【博文】Top 20 Recent Research Papers on Machine Learning and Deep Learning

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1. By Thuy T. Pham, U. of Sydney.
   
2. c comments
   
3. Machine learning, especially its subfield of Deep Learning, had many amazing advances in the recent years, and important research papers may lead to breakthroughs in technology that get used by billions of people. The research in this field is developing very quickly and to help our readers monitor the progress we present the list of most important recent scientific papers published since 2014.
   
4. The criteria we used to select the 20 top papers are by using citation counts from three academic sources: scholar.google.com;academic.microsoft.com; and semanticscholar.org. Since the number of citations varied among sources and are estimated, we listed the results from academic.microsoft.com which is slightly lower than others.
   
5. 
   
6. For each paper we also give the year it was published, a Highly Influential Citation count (HIC) and Citation Velocity (CV) measures provided by semanticscholar.org.  HIC that presents how publications build upon and relate to each other is result of identifying meaningful citations. CV is the weighted average number of citations per year over the last 3 years. For some references, where CV is zero that means it was blank or not shown by semanticscholar.org.
   
7. 
   
8. Most (but not all) of these 20 papers, including the top 8, are on the topic of Deep Learning. However, we see strong diversity - only one author (Yoshua Bengio) has 2 papers, and the papers were published in many different venues: CoRR (3), ECCV (3), IEEE CVPR (3), NIPS (2), ACM Comp Surveys, ICML, IEEE PAMI, IEEE TKDE, Information Fusion, Int. J. on Computers & EE, JMLR, KDD, and Neural Networks. The top two papers have by far the highest citation counts than the rest. Note that the second paper is only published last year. Read (or re-read them) and learn about the latest advances.

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