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List of 75 Most Popular Deep Learning Papers in the Bibliography

A month ago we published a blog post showing the 20 most popular Deep Learning papers in the bibliography,  this time we’re showing the 75 most popular Deep Learning papers in the list below. Note that this blog post (and the other blog posts) and the deep learning bibliography itself is available on github:

Note: If you’re curious what Memkite does in Deep Learning – besides maintaining this bibliography – we’re building support for using large-scale deep networks on mobile and wearable devices. Check out demo video on deeplearning.education for a use case.

Best regards, Amund Tveit (@atveit)
Memkite Deep Learning Team

75 most popular Deep Learning Papers from the Bibliography

  1. Neural Networks: A Review
  2. Analysis of Deep Convolutional Neural Network Architectures
  3. Manifold Regularized Deep Neural Networks
  4. Emotion Detection using Deep Belief Networks
  5. Modeling interestingness with deep neural networks
  6. Classifying EEG recordings of rhythm perception
  7. Deep learning for real-time robust facial expression recognition on a smartphone
  8. Deep learning based imaging data completion for improved brain disease diagnosis
  9. A comparison of dropout and weight decay for regularizing deep neural networks
  10. Dropout: A Simple Way to Prevent Neural Networks from Overfitting
  11. RankCNN: When learning to rank encounters the pseudo preference feedback
  12. Effective Multi-Modal Retrieval based on Stacked Auto-Encoders
  13. Unsupervised Learning of Word Semantic Embedding using the Deep Structured Semantic Model
  14. Asynchronous stochastic optimization for sequence training of deep neural networks
  15. Deep Learning for the Connectome
  16. DeepID-Net: multi-stage and deformable deep convolutional neural networks for object detection
  17. Learning motion-difference features using Gaussian restricted Boltzmann machines for efficient human action recognition
  18. Learning Sparse Recurrent Neural Networks in Language Modeling
  19. Spoken emotion recognition using deep learning
  20. Single Channel Source Separation with General Stochastic Networks
  21. Caffe: Convolutional Architecture for Fast Feature Embedding
  22. Deep Belief Networks (DBNs)
  23. Deep learning multi-view representation for face recognition
  24. Training Large Scale Deep Neural Networks on the Intel Xeon Phi Many-core Coprocessor
  25. Exponentially Increasing the Capacity-to-Computation Ratio for Conditional Computation in Deep Learning
  26. Feed Forward Pre-training for Recurrent Neural Network Language Models
  27. Improved audio features for large-scale multimedia event detection
  28. Learning ensemble classifiers via restricted Boltzmann machines
  29. Training itself: Mixed-signal training acceleration for memristor-based neural network.
  30. A brief survey on deep belief networks and introducing a new object oriented MATLAB toolbox (DeeBNet)
  31. Hierarchical spatiotemporal feature extraction using recurrent online clustering
  32. Learning Separable Filters
  33. Best Practices for Convolutional Neural Networks Applied to Object Recognition in Images
  34. Deep Supervised and Convolutional Generative Stochastic Network for Protein Secondary Structure Prediction
  35. Think Positive: Towards Twitter Sentiment Analysis from Scratch
  36. SSpro/ACCpro 5: Almost Perfect Prediction of Protein Secondary Structure and Relative Solvent Accessibility Using Profiles, Machine Learning, and Structural Similarity.
  37. Large-scale video classification with convolutional neural networks
  38. Traffic Flow Prediction With Big Data: A Deep Learning Approach
  39. Multi-task Neural Networks for QSAR Predictions
  40. Should deep neural nets have ears? The role of auditory features in deep learning approaches
  41. Multimodal integration learning of robot behavior using deep neural networks
  42. SPINDLE: SPINtronic deep learning engine for large-scale neuromorphic computing
  43. The relation of eye gaze and face pose: Potential impact on speech recognition
  44. Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation
  45. Reweighted Wake-Sleep
  46. Generic Object Detection With Dense Neural Patterns and Regionlets
  47. Learning Methods for Variable Selection and Time Series Prediction
  48. Gpu Implementation of a Deep Learning Network for Financial Prediction
  49. Action Recognition Using Ensemble of Deep Convolutional Neural Networks
  50. RASR/NN: The RWTH neural network toolkit for speech recognition
  51. Hough Networks for Head Pose Estimation and Facial Feature Localization
  52. Alternate Layer Sparsity and Intermediate Fine-tuning for Deep Autoencoders
  53. Sequence to Sequence Learning with Neural Networks
  54. Facial Landmark Detection by Deep Multi-task Learning
  55. Learned-Norm Pooling for Deep Feedforward and Recurrent Neural Networks
  56. Handwritten Hangul recognition using deep convolutional neural networks
  57. GPU Accelerated Computation and Real-time Rendering of Cellular Automata Model for Spatial Simulation
  58. Survey and Implementation of Computer Vision Techniques for Humanoid Robots
  59. Kernel methods match deep neural networks on timit
  60. Searching for exotic particles in high-energy physics with deep learning
  61. Speeding up Convolutional Neural Networks with Low Rank Expansions
  62. Comparing Raw Data and Feature Extraction for Seizure Detection with Deep Learning Methods
  63. Autoencoder Trees
  64. Object Detection and Viewpoint Estimation with Auto-masking Neural Network
  65. Discriminative Convolutional Sum-Product Networks on GPU
  66. Deep Network Cascade for Image Super-resolution
  67. Deep Belief Networks used on High Resolution Multichannel Electroencephalography Data for Seizure Detection
  68. UAV Application for DARPA PERFECT
  69. ML-o-scope: a diagnostic visualization system for deep machine learning pipelines
  70. Draft: Deep Learning in Neural Networks: An Overview
  71. Comparisons of machine learning techniques for detecting malicious webpages
  72. Project Adam: Building an Efficient and Scalable Deep Learning Training System
  73. Learning to encode motion using spatio-temporal synchrony
  74. Modeling Video Dynamics with Deep Dynencoder
  75. On the saddle point problem for non-convex optimization

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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