Deep Learning for Ultrasound Analysis

Ultrasound (also called Sonography) are sound waves with higher frequency than humans can hear, they frequently used in medical settings, e.g. for checking that pregnancy is going well with fetal ultrasound. For more about Ultrasound data formats check out Ultrasound Research Interface. This blog post has recent publications about applying Deep Learning for analyzing Ultrasound data.

Best regards,
Amund Tveit

Year  Title Authors
2016   Early-stage atherosclerosis detection using deep learning over carotid ultrasound images  RM Menchón
2016   Automatic Detection of Standard Sagittal Plane in the First Trimester of Pregnancy Using 3-D Ultrasound Data  S Nie, J Yu, P Chen, Y Wang, JQ Zhang
2016   Detection of prostate cancer using temporal sequences of ultrasound data: a large clinical feasibility study  S Azizi, F Imani, S Ghavidel, A Tahmasebi, JT Kwak
2016   Hough-CNN: Deep Learning for Segmentation of Deep Brain Regions in MRI and Ultrasound  F Milletari, SA Ahmadi, C Kroll, A Plate, V Rozanski
2016   Hybrid approach for automatic segmentation of fetal abdomen from ultrasound images using deep learning  H Ravishankar, SM Prabhu, V Vaidya, N Singhal
2016   Iterative Multi-domain Regularized Deep Learning for Anatomical Structure Detection and Segmentation from Ultrasound Images  H Chen, Y Zheng, JH Park, PA Heng, SK Zhou
2016   4D Cardiac Ultrasound Standard Plane Location by Spatial-Temporal Correlation  Y Gu, GZ Yang, J Yang, K Sun
2016   Computer-Aided Diagnosis for Breast Ultrasound Using Computerized BI-RADS Features and Machine Learning Methods  J Shan, SK Alam, B Garra, Y Zhang, T Ahmed
2016   Stacked Deep Polynomial Network Based Representation Learning for Tumor Classification with Small Ultrasound Image Dataset  J Shi, S Zhou, X Liu, Q Zhang, M Lu, T Wang
2016   Coupling Convolutional Neural Networks and Hough Voting for Robust Segmentation of Ultrasound Volumes  C Kroll, F Milletari, N Navab, SA Ahmadi
2016   Classifying Cancer Grades Using Temporal Ultrasound for Transrectal Prostate Biopsy  S Azizi, F Imani, JT Kwak, A Tahmasebi, S Xu, P Yan
2015   Tumor Classification by Deep Polynomial Network and Multiple Kernel Learning on Small Ultrasound Image Dataset  X Liu, J Shi, Q Zhang
2015   Automatic Recognition of Fetal Facial Standard Plane in Ultrasound Image via Fisher Vector  B Lei, EL Tan, S Chen, L Zhuo, S Li, D Ni, T Wang
2015   Estimation of the Arterial Diameter in Ultrasound Images of the Common Carotid Artery  RM Menchón
2015   Cell recognition based on topological sparse coding for microscopy imaging of focused ultrasound treatment  Z Wang, J Zhu, Y Xue, C Song, N Bi
2014   Mapping between ultrasound and vowel speech using DNN framework  X Zheng, J Wei, W Lu, Q Fang, J Dang
2014   High-definition 3D Image Processing Technology for Ultrasound Diagnostic Scanners  M Ogino, T Shibahara, Y Noguchi, T Tsujita
2014   Fully automatic segmentation of ultrasound common carotid artery images based on machine learning  RM Menchón
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Deep Learning with FPGA

This chapter presents recent papers for using FPGAs (Field Programmable Gate Arrays) for Deep Learning. FPGAs can roughly be seen as a Software-configurable Hardware, i.e you in some cases get close to dedicated hardware speed (although typically at lower clock frequency than chips, but typically with strong on-FPGA parallelism), this can be a potential good fit for e.g. Convolutional Neural Networks since they require many convolutional layer calculations (with many convolutional filters per conv.layer) with large tensors. Recommend starting with having a look at Deep Learning on FPGAs: Past, Present, and Future

Best regards,

Amund Tveit

Convolutional Neural Network

  1. Throughput-Optimized OpenCL-based FPGA Accelerator for Large-Scale Convolutional Neural Networks
    – Authors: N Suda, V Chandra, G Dasika, A Mohanty, Y M (2016)
  2. Curbing the roofline: a scalable and flexible architecture for CNNs on FPGA
    – Authors: P Meloni, G Deriu, F Conti, I Loi, L Raffo, L Benini (2016)
  3. Comprehensive Evaluation of OpenCL-based Convolutional Neural Network Accelerators in Xilinx and Altera FPGAs
    – Authors: R Tapiador, A Rios (2016)
  4. Optimizing FPGA-based Accelerator Design for Deep Convolutional Neural Networks
    – Authors: C Zhang, P Li, G Sun, Y Guan, B Xiao, J Cong (2015)
  5. Musical notes classification with Neuromorphic Auditory System using FPGA and a Convolutional Spiking Network
    – Authors: E Cerezuela (2015)
  6. CNNLab: a Novel Parallel Framework for Neural Networks using GPU and FPGA-a Practical Study with Trade-off Analysis
    – Authors: M Zhu, L Liu, C Wang, Y Xie (2016)
  7. Energy-Efficient CNN Implementation on a Deeply Pipelined FPGA Cluster
    – Authors: C Zhang, D Wu, J Sun, G Sun, G Luo, J Cong (2016)


