The h Index & the Top 15 Deep Learning Conferences and Journals

The Google Scholar resource ranks the top journals and conferences using a fully automated h-index score.  The h-index is named after Jorge Hirsch, a physicist at the University of California, San Diego (UCSD), who proposed the index to determine theoretical physicists’ relative quality.  It is sometimes called the Hirsch index.  According to Wikipedia, the h index measures “both the productivity and citation impact of the publications of a scientist or scholar. The index is based on the set of the scientist’s most cited papers and the number of citations that they have received in other publications. The index can also be applied to the productivity and impact of a scholarly journal” (as is the case here).

Searching the Google Scholar site for Deep Learning resources returned the following list of the top 15 journals and conferences (the number to the right of each entry is the resource’s h5-index).  For comparison, Nature, the top-rated journal in the sciences, has an h5-index rating of 366.

The #1 Deep Learning resource in this list is the International Conference on Deep Learning, which takes place next month (Jul 10-15, 2018) in Stockholm, Sweden.

The #2 resource is the arXiv Machine Learning (stat.ML) archive of pre-press journal papers, hosted by the Cornell University Library.  This is an excellent collection of scholarly papers on topics related to machine learning.

The full list of Google Scholar’s top-15 resources follows:

1. International Conference on Machine Learning (ICML) – 91
2. arXiv Machine Learning (stat.ML) – 76
3. The Journal of Machine Learning Research – 73
4. Machine Learning – 37
5. European Conference on Machine Learning and Knowledge Discovery in Databases – 31
6. International Journal of Machine Learning and Cybernetics – 23
7. IEEE International Workshop on Machine Learning for Signal Processing – 19
8. International Conference on Machine Learning and Applications – 18
9. International Journal of Machine Learning and Computing – 16
10. International Workshop on Machine Learning in Medical Imaging – 12
11. Machine Learning and Data Mining in Pattern Recognition (MLDM) – 11
12. International Conference on Machine Learning and Cybernetics – 10
13. Asian Conference on Machine Learning – 10
14. Artificial Intelligent Systems and Machine Learning – 5
15. Transactions on Machine Learning and Artificial Intelligence – 5

 

About David Calloway

Hi! I'm David Calloway, the author of this blog on deep learning and artificial intelligence. I first started working with neural networks in the mid-80's, before the "dark winter" of neural networking technologies. I graduated from the U.S. Air Force Academy in 1979 with B.S. degrees in Physics and Electrical Engineering. In 1982, I received an MS degree in Electrical Engineering from Purdue University where I worked on early attempts at speech recognition. In 2005, I obtained another M.S. degree, this time in Biology from the University of Central Florida. My interest in neural networks and deep learning was rekindled recently, when I got involved in a project at Nova Technologies where I am using deep learning and TensorFlow to recognize and classify objects from satellite imagery.
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