Answers.Body: <p>There are some wonderful resources for keeping up to date in the ML community. Here are just a handful that a coworker showed me:</p>
  
  <ol>
  <li><p><a href="http://deeplearn.org/" rel="noreferrer">Deep Learning Monitor</a>: this site contains hot and new papers along with tweets that are popularized by the community! You can even checkout RL papers specifically here  </p></li>
  <li><p><a href="http://www.arxiv-sanity.com/" rel="noreferrer">arxiv-sanity</a>: this site updates with popular and new papers that make it onto Arxiv</p></li>
  <li><p><a href="https://paperswithcode.com/" rel="noreferrer">papers with code</a>: this site is wonderful because not only does it link to papers, but it links to their implementation for reproduction or assistance in your own personal projects. They even have a leaderboard and track state of the art (<a href="https://paperswithcode.com/sota" rel="noreferrer">SoTA</a>) on tons of different tasks  </p></li>
  <li><p><a href="https://twitter.com/DL_ML_Loop/lists/deep-learning-loop" rel="noreferrer">DL_twitter loop</a>: You can't forget twitter, given that most researchers use it; this is just a single nice group you may like</p></li>
  </ol>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-07-18T12:23:59.887Z
Answers.LastActivityDate: 2019-07-18T21:58:58.073Z
Answers.LastEditDate: 2019-07-18T21:58:58.073Z
Answers.UserDisplayName: mshlis
Body: <p>As a student who wants to work on machine learning, I would like to know how it is possible to start my studies and how to follow it to stay up-to-date. For example, I am willing to work on RL and MAB problems, but there are huge literatures on these topics. Moreover, these topics are studied by researchers from different communities such as AI and ML, Operations Research, Control Engineering, Statistics, etc. And, I think that several papers are published on these topics every week which make it so difficult to follow them. </p>
  
  <p>I would be thankful if someone can suggest a road-map to start studying these topics, follow them and how I should select and study new published papers. Finally, I am willing to know the new trend in RL and MAB problem.</p>
  
Tags: <machine-learning><reinforcement-learning><research><markov-decision-process>
Title: How to stay a up-to-date researcher in ML/RL community?
UserDisplayName: Amin
ThreadText: # How to stay a up-to-date researcher in ML/RL community?
  
  (Asked by: username_0 on 2019-07-18)
  
  As a student who wants to work on machine learning, I would like to know how it is possible to start my studies and how to follow it to stay up-to-date. For example, I am willing to work on RL and MAB problems, but there are huge literatures on these topics. Moreover, these topics are studied by researchers from different communities such as AI and ML, Operations Research, Control Engineering, Statistics, etc. And, I think that several papers are published on these topics every week which make it so difficult to follow them.
  
  I would be thankful if someone can suggest a road-map to start studying these topics, follow them and how I should select and study new published papers. Finally, I am willing to know the new trend in RL and MAB problem.
  
  **Tags:** machine-learning, reinforcement-learning, research, markov-decision-process
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2019-07-18. Score: 18
  
  There are some wonderful resources for keeping up to date in the ML community. Here are just a handful that a coworker showed me:
  
  1. [Deep Learning Monitor](http://deeplearn.org/): this site contains hot and new papers along with tweets that are popularized by the community! You can even checkout RL papers specifically here
  2. [arxiv-sanity](http://www.arxiv-sanity.com/): this site updates with popular and new papers that make it onto Arxiv
  3. [papers with code](https://paperswithcode.com/): this site is wonderful because not only does it link to papers, but it links to their implementation for reproduction or assistance in your own personal projects. They even have a leaderboard and track state of the art ([SoTA](https://paperswithcode.com/sota)) on tons of different tasks
  4. [DL\_twitter loop](https://twitter.com/DL_ML_Loop/lists/deep-learning-loop): You can't forget twitter, given that most researchers use it; this is just a single nice group you may like
  
  
  ---
  
  