Other uses of FPGA in Deep Learning

  1. Deep Neural Network Architecture Implementation on FPGAs Using a Layer Multiplexing Scheme
    – Authors: F Ortega (2016)
  2. FPGA Based Multi-core Architectures for Deep Learning Networks
    – Authors: H Chen (2016)
  3. FPGA Implementation of a Scalable and Highly Parallel Architecture for Restricted Boltzmann Machines
    – Authors: K Ueyoshi, T Marukame, T Asai, M Motomura… (2016)
  4. DLAU: A Scalable Deep Learning Accelerator Unit on FPGA
    – Authors: C Wang, Q Yu, L Gong, X Li, Y Xie, X Zhou (2016)
  5. Deep Learning on FPGAs
    – Authors: Gj Lacey (2016)
  6. DNNWEAVER: From High-Level Deep Network Models to FPGA Acceleration
    – Authors: H Sharma, J Park, E Amaro, B Thwaites, P Kotha (2016)
  7. Handwritten Digit Classification on FPGA
    – Authors: K Kudrolli, S Shah, Dj Park (2016)
  8. Fpga Based Implementation of Deep Neural Networks Using On-chip Memory Only
    – Authors: J Park, W Sung (2016)
  9. FPGA Implementation of Autoencoders Having Shared Synapse Architecture
    – Authors: A Suzuki, T Morie, H Tamukoh (2016)
  10. Programming and Runtime Support to Blaze FPGA Accelerator Deployment at Datacenter Scale
    – Authors: M Huang, D Wu, Ch Yu, Z Fang, M Interlandi, T Condie… (2016)
  11. A Deep Learning Prediction Process Accelerator Based FPGA
    – Authors: Q Yu, C Wang, X Ma, X Li, X Zhou (2015)
  12. FPGA implementation of a Deep Belief Network architecture for character recognition using stochastic computation
    – Authors: K Sanni, G Garreau, Jl Molin, Ag Andreou (2015)
  13. An FPGA-Based Multiple-Weight-and-Neuron-Fault Tolerant Digital Multilayer Perceptron (Full Version)
    – Authors: T Horita, I Takanami, M Akiba, M Terauchi, T Kanno (2015)
  14. Efficient Generation of Energy and Performance Pareto Front for FPGA Designs
    – Authors: Sr Kuppannagari, Vk Prasanna (2015)
  15. Optimizing FPGA-based Accelerator Design for Deep Convolutional Neural Networks
    – Authors: C Zhang, P Li, G Sun, Y Guan, B Xiao, J Cong (2015)
  16. Musical notes classification with Neuromorphic Auditory System using FPGA and a Convolutional Spiking Network
    – Authors: E Cerezuela (2015)
  17. FPGA Acceleration of Recurrent Neural Network based Language Model
    – Authors: S Li, C Wu, Hh Li, B Li, Y Wang, Q Qiu (2015)
  18. FPGA emulation of a spike-based, stochastic system for real-time image dewarping
    – Authors: Jl Molin, T Figliolia, K Sanni, I Doxas, A Andreou… (2015)
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Deep Learning for Music

Deep Learning (creative AI) might potentially be used for music analysis and music creation. Deepmind’s Wavenet is a step in that direction. This blog post presents recent papers in Deep Learning for Music.

note: If you are curious about oversight of Deep Learning topics, please consider subscribing to my Deep Learning Newsletter at the end of this blog post.

Best regards,

Amund Tveit

Generation, Analysis and Segmentation of Music

  1. Deep Learning for Music
    – Authors: 
    A HuangR Wu (2016)
  2. Live Orchestral Piano, a system for real-time orchestral music generation
    – Authors: L CrestelP Esling (2016)
  3. Unsupervised feature learning for Music Structural Analysis
    – Authors: M BuccoliM ZanoniA SartiS TubaroD Andreoletti (2016)
  4. Music staff removal with supervised pixel classification
    – Authors: J Calvo (2016)
  5. An Intelligent Musical Rhythm Variation Interface
    – Authors: R VoglP Knees (2016)
  6. Probabilistic Segmentation of Musical Sequences using Restricted Boltzmann Machines
    – Authors: S LattnerM GrachtenK AgresCec Chacan (2015)
  7. Machine Learning Applied to Musical Improvisation
    – Authors: Rm Keller (2015)
  8. A Deep Neural Network for Modeling Music
    – Authors: P ZhangX ZhengW ZhangS LiS QianW (2015)
  9. Deep symbolic learning of multiple temporal granularities for musical orchestration.
    – Authors: F JacquemardG PeetersP E. (2015)
  10. Deep Karaoke: Extracting Vocals from Musical Mixtures Using a Convolutional Deep Neural Network
    – Authors: Ajr SimpsonG RomaMd Plumbley (2015)
  11. Deep Remix: Remixing Musical Mixtures Using a Convolutional Deep Neural Network
    – Authors: Ajr SimpsonG RomaMd Plumbley (2015)
  12. Analysis of musical structure: an approach based on deep learning
    – Authors: D Andreoletti (2015)
  13. An exploration of deep learning in content-based music informatics
    – Authors: Ej Humphrey (2015)
  14. Fusing Music and Video Modalities Using Multi-timescale Shared Representations
    – Authors: B XuX WangX Tang (2014)

