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Added 152 new Deep Learning Publications to the Bibliography

Added 152 new Deep Learning papers to the Deeplearning.University Bibliography, if you want to see them separate from the previous papers in the bibliography the new ones are listed below. There are many very interesting papers, e.g. in the medicine (e.g. deep learning for cancer-related analysis such as mammogram and pancreas cancer, and heart diseases), in addition to the social network category as shown here:

Deep Learning for Medicine

  1. A proteomic signature predicts response to a therapeutic vaccine in pancreas cancer; analysis from the Gi-4000-02 trial
  2. Fully automatic segmentation of ultrasound common carotid artery images based on machine learning
  3. Automatic vertebrae localization, identification, and segmentation using deep learning and statistical models
  4. Automatic Vaginal Bacteria Segmentation and Classification Based on Superpixel and Deep Learning
  5. Predicting Likelihood of Hospitalization with Subset-Filtered Emerging Patterns
  6. Deep Structured learning for mass segmentation from Mammograms

Deep Learning for Social Networks

  1. Deep learning-based target customer position extraction on social network
  2. Recursive Deep Learning for Sentiment Analysis over Social Data

(Complete Bibliography – at Deeplearning.University)

Disclaimer: we’re so far primarily covering 2014 deep learning papers, so still far from a complete bibliography wrt to older papers, but our goal is to come closer eventually. If you want to contribute please see the corresponding github repo for details:

REPO URL: github.com/memkite/DeepLearningBibliography

Best regards,

Amund Tveit (Memkite Deep Learning Team)

Links to Deep Learning Subtopics

[3d] [activity recognition] [algorithm] [algorithms] [applications] [architecture] [asthma] [asynchronous][auto-encoder] [autoencoder] [bacteria] [big data] [bing challenge] [bioinformatics] [cancer] [challenges] [character recognition] [clustering] [collaborative filtering] [convolutional network] [convolutional neural network] [deep belief network] [deep neural network] [diabetes] [dropout] [emotion] [estimation] [experimental] [eye detection] [face detection] [face expression analysis] [face recognition] [feature extraction] [food detection] [game] [gesture recognition] [gpu] [hardware] [hashing] [heart failure] [image recognition] [image recognitionx] [image segmentation] [information retrieval] [machine translation] [mammogram analysis] [manufacturing] [medicine] [mobile] [music] [natural language processing] [noise] [online learning] [open source] [optimization] [parallelization] [platform] [recurrant neural networks] [recurrent neural networks] [restricted boltzmann machine] [restricted boltzmann machines] [search] [semantix indexing] [sentiment analysis] [social network] [sosial network] [spam] [speech recognition] [stochastic gradient] [stochastic gradient descent] [subspace learning] [survey] [theory] [time series] [tongue] [tool] [ultrasound] [vehicle classification] [vehicle classificationx][web mining]

3D

  1. Indirect shape analysis for 3d shape retrieval
  2. Supervised feature learning via â„“2-norm regularized logistic regression for 3d object recognition
  3. A 3d model recognition mechanism based on deep boltzmann machines
  4. Combining heterogenous features for 3d hand-held object recognition

 

Activity Recognition

  1. Proposal for a Deep Learning Architecture for Activity Recognition

 

Algorithm

  1. Enhanced Higgs to $\ tau^+\ tau^-$ Searches with Deep Learning
  2. A Simple Stochastic Algorithm for Structural Features Learning
  3. Meta-parameter free unsupervised sparse feature learning
  4. On the Link Between Gaussian Homotopy Continuation and Convex Envelopes
  5. Coarse-to-Fine Minimization of Some Common Nonconvexities
  6. A Method For Extracting Information From The Web Using Deep Learning Algorithm
  7. Adaptive Information-Theoretical Feature Selection for Pattern Classification
  8. Stacked Extreme Learning Machines
  9. The atoms of neural computation
  10. On The Dynamical Nature Of Computation
  11. Greedy Approaches to Semi-Supervised Subspace Learning
  12. Low Rank Tensor Manifold Learning
  13. Random feedback weights support learning in deep neural networks
  14. Temporal Dropout of Changes Approach to Convolutional Learning of Spatio-Temporal Features
  15. A new algorithm on variable-rate convolutional broadcast for network coding in cyclic networks
  16. Identification and Elucidation of Expression Quantitative Trait Loci (eQTL) and their regulating mechanisms using Decodive Deep Learning
  17. Dynamic Background Learning through Deep Auto-encoder Networks

 

Algorithms

  1. Margin Perceptrons for Graphs

 

Applications

  1. Non-parametric Bayesian Learning with Deep Learning Structure and Its Applications in Wireless Networks
  2. Log-Linear Models, Extensions and Applications

