Notes on a few specific types of projects: This section contains the detailed instructions for the different parts of your project.Submission: We’ll be using Gradescope for submission of all four parts of the final project.[Notice] This list is not being maintained anymore because of the overwhelming amount of deep learning papers published every day since 2017.
(Please read the contributing guide for further instructions, though just letting me know the title of papers can also be a big contribution to us.) (Update) You can download all top-100 papers with this and collect all authors' names with this. Please make sure to read the contributing guide before you make a pull request. Um has waived all copyright and related or neighboring rights to this work.
Also, bib file for all top-100 papers are available.
As I mentioned in the introduction, I believe that seminal works can give us lessons regardless of their application domain.
Thus, I would like to introduce top 100 deep learning papers here as a good starting point of overviewing deep learning researches.
Two of the main machine learning conferences are ICML and NIPS.
You can find papers from recent ICML conferences online: https://2017cc/Conferences/2017/Schedule. Finally, looking at class projects from previous years is a good way to get ideas.
Once you have identified a topic of interest, it can be useful to look up existing research on relevant topics by searching related keywords on an academic search engine such as:
Another important aspect of designing your project is to identify one or several datasets suitable for your topic of interest.
To get the news for newly released papers everyday, follow my twitter or facebook page!
Please note that we prefer seminal deep learning papers that can be applied to various researches rather than application papers.