Music Transcription

  1. An End-to-End Neural Network for Polyphonic Piano Music Transcription
    – Authors: S SigtiaE BenetosS Dixon (2016)
  2. Music transcription modelling and composition using deep learning
    – Authors: Bl SturmJf SantosO Ben (2016)
  3. Rewind: A Music Transcription Method
    – Authors: Cd Carthen (2016)
  4. An End-to-End Neural Network for Polyphonic Music Transcription
    – Authors: S SigtiaE BenetosS Dixon (2015)


  1. Novel Affective Features For Multiscale Prediction Of Emotion In Music
    – Authors: N KumarT GuhaCw HuangC VazSs Narayanan (2016)
  2. Demv-Matchmaker: Emotional Temporal Course Representation And Deep Similarity Matching For Automatic Music Video Generation 
    – Authors: Jc LinWl WeiHm Wang (2016)


  1. A Comparative Study on Music Genre Classification Algorithms
    – Authors: W Stokowiec (2016)
  2. Audio-Based Music Classification with A Pretrained Convolutional Network
    – Authors: E DielemanB Schrauwen (2016)
  3. Semantic Labeling of Music
    – Authors: P KneesM Schedl (2016)
  4. Classification of classic Turkish music makams by using deep belief networks
    – Authors: Mak SB Bolat (2016)
  5. Deep convolutional neural networks for predominant instrument recognition in polyphonic music
    – Authors: Y HanJ KimK Lee (2016)
  6. Deep learning, audio adversaries, and music content analysis
    – Authors: C KereliukBl SturmJ Larsen (2015)
  7. Speech Music Discrimination Using an Ensemble of Biased Classifiers
    – Authors: K KimA BaijalBs KoS LeeI HwangY Kim (2015)
  8. Deep Learning and Music Adversaries
    – Authors: C KereliukBl SturmJ Larsen (2015)
  9. Musical notes classification with Neuromorphic Auditory System using FPGA and a Convolutional Spiking Network
    – Authors: E Cerezuela (2015)
  10. A Deep Bag-of-Features Model for Music Auto-Tagging
    – Authors: J NamJ HerreraK Lee (2015)
  11. Music Genre Classification Using Convolutional Neural Network
    – Authors: Q KongX FengY Li (2014)
  12. An Associative Memorization Architecture of Extracted Musical Features from Audio Signals by Deep Learning Architecture
    – Authors: T NiwaK NaruseR OoeM KinoshitaT M (2014)
  13. Unsupervised Feature Pre-training of the Scattering Wavelet Transform for Musical Genre Recognition
    – Authors: M KD K (2014)

Search and Recommender Systems

  1. Deep learning for audio-based music recommendation
    – Authors: S Dieleman (2016)
  2. A compositional hierarchical model for music information retrieval
    – Authors: M PesekA LeonardisM Marolt (2014)

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Recommender Systems with Deep Learning

Update: 2017-Feb-03 – launched new service – (navigation and search in papers). Try e.g. out its Collaborative Filtering and Recommender pages.

This blog post presents recent research in Recommender Systems (/collaborative filtering) with Deep Learning. To get started I recommend having a look at A Survey and Critique of Deep Learning in Recommender Systems.

If you are curious about oversight of Deep Learning topics, please consider subscribing to my Deep Learning Newsletter at the end of this blog post.