 

Architecture

  1. Unsupervised Feature Learning For Bootleg Detection Using Deep Learning Architectures
  2. Proposal for a Deep Learning Architecture for Activity Recognition

 

Asthma

  1. Predicting Likelihood of Hospitalization with Subset-Filtered Emerging Patterns

 

Asynchronous

  1. Improving training time of deep neural networkwith asynchronous averaged stochastic gradient descent

 

Audio

  1. Automated intelligent system for sound signalling device quality assurance
  2. Neural Network Based Pitch Tracking In Very Noisy Speech
  3. Prosodic, spectral and voice quality feature selection using a long-term stopping criterion for audio-based emotion recognition

 

Auto-Encoder

  1. Dynamic Background Learning through Deep Auto-encoder Networks

 

Autoencoder

  1. Recognition of Handwritten Characters in Chinese Legal Amounts by Stacked Autoencoders
  2. Improving generation performance of speech emotion recognition by denoising autoencoders
  3. Stacked Extreme Learning Machines
  4. Cross-modal Retrieval with Correspondence Autoencoder
  5. Auto-encoding Variational Bayes

 

Bacteria

  1. Automatic Vaginal Bacteria Segmentation and Classification Based on Superpixel and Deep Learning

 

Big Data

  1. Big Data Opportunities and Challenges: Discussions from Data Analytics Perspectives [Discussion Forum]
  2. Tensor index for large scale image retrieval
  3. Parallel deep neural network training for big data on blue gene/Q

 

Bing Challenge

  1. Cross Modal Deep Model and Gaussian Process Based Model for MSR-Bing Challenge

 

Bioinformatics

  1. A generative model of identifying informative proteins from dynamic Ppi networks
  2. Dann: a deep learning approach for annotating the pathogenicity of genetic variants
  3. Parallel deep neural network training for big data on blue gene/Q

 

Cancer

  1. A proteomic signature predicts response to a therapeutic vaccine in pancreas cancer; analysis from the Gi-4000-02 trial

 

Challenges

  1. Big Data Opportunities and Challenges: Discussions from Data Analytics Perspectives [Discussion Forum]

 

Character Recognition

  1. A Study of Designing Compact Classifiers using Deep Neural Networks for Online Handwritten Chinese Character Recognition
  2. Recognition of Handwritten Characters in Chinese Legal Amounts by Stacked Autoencoders

 

Clustering

  1. Ghent University-iMinds at MediaEval 2014 Diverse Images: Adaptive Clustering with Deep Features
  2. Deep Embedding Network for Clustering

 

Collaborative Filtering

  1. Implicitly Learning a User Interest Profile for Personalization of Web Search Using Collaborative Filtering

 

Convolutional Network

  1. Look Closely: Learning Exemplar Patches for Recognizing Textiles from Product Images
  2. Cross Dataset Person Re-identification
  3. Recod at MediaEval 2014: Violent Scenes Detection Task
  4. Learning Convolutional NonLinear Features for K Nearest Neighbor Image Classification
  5. Early Hierarchical Contexts Learned by Convolutional Networks for Image Segmentation
  6. Deep Multimodal Fusion: Combining Discrete Events and Continuous Signals
  7. A new algorithm on variable-rate convolutional broadcast for network coding in cyclic networks

 

Convolutional Neural Network

  1. Convolutional Neural Networks for Human Activity Recognition using Mobile Sensors
  2. Raw Speech Signal-based Continuous Speech Recognition using Convolutional Neural Networks
  3. Music Genre Classification Using Convolutional Neural Network
  4. Vehicle Type Classification Using Unsupervised Convolutional Neural Network
  5. Cascaded Convolutional Neural Network for Eye Detection Under Complex Scenarios
  6. Vehicle Type Classification Using Semi-Supervised Convolutional Neural Network
  7. Deep Convolutional Neural Network for Image Deconvolution
  8. DeepSentiBank: Visual Sentiment Concept Classification with Deep Convolutional Neural Networks
  9. Food Detection and Recognition Using Convolutional Neural Network
  10. Error-Driven Incremental Learning in Deep Convolutional Neural Network for Large-Scale Image Classification

 

Deep Belief Network

  1. Deep Adaptive Networks for Visual Data Classification
  2. Learning Local Receptive Fields in Deep Belief Networks for Visual Feature Detection
  3. Deep learning-based target customer position extraction on social network
  4. Deep belief network based Crf for spoken language understanding

 

