> For the complete documentation index, see [llms.txt](https://sliu583.gitbook.io/blog/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://sliu583.gitbook.io/blog/specific-work/seminar-and-talk/reading-groups/network-reading-group/ml-and-networking.md).

# ML & Networking

* [Networks of Machine Learning, for Machine Learning, by Machine Learning](https://www.youtube.com/watch?v=gfGTd7PXK54\&list=PLMPUUgLIYH1ZJVEXTTZT82ipTprSu1hn8\&index=1) 
* Categorize by applications 
  * Congestion control
    * [TCP ex Machina: Computer Generated Congestion Control](https://web.mit.edu/remy/) 
    * [A Deep Reinforcement Learning Perspective on Internet Congestion Control ](http://proceedings.mlr.press/v97/jay19a/jay19a.pdf)
    * [Pantheon: the training ground for internet congestion control research](https://www.usenix.org/system/files/conference/atc18/atc18-yan-francis.pdf) 
  * Scheduling
    * [Learning scheduling algorithms for data processing clusters](https://dl.acm.org/doi/10.1145/3341302.3342080) 
  * Video streaming 
    * [Neural Adaptive Video Streaming with Pensieve](http://people.csail.mit.edu/hongzi/content/publications/Pensieve-Sigcomm17.pdf) 
    * [Learning in situ: a randomized experiment in video streaming](https://www.usenix.org/system/files/nsdi20-paper-yan.pdf) (Fugu) 
    * [Cracking open the DNN black-box: video analytics with DNNs across the camera-cloud boundary](https://www.microsoft.com/en-us/research/publication/cracking-open-the-dnn-black-box-video-analytics-with-dnns-across-the-camera-cloud-boundary/)  
    * [Self-driven video streaming for deep learning inference](https://people.cs.uchicago.edu/~junchenj/docs/DDS-Sigcomm20.pdf) 
  * Other
    * [Neural Packet Classification ](https://xinjin.github.io/files/SIGCOMM19_NeuroCuts.pdf)