Best regards,
Amund Tveit

Recommender Systems with Deep Learning

  1. Improving Scalability of Personalized Recommendation Systems for Enterprise Knowledge Workers
    – Authors: C Verma, M Hart, S Bhatkar, A Parker (2016)
  2. Multi-modal learning for video recommendation based on mobile application usage
    – Authors: X Jia, A Wang, X Li, G Xun, W Xu, A Zhang (2016)
  3. Collaborative Filtering with Stacked Denoising AutoEncoders and Sparse Inputs
    – Authors: F Strub, J Mary (2016)
  4. Applying Visual User Interest Profiles for Recommendation and Personalisation
    – Authors: J Zhou, R Albatal, C Gurrin (2016)
  5. Comparative Deep Learning of Hybrid Representations for Image Recommendations
    – Authors: C Lei, D Liu, W Li, Zj Zha, H Li (2016)
  6. Tag-Aware Recommender Systems Based on Deep Neural Networks
    – Authors: Y Zuo, J Zeng, M Gong, L Jiao (2016)
  7. Quote Recommendation in Dialogue using Deep Neural Network
    – Authors: H Lee, Y Ahn, H Lee, S Ha, S Lee (2016)
  8. Toward Fashion-Brand Recommendation Systems Using Deep-Learning: Preliminary Analysis
    – Authors: Y Wakita, K Oku, K Kawagoe (2016)
  9. Word embedding based retrieval model for similar cases recommendation
    – Authors: Y Zhao, J Wang, F Wang (2016)
  10. ConTagNet: Exploiting User Context for Image Tag Recommendation
    – Authors: Ys Rawat, Ms Kankanhalli (2016)
  11. Wide & Deep Learning for Recommender Systems
    – Authors: Ht Cheng, L Koc, J Harmsen, T Shaked, T Chandra… (2016)
  12. On Deep Learning for Trust-Aware Recommendations in Social Networks.
    – Authors: S Deng, L Huang, G Xu, X Wu, Z Wu (2016)
  13. A Survey and Critique of Deep Learning on Recommender Systems
    – Authors: L Zheng (2016)
  14. Collaborative Filtering and Deep Learning Based Hybrid Recommendation for Cold Start Problem
    – Authors: J Wei, J He, K Chen, Y Zhou, Z Tang (2016)
  15. Collaborative Filtering and Deep Learning Based Recommendation System For Cold Start Items
    – Authors: J Wei, J He, K Chen, Y Zhou, Z Tang (2016)
  16. Deep Neural Networks for YouTube Recommendations
    – Authors: P Covington, J Adams, E Sargin (2016)
  17. Towards Latent Context-Aware Recommendation Systems
    – Authors: M Unger, A Bar, B Shapira, L Rokach (2016)
  18. Automatic Recommendation Technology for Learning Resources with Convolutional Neural Network
    – Authors: X Shen, B Yi, Z Zhang, J Shu, H Liu (2016)
  19. Tag-Aware Personalized Recommendation Using a Deep-Semantic Similarity Model with Negative Sampling
    – Authors: Z Xu, C Chen, T Lukasiewicz, Y Miao, X Meng (2016)
  20. Latent Factor Representations for Cold-Start Video Recommendation
    – Authors: S Roy, Sc Guntuku (2016)
  21. Convolutional Matrix Factorization for Document Context-Aware Recommendation
    – Authors: D Kim, C Park, J Oh, S Lee, H Yu (2016)
  22. Conversational Recommendation System with Unsupervised Learning
    – Authors: Y Sun, Y Zhang, Y Chen, R Jin (2016)
  23. RecSys’ 16 Workshop on Deep Learning for Recommender Systems (DLRS)
    – Authors: A Karatzoglou, B Hidasi, D Tikk, O Sar (2016, Workshop proceedings)
  24. Ask the GRU: Multi-task Learning for Deep Text Recommendations
    – Authors: T Bansal, D Belanger, A Mccallum (2016)
  25. Recurrent Coevolutionary Latent Feature Processes for Continuous-Time Recommendation
    – Authors: H Dai, Y Wang, R Trivedi, L Song (2016)
  26. Keynote: Deep learning for audio-based music recommendation
    – Authors: S Dieleman (2016)
  27. Tumblr Blog Recommendation with Boosted Inductive Matrix Completion
    – Authors: D Shin, S Cetintas, Kc Lee, Is Dhillon (2015)
  28. Deep Collaborative Filtering via Marginalized Denoising Auto-encoder
    – Authors: S Li, J Kawale, Y Fu (2015)
  29. Learning Image and User Features for Recommendation in Social Networks
    – Authors: X Geng, H Zhang, J Bian, Ts Chua (2015)
  30. UCT-Enhanced Deep Convolutional Neural Network for Move Recommendation in Go
    – Authors: S Paisarnsrisomsuk (2015)
  31. A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems
    – Authors: A Elkahky, Y Song, X He (2015)
  32. It Takes Two to Tango: An Exploration of Domain Pairs for Cross-Domain Collaborative Filtering
    – Authors: S Sahebi, P Brusilovsky (2015)
  33. Latent Context-Aware Recommender Systems
    – Authors: M Unger (2015)
  34. Learning Distributed Representations from Reviews for Collaborative Filtering
    – Authors: A Almahairi, K Kastner, K Cho, A Courville (2015)
  35. A Collaborative Filtering Approach to Real-Time Hand Pose Estimation
    – Authors: C Choi, A Sinha, Jh Choi, S Jang, K Ramani (2015)
  36. Collaborative Deep Learning for Recommender Systems
    – Authors: H Wang, N Wang, Dy Yeung (2014)
  37. CARS2: Learning Context-aware Representations for Context-aware Recommendations
    – Authors: Y Shi, A Karatzoglou, L Baltrunas, M Larson, A Hanjalic (2014)
  38. Relational Stacked Denoising Autoencoder for Tag Recommendation
    – Authors: H Wang, X Shi, Dy Yeung (2014)

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Deep Learning for Alzheimer Diagnostics and Decision Support

Alzheimer’s Disease is the cause of 60-70% of cases of Dementia, costs associated to diagnosis, treatment and care of patients with it is estimated to be in the range of a hundred billion dollars in USA. This blog post have some recent papers related to using Deep Learning for diagnostics and decision support related to Alzheimer’s disease.

  1. Clinical decision support for Alzheimer’s disease based on deep learning and brain network
    – Authors: C Hu, R Ju, Y Shen, P Zhou, Q Li (2016)
  2. Classification of Alzheimer’s Disease using fMRI Data and Deep Learning Convolutional Neural Networks
    – Authors: S Sarraf, G Tofighi (2016)
  3. Non-Invasive Detection of Alzheimerâ s Disease-Multifractality of Emotional Speech
    – Authors: S Bhaduri, R Das, D Ghosh (2016)
  4. Alzheimer’s Disease Diagnostics by a Deeply Supervised Adaptable 3D Convolutional Network
    – Authors: E Hosseini (2016)
  5. Application of machine learning on postural control kinematics for the Diagnosis of Alzheimer’s disease
    – Authors: L Costa, Mf Gago, D Yelshyna, J Ferreira, Hd Silva… (2016)
  6. Multi-modality stacked deep polynomial network based feature learning for Alzheimer’s disease diagnosis
    – Authors: X Zheng, J Shi, Y Li, X Liu, Q Zhang (2016)
  7. Predicting Alzheimer’s disease: a neuroimaging study with 3D convolutional neural networks
    – Authors: A Payan, G Montana (2015)
  8. Linguistic Features Identify Alzheimer’s Disease in Narrative Speech
    – Authors: Kc Fraser, Ja Meltzer, F Rudzicz (2015)
  9. Detection of Alzheimer’s disease using group lasso SVM-based region selection
    – Authors: Z Sun, Y Fan, Bpf Lelieveldt, M Van De Giessen (2015)
  10. Anatomically Constrained Weak Classifier Fusion for Early Detection of Alzheimer’s Disease
    – Authors: D Domenger, P Coupé (2014)
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Zero-Shot (Deep) Learning

