-li_mu.pdf) | 支持千亿参数的传统机器学习模型 |[![citation](https://img.shields.io/badge/dynamic/json?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F0de0c3240bda7972bd0a3c8369ebc4b4f2e4f9c2%3Ffields%3DcitationCount)](https://www.semanticscholar.org/paper/Scaling-Distributed-Machine-Learning-with-the-Li-Andersen/0de0c3240bda7972bd0a3c8369ebc4b4f2e4f9c2) |
| | 2018 | [GPipe](https://proceedings.neurips.cc/paper/2019/file/093f65e080a295f8076b1c5722a46aa2-Paper.pdf) | 流水线（Pipeline）并行 |[![citation](https://img.shields.io/badge/dynamic/json?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2Fc18663fea10c8a303d045fd2c1f33cacf9b73ca3%3Ffields%3DcitationCount)](https://www.semanticscholar.org/paper/GPipe%3A-Efficient-Training-of-Giant-Neural-Networks-Huang-Cheng/c18663fea10c8a303d045fd2c1f33cacf9b73ca3) |
| | 2019 | [Megatron-LM](https://arxiv.org/pdf/1909.08053.pdf) | 张量（Tensor）并行 |[![citation](https://img.shields.io/badge/dynamic/json?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F8323c591e119eb09b28b29fd6c7bc76bd889df7a%3Ffields%3DcitationCount)](https://www.semanticscholar.org/paper/Megatron-LM%3A-Training-Multi-Billion-Parameter-Using-Shoeybi-Patwary/8323c591e119eb09b28b29fd6c7bc76bd889df7a) |
| | 2019 | [Zero](https://arxiv.org/pdf/1910.02054.pdf) | 参数分片 |[![citation](https://img.shields.io/badge/dynamic/json?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F00c957711b12468cb38424caccdf5291bb354033%3Ffields%3DcitationCount)](https://www.semanticscholar.org/paper/ZeRO%3A-Memory-optimizations-Toward-Training-Trillion-Rajbhandari-Rasley/00c957711b12468cb38424caccdf5291bb354033) |
| ✅ | 2018 | [GPipe](https://proceedings.neurips.cc/paper/2019/file/093f65e080a295f8076b1c5722a46aa2-Paper.pdf) | 流水线（Pipeline）并行 |[![citation](https://img.shields.io/badge/dynamic/json?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2Fc18663fea10c8a303d045fd2c1f33cacf9b73ca3%3Ffields%3DcitationCount)](https://www.semanticscholar.org/paper/GPipe%3A-Efficient-Training-of-Giant-Neural-Networks-Huang-Cheng/c18663fea10c8a303d045fd2c1f33cacf9b73ca3) |
| ✅ | 2019 | [Megatron-LM](https://arxiv.org/pdf/1909.08053.pdf) | 张量（Tensor）并行 |[![citation](https://img.shields.io/badge/dynamic/json?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F8323c591e119eb09b28b29fd6c7bc76bd889df7a%3Ffields%3DcitationCount)](https://www.semanticscholar.org/paper/Megatron-LM%3A-Training-Multi-Billion-Parameter-Using-Shoeybi-Patwary/8323c591e119eb09b28b29fd6c7bc76bd889df7a) |
| ✅ | 2019 | [Zero](https://arxiv.org/pdf/1910.02054.pdf) | 参数分片 |[![citation](https://img.shields.io/badge/dynamic/json?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F00c957711b12468cb38424caccdf5291bb354033%3Ffields%3DcitationCount)](https://www.semanticscholar.org/paper/ZeRO%3A-Memory-optimizations-Toward-Training-Trillion-Rajbhandari-Rasley/00c957711b12468cb38424caccdf5291bb354033) |
| ✅ | 2022 | [Pathways](https://arxiv.org/pdf/2203.12533.pdf) | 将Jax拓展到上千TPU核上 |[![citation](https://img.shields.io/badge/dynamic/json?label=citation&query=citationCount&url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F512e9aa873a1cd7eb61ce1f25ca7df6acb7e2352%3Ffields%3DcitationCount)](https://www.semanticscholar.org/paper/Pathways%3A-Asynchronous-Distributed-Dataflow-for-ML-Barham-Chowdhery/512e9aa873a1cd7eb61ce1f25ca7df6acb7e2352) |
### 图神经网络