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Most popular Deep Learning papers in the Bibliography

The response to our first published version of the Annotated Deeplearning Bibliography was overwhelming, in just 4 days it has had more than 14000 (fourteen thousand) visits from more than a 100 countries. We got both good feedback and constructive criticism, and will update the bibliography based on this soon.

Most popular papers so far
While you wait for an improved / iterated version of the bibliography, here are the 20 most popular papers so far in the bibliography:

  1. Neural Networks – a Review
  2. Analysis of Deep Convolutional Neural Networks
  3. Manifold Regularized Deep Neural Networks
  4. Modeling Interestingness with Deep Neural Networks
  5. Replicating the Paper “Playing Atari with Deep Reinforcement Learning”
  6. Emotion Detection using Deep Belief Networks
  7. Deep learning based imaging data completion for improved brain disease diagnosis
  8. Deep learning for real-time robust facial expression recognition no a smartphone
  9. Classifying EEEG recordings of rhytm perception
  10. A comparison of dropout and weight decay for regularizing deep neural networks
  11. Dropout: a Simple way to Prevent Neural Networks from Overfitting
  12. DeepID-Net: multi-stage and deformable deep convolutional neural networks for object detection
  13. Asynchronous stochastic optimization for sequence training of deep neural networks
  14. RankCNN: When learning to rank encounters the pseudo reference feedback
  15. Effective Multi-Modal Retrieval based on stacked Auto-Encoders
  16. Unsupervised Learning of Word Semantic Embedding using the Deep Structured Semantic Model
  17. Learning motion-difference features using Gaussian restricted Boltzmann machines for efficient human action recognition
  18. Single Channel Source Separation with General Stochastic Networks
  19. Training itself: Mixed-signal training acceleration for memristor-based neural network
  20. Deep Learning for the Connectome

Want to contribute to the bibliography?
If you want to contribute to the bibliography please have a look at the github repository: github.com/memkite/DeepLearningBibliography and follow instructions in the readme file.

(we already got contributions from Knut Hellan in Zedge – thanks!)

Best regards,


Amund Tveit, Torbjørn Morland and Thomas Brox Røst

Memkite Engineering Team

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About Amund Tveit (@atveit - amund@memkite.com)

Amund Tveit works in Memkite on developing large-scale Deep Learning and Search (Convolutional Neural Network) with Swift and Metal for iOS (see deeplearning.education for a Memkite app video demo). He also maintains the deeplearning.university bibliography (github.com/memkite/DeepLearningBibliography)

Amund previously co-founded Atbrox , a cloud computing/big data service company (partner with Amazon Web Services), also doing some “sweat equity” startup investments in US and Nordic startups. His presentations about Hadoop/Mapreduce Algorithms and Search were among top 3% of all SlideShare presentations in 2013 and his blog posts has been frequently quoted by Big Data Industry Leaders and featured on front pages of YCombinator News and Reddit Programming

He previously worked for Google, where he was tech.lead for Google News for iPhone (mentioned as “Google News Now Looks Beautiful On Your iPhone” on Mashable.com), lead a team measuring and improving Google Services in the Scandinavian Countries (Maps and Search) and worked as a software engineer on infrastructure projects. Other work experience include telecom (IBM Canada) and insurance/finance (Storebrand).

Amund has a PhD in Computer Science. His publications has been cited more than 500 times. He also holds 4 US patents in the areas of search and advertisement technology, and a pending US patent in the area of brain-controlled search with consumer-level EEG devices.

Amund enjoys coding, in particular Python, C++ and Swift (iOS)

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