Zero-Shot Learning is making decisions after seing only one or few examples (as opposed to other types of learning that typically requires large amount of training examples). Recommend having a look at An embarrassingly simple approach to zero-shot learning first.

Best regards,

Amund Tveit

  1. Less is more: zero-shot learning from online textual documents with noise suppression
    – Authors: R Qiao, L Liu, C Shen, A Hengel (2016)
  2. Synthesized Classifiers for Zero-Shot Learning
    – Authors: S Changpinyo, Wl Chao, B Gong, F Sha (2016)
  3. Tinkering Under The Hood: Interactive Zero-Shot Learning with Pictorial Classifiers
    – Authors: V Krishnan (2016)
  4. Active Transfer Learning with Zero-Shot Priors: Reusing Past Datasets for Future Tasks
    – Authors: E Gavves, T Mensink, T Tommasi, Cgm Snoek… (2015)
  5. Transductive Multi-view Zero-Shot Learning
    – Authors: Y Fu, Tm Hospedales, T Xiang, S Gong (2015)
  6. Predicting Deep Zero-Shot Convolutional Neural Networks using Textual Descriptions
    – Authors: J Ba, K Swersky, S Fidler, R Salakhutdinov (2015)
  7. Zero-Shot Learning with Structured Embeddings
    – Authors: Z Akata, H Lee, B Schiele (2014)
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Embedding for NLP with Deep Learning

Word Embedding was introduced by Bengio in early 2000s, and interest in it really accelerated when Google presented Word2Vec in 2013.

This blog post has recent papers related to embedding for Natural Language Processing with Deep Learning. Example application areas embedding is used for in the papers include finance (stock market prediction), biomedical text analysis, part-of-speech tagging, sentiment analysis, pharmacology (drug adverse effects).

I recommend you to start with the paper: In Defense of Word Embedding for Generic Text Representation

Best regards,

Amund Tveit

  1. An approach to the use of word embeddings in an opinion classification task
    – Authors: F Enríquez, Ja Troyano, T López (2016)
  2. Learning Document Embeddings by Predicting N-grams for Sentiment Classification of Long Movie Reviews
    – Authors: B Li, T Liu, X Du, D Zhang, Z Zhao (2016)
  3. A Distributed Chinese Naive Bayes Classifier Based on Word Embedding
    – Authors: M Feng, G Wu (2016)
  4. An Empirical Study on Sentiment Classification of Chinese Review using Word Embedding
    – Authors: Y Lin, H Lei, J Wu, X Li (2015)
  5. Learning Bilingual Embedding Model for Cross-Language Sentiment Classification
    – Authors: X Tang, X Wan (2014)
  6. Training word embeddings for deep learning in biomedical text mining tasks
    – Authors: Z Jiang, L Li, D Huang, L Jin (2016)
  7. Learning Dense Convolutional Embeddings For Semantic Segmentation
    – Authors: Aw Harley, Kg Derpanis, I Kokkinos (2016)
  8. Creating Causal Embeddings for Question Answering with Minimal Supervision
    – Authors: R Sharp, M Surdeanu, P Jansen, P Clark, M Hammond (2016)
  9. Discriminative Phrase Embedding for Paraphrase Identification
    – Authors: W Yin, H Schütze (2016)
  10. Word embedding based retrieval model for similar cases recommendation
    – Authors: Y Zhao, J Wang, F Wang (2016)
  11. Learning Embeddings of API Tokens to Facilitate Deep Learning Based Program Processing
    – Authors: Y Lu, G Li, R Miao, Z Jin (2016)
  12. Locally Embedding Autoencoders: A Semi-Supervised Manifold Learning Approach of Document Representation
    – Authors: C Wei, S Luo, X Ma, H Ren, J Zhang, L Pan (2016)
  13. Deep Learning Architecture for Part-of-Speech Tagging with Word and Suffix Embeddings
    – Authors: A Popov (2016)
  14. Robobarista: Learning to Manipulate Novel Objects via Deep Multimodal Embedding
    – Authors: J Sung, Sh Jin, I Lenz, A Saxena (2016)
  15. Pruning subsequence search with attention-based embedding
    – Authors: C Raffel, Dpw Ellis (2016)
  16. Sentence Embedding Evaluation Using Pyramid Annotation
    – Authors: T Baumel, R Cohen, M Elhadad (2016)
  17. Deep Sentence Embedding Using the Long Short Term Memory Network: Analysis and Application to Information Retrieval
    – Authors: H Palangi, L Deng, Y Shen, J Gao, X He, J Chen… (2015)
  18. Feedback recurrent neural network-based embedded vector and its application in topic model
    – Authors: L Li, S Gan, X Yin (2016)
  19. Enhancing Sentence Relation Modeling with Auxiliary Character-level Embedding
    – Authors: P Li, H Huang (2016)
  20. Learning Word Meta-Embeddings by Using Ensembles of Embedding Sets
    – Authors: W Yin, H Schütze (2015)
  21. Syntax-Aware Multi-Sense Word Embeddings for Deep Compositional Models of Meaning
    – Authors: J Cheng, D Kartsaklis (2015)
  22. A Deep Embedding Model for Co-occurrence Learning
    – Authors: Y Shen, R Jin, J Chen, X He, J Gao, L Deng (2015)
  23. Jointly Modeling Embedding and Translation to Bridge Video and Language
    – Authors: Y Pan, T Mei, T Yao, H Li, Y Rui (2015)
  24. Learning semantic word embeddings based on ordinal knowledge constraints
    – Authors: Q Liu, H Jiang, S Wei, Zh Ling, Y Hu (2015)
  25. Boosting Named Entity Recognition with Neural Character Embeddings
    – Authors: C Dos Santos, V Guimaraes, Rj Niterói, R De Janeiro (2015)
  26. Evaluating word embeddings and a revised corpus for part-of-speech tagging in Portuguese
    – Authors: Er Fonseca, Jlg Rosa, Sm Aluísio (2015)
  27. Temporal Embedding in Convolutional Neural Networks for Robust Learning of Abstract Snippets
    – Authors: J Liu, K Zhao, B Kusy, J Wen, R Jurdak (2015)
  28. Learning Feature Hierarchies: A Layer-wise Tag-embedded Approach
    – Authors: Z Yuan, C Xu, J Sang, S Yan, M Hossain (2015)
  29. Multi-Source Bayesian Embeddings for Learning Social Knowledge Graphs
    – Authors: Z Yang, J Tang (2015)
  30. PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks
    – Authors: J Tang, M Qu, Q Mei (2015)
  31. Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features
    – Authors: A Nikfarjam, A Sarker, K O’Connor, R Ginn, G Gonzalez (2015)
  32. Projective Label Propagation by Label Embedding
    – Authors: Z Zhang, W Jiang, F Li, L Zhang, M Zhao, L Jia (2015)
  33. An Investigation of Neural Embeddings for Coreference Resolution
    – Authors: V Godbole, W Liu, R Togneri (2015)
  34. Deep Multimodal Embedding: Manipulating Novel Objects with Point-clouds, Language and Trajectories
    – Authors: J Sung, I Lenz, A Saxena (2015)
  35. AutoExtend: Extending Word Embeddings to Embeddings for Synsets and Lexemes
    – Authors: S Rothe, H Schütze (2015)
  36. Deep Multilingual Correlation for Improved Word Embeddings
    – Authors: A Lu, W Wang, M Bansal, K Gimpel, K Livescu (2015)
  37. Leverage Financial News to Predict Stock Price Movements Using Word Embeddings and Deep Neural Networks
    – Authors: Y Peng, H Jiang (2015)
  38. The Impact of Structured Event Embeddings on Scalable Stock Forecasting Models
    – Authors: Jb Nascimento, M Cristo (2015)
  39. Representing Text for Joint Embedding of Text and Knowledge Bases
    – Authors: K Toutanova, D Chen, P Pantel, H Poon, P Choudhury… (2015)
  40. In Defense of Word Embedding for Generic Text Representation
    – Authors: G Lev, B Klein, L Wolf (2015)
  41. Learning Multi-Relational Semantics Using Neural-Embedding Models
    – Authors: B Yang, W Yih, X He, J Gao, L Deng (2014)
  42. Improving relation descriptor extraction with word embeddings and cluster features
    – Authors: T Liu, M Li (2014)
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Deep Learning in combination with EEG electrical signals from the brain


EEG (Electroencephalography) is the measurement of electrical signals in the brain. It has long been used for medical purposes (e.g. diagnosis of epilepsy), and has in more recent years also been used in Brain Computer Interfaces (BCI) – note: if BCI is new to you don’t get overly excited about it, since these interfaces are still in my opinion quite premature. But they are definitely interesting in a longer term perspective .

This blog post gives an overview of recent research on Deep Learning in combination with EEG, e.g. r for classification, feature representation, diagnosis, safety (cognitive state of drivers) and hybrid methods (Computer Vision or Speech Recognition together with EEG and Deep Learning).

Best regards,

Amund Tveit

Diagnosis and Medicine

  1. Classification of Epileptic EEG Signals with Stacked Sparse Autoencoder Based on Deep Learning
    – Authors: Q Lin, S Ye, X Huang, S Li, M Zhang, Y Xue, Ws Chen (2016)
  2. Advanced use of EEG in drug development and personalized medicine
    – Authors: S Simpraga, R Alvarez (2016)
  3. Low-complexity algorithms for automatic detection of sleep stages and events for use in wearable EEG systems
    – Authors: Sa Imtiaz (2016)
  4. Predicting epileptic seizure from Electroencephalography (EEG) using hilbert huang transformation and neural network
    – Authors: Mo Rahman, Mn Karim (2015)
  5. Multi-Channel EEG based Sleep Stage Classification with Joint Collaborative Representation and Multiple Kernel Learning
    – Authors: J Shi, X Liu, Y Li, Q Zhang, Y Li, S Yin (2015)
  6. Superchords: decoding EEG signals in the millisecond range
    – Authors: R Normand, Ha Ferreira (2015)
  7. A novel motor imagery EEG recognition method based on deep learning
    – Authors: M Li, M Zhang, Y Sun (2016)

Brain Computer Interfaces

  1. Convolutional Networks for EEG Signal Classification in Non-Invasive Brain-Computer Interfaces
    – Authors: E Forney, C Anderson, W Gavin, P Davies (2016)
  2. Using Deep Learning for Human Computer Interface via Electroencephalography
    – Authors: S Redkar (2016)
  3. Deep learning EEG response representation for brain computer interface
    – Authors: L Jingwei, C Yin, Z Weidong (2015)

Cognition and Emotion

  1. Single-channel EEG-based mental fatigue detection based on deep belief network
    – Authors: P Li, W Jiang, F Su (2016)
  2. EEG-based prediction of driver’s cognitive performance by deep convolutional neural network
    – Authors: M Hajinoroozi, Z Mao, Tp Jung, Ct Lin, Y Huang (2016)
  3. EEG-based Driver Fatigue Detection using Hybrid Deep Generic Model
    – Authors: Pp San, Sh Ling, R Chai, Y Tran, A Craig, Ht Nguyen (2016)
  4. Mental State Recognition via Wearable EEG
    – Authors: P Bashivan, I Rish, S Heisig (2016)
  5. Single trial prediction of normal and excessive cognitive load through EEG feature fusion
    – Authors: P Bashivan, M Yeasin, Gm Bidelman (2016)
  6. Prediction of driver’s drowsy and alert states from EEG signals with deep learning
    – Authors: M Hajinoroozi, Z Mao, Y Huang (2016)
  7. Investigating Critical Frequency Bands and Channels for EEG-based Emotion Recognition with Deep Neural Networks
    – Authors: Wl Zheng, Bl Lu (2015)
  8. Deep learninig of EEG signals for emotion recognition
    – Authors: Y Gao, Hj Lee, Rm Mehmood (2015)
  9. EEG Based Emotion Identification Using Unsupervised Deep Feature Learning
    – Authors: X Li, P Zhang, D Song, G Yu, Y Hou, B Hu (2015)
  10. Feature extraction with deep belief networks for driver’s cognitive states prediction from EEG data
    – Authors: M Hajinoroozi, Tp Jung, Ct Lin, Y Huang (2015)
  11. Measurement Of Stress Intensity Using Eeg
    – Authors: V Tóth (2015)
  12. Revealing critical channels and frequency bands for emotion recognition from EEG with deep belief network
    – Authors: Wl Zheng, Ht Guo, Bl Lu (2015)
  13. Interpretable Deep Neural Networks for Single-Trial EEG Classification
    – Authors: I Sturm, S Bach, W Samek, Kr Müller (2016)

Hybrid methods – combining Deep Learning and EEG with other Deep Learning methods

  1. Improving Electroencephalography-Based Imagined Speech Recognition with a Simultaneous Video Data Stream
    – Authors: Sj Stolze (2016)
  2. A closed-loop system for rapid face retrieval by combining EEG and computer vision
    – Authors: Y Wang, L Jiang, B Cai, Y Wang, S Zhang, X Zheng (2015)
  3. Emotional Affect Estimation Using Video and EEG Data in Deep Neural Networks
    – Authors: A Frydenlund, F Rudzicz (2015)

Feature representation and Classification

  1. Improving EEG feature learning via synchronized facial video
    – Authors: X Li, X Jia, G Xun, A Zhang (2016)
  2. Decoding EEG and LFP signals using deep learning: heading TrueNorth
    – Authors: E Nurse, Bs Mashford, Aj Yepes, I Kiral (2016)
  3. Fast and efficient rejection of background waveforms in interictal EEG
    – Authors: E Bagheri, J Jin, J Dauwels, S Cash, Mb Westover (2016)
  4. Class-wise Deep Dictionaries for EEG Classification
    – Authors: P Khurana, A Majumdar, R Ward (2016)
  5. EEG-based affect states classification using Deep Belief Networks
    – Authors: H Xu, Kn Plataniotis (2016)
  6. Feature Learning from Incomplete EEG with Denoising Autoencoder
    – Authors: J Li, Z Struzik, L Zhang, A Cichocki (2014)
  7. Joint optimization of algorithmic suites for EEG analysis
    – Authors: E Santana, Aj Brockmeier, Jc Principe (2014)
  8. Deep Extreme Learning Machine and Its Application in EEG Classification
    – Authors: S Ding, N Zhang, X Xu, L Guo, J Zhang (2014)
  9. Deep Feature Learning for EEG Recordings
    – Authors: S Stober, A Sternin, Am Owen, Ja Grahn (2015)
  10. A Multichannel Deep Belief Network for the Classification of EEG Data
    – Authors: Am Al (2015)
  11. Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks
    – Authors: P Bashivan, I Rish, M Yeasin, N Codella (2015)
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Regularized Deep Networks – ICLR 2017 Discoveries

This blog post gives an overview of papers related to using Regularization in Deep Learning submitted to ICLR 2017, see underneath for the list of papers. If you want to learn about Regularization in Deep Learning check out:

  1. Mode Regularized Generative Adversarial Networks – Authors: Tong Che, Yanran Li, Athul Jacob, Yoshua Bengio, Wenjie Li
  2. Representation Stability as a Regularizer for Neural Network Transfer Learning – Authors: Matthew Riemer, Elham Khabiri, Richard Goodwin
  3. Neural Causal Regularization under the Independence of Mechanisms Assumption – Authors: Mohammad Taha Bahadori, Krzysztof Chalupka, Edward Choi, Walter F. Stewart, Jimeng Sun
  4. Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations – Authors: David Krueger, Tegan Maharaj, Janos Kramar, Mohammad Pezeshki, Nicolas Ballas, Nan Rosemary Ke, Anirudh  Goyal, Yoshua Bengio, Aaron Courville, Christopher Pal
  5. Bridging Nonlinearities and Stochastic Regularizers with Gaussian Error Linear Units – Authors: Dan Hendrycks, Kevin Gimpel
  6. Regularizing CNNs with Locally Constrained Decorrelations – Authors: Pau Rodríguez, Jordi Gonzàlez, Guillem Cucurull, Josep M. Gonfaus, Xavier Roca
  7. Regularizing Neural Networks by Penalizing Confident Output Distributions – Authors: Gabriel Pereyra, George Tucker, Jan Chorowski, Lukasz Kaiser, Geoffrey Hinton
  8. Multitask Regularization for Semantic Vector Representation of Phrases – Authors: Xia Song, Saurabh Tiwary \& Rangan Majumdar
  9. (F)SPCD: Fast Regularization of PCD by Optimizing Stochastic ML Approximation under Gaussian Noise – Authors: Prima Sanjaya, Dae-Ki Kang
  10. Crossmap Dropout : A Generalization of Dropout Regularization in Convolution Level – Authors: Alvin Poernomo, Dae-Ki Kang
  11. Non-linear Dimensionality Regularizer for Solving Inverse Problems – Authors: Ravi Garg, Anders Eriksson, Ian Reid
  12. Support Regularized Sparse Coding and Its Fast Encoder – Authors: Yingzhen Yang, Jiahui Yu, Pushmeet Kohli, Jianchao Yang, Thomas S. Huang
  13. An Analysis of Feature Regularization for Low-shot Learning – Authors: Zhuoyuan Chen, Han Zhao, Xiao Liu, Wei Xu
  14. Dropout with Expectation-linear Regularization – Authors: Xuezhe Ma, Yingkai Gao, Zhiting Hu, Yaoliang Yu, Yuntian Deng, Eduard Hovy
  15. SoftTarget Regularization: An Effective Technique to Reduce Over-Fitting in Neural Networks – Authors: Armen Aghajanyan
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Unsupervised Deep Learning – ICLR 2017 Discoveries

This blog post gives an overview of papers related to Unsupervised Deep Learning submitted to ICLR 2017, see underneath for the list of papers. If you want to learn about Unsupervised Deep Learning check out: Ruslan Salkhutdinov’s video Foundations of Unsupervised Deep Learning.

Best regards,
Amund Tveit

  1. Unsupervised Learning Using Generative Adversarial Training And Clustering – Authors: Vittal Premachandran, Alan L. Yuille
  2. An Information-Theoretic Framework for Fast and Robust Unsupervised Learning via Neural Population Infomax – Authors: Wentao Huang, Kechen Zhang
  3. Unsupervised Cross-Domain Image Generation – Authors: Yaniv Taigman, Adam Polyak, Lior Wolf
  4. Unsupervised Perceptual Rewards for Imitation Learning – Authors: Pierre Sermanet, Kelvin Xu, Sergey Levine
  5. Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning – Authors: William Lotter, Gabriel Kreiman, David Cox
  6. Unsupervised sentence representation learning with adversarial auto-encoder – Authors: Shuai Tang, Hailin Jin, Chen Fang, Zhaowen Wang
  7. Unsupervised Program Induction with Hierarchical Generative Convolutional Neural Networks – Authors: Qucheng Gong, Yuandong Tian, C. Lawrence Zitnick
  8. Generalizable Features From Unsupervised Learning – Authors: Mehdi Mirza, Aaron Courville, Yoshua Bengio
  9. Reinforcement Learning with Unsupervised Auxiliary Tasks – Authors: Max Jaderberg, Volodymyr Mnih, Wojciech Marian Czarnecki, Tom Schaul, Joel Z Leibo, David Silver, Koray Kavukcuoglu
  10. Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data – Authors: Maximilian Karl, Maximilian Soelch, Justin Bayer, Patrick van der Smagt
  11. Unsupervised Learning of State Representations for Multiple Tasks – Authors: Antonin Raffin, Sebastian Höfer, Rico Jonschkowski, Oliver Brock, Freek Stulp
  12. Unsupervised Pretraining for Sequence to Sequence Learning – Authors: Prajit Ramachandran, Peter J. Liu, Quoc V. Le
  13. Unsupervised Deep Learning of State Representation Using Robotic Priors – Authors: Timothee LESORT, David FILLIAT
  14. Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders – Authors: Nat Dilokthanakul, Pedro A. M. Mediano, Marta Garnelo, Matthew C.H. Lee, Hugh Salimbeni, Kai Arulkumaran, Murray Shanahan
  15. Deep unsupervised learning through spatial contrasting – Authors: Elad Hoffer, Itay Hubara, Nir Ailon

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