Deep Neural Network

  1. Deep Neural Networks For Spoken Dialog Systems
  2. Parallel deep neural network training for big data on blue gene/Q
  3. A Study of Designing Compact Classifiers using Deep Neural Networks for Online Handwritten Chinese Character Recognition
  4. Acoustic emotion recognition using deep neural network
  5. Research on deep neural network’s hidden layers in phoneme recognition
  6. Cross-language transfer learning for deep neural network based speech enhancement
  7. Improving training time of deep neural networkwith asynchronous averaged stochastic gradient descent
  8. Multiple time-span feature fusion for deep neural network modeling
  9. Investigation of stochastic Hessian-Free optimization in Deep neural networks for speech recognition
  10. TANDEM-bottleneck feature combination using hierarchical Deep Neural Networks
  11. Random feedback weights support learning in deep neural networks

 

Diabetes

  1. Predicting Likelihood of Hospitalization with Subset-Filtered Emerging Patterns

 

Dropout

  1. Temporal Dropout of Changes Approach to Convolutional Learning of Spatio-Temporal Features

 

Emotion

  1. Prosodic, spectral and voice quality feature selection using a long-term stopping criterion for audio-based emotion recognition
  2. Acoustic emotion recognition using deep neural network
  3. Improving generation performance of speech emotion recognition by denoising autoencoders
  4. Multi-scale Temporal Modeling for Dimensional Emotion Recognition in Video
  5. Speech Emotion Recognition Using Cnn

 

Estimation

  1. Nice: Non-linear Independent Components Estimation

 

Experimental

  1. Shallow Classification or Deep Learning: An Experimental Study

 

Eye Detection

  1. Eye gaze for spoken language understanding in multi-modal conversational interactions
  2. Smartphone based visible iris recognition using deep sparse filtering
  3. Cascaded Convolutional Neural Network for Eye Detection Under Complex Scenarios

 

Face Detection

  1. Deep Metric Learning for Person Re-Identification
  2. Cross Dataset Person Re-identification

 

Face Expression Analysis

  1. Facial Expression Analysis Based on High Dimensional Binary Features

 

Face Recognition

  1. Extended Supervised Descent Method for Robust Face Alignment
  2. Facial Expression Analysis Based on High Dimensional Binary Features
  3. DeepReID: Deep Filter Pairing Neural Network for Person Re-Identification
  4. Discriminative Deep Face Shape Model for Facial Point Detection
  5. Learning Compact Face Representation: Packing a Face into an int32

 

Feature Extraction

  1. An exact mapping between the Variational Renormalization Group and Deep Learning
  2. Meta-parameter free unsupervised sparse feature learning
  3. High-Order Semi-RBMs and Deep Gated Neural Networks for Feature Interaction Identification and Non-Linear Semantic Indexing
  4. Unsupervised Feature Learning For Bootleg Detection Using Deep Learning Architectures
  5. Learning Local Receptive Fields in Deep Belief Networks for Visual Feature Detection
  6. Recod at MediaEval 2014: Violent Scenes Detection Task
  7. A Method For Extracting Information From The Web Using Deep Learning Algorithm
  8. Feature Mapping of Multiple Beamformed Sources for Robust Overlapping Speech Recognition Using a Microphone Array
  9. Learning Multiple Complex Features Based on Classification Results
  10. Multiple time-span feature fusion for deep neural network modeling

 

Food Detection

  1. Food Detection and Recognition Using Convolutional Neural Network

 

Game

  1. Deep learning for real-time Atari game play using offline Monte-Carlo tree search planning

 

Gesture Recognition

  1. Feature learning based on Sae-pca network for Human gesture recognition in Rgbd images

 

Gpu

  1. Introducing CURRENNT–the Munich open-source Cuda RecurREnt Neural Network Toolkit
  2. Gaussian Process Models with Parallelization and Gpu acceleration
  3. Query Optimization in Heterogeneous Cpu/gpu Environment for Time Series Databases

 

Hardware

  1. Performance Prediction by Deep Learning Methods for Semiconductor Manufacturing

 

Hashing

  1. Inductive Transfer Deep Hashing for Image Retrieval
  2. Cross-Media Hashing with Neural Networks

 

Heart Failure

  1. Predicting Likelihood of Hospitalization with Subset-Filtered Emerging Patterns

 

Image Recognition

  1. Tensor index for large scale image retrieval
  2. Indirect shape analysis for 3d shape retrieval
  3. Convolutional Neural Networks for Human Activity Recognition using Mobile Sensors
  4. A Simple Stochastic Algorithm for Structural Features Learning
  5. Deep supervised, but not unsupervised, models may explain It cortical representation
  6. Fully automatic segmentation of ultrasound common carotid artery images based on machine learning
  7. Automatic vertebrae localization, identification, and segmentation using deep learning and statistical models
  8. Deep Adaptive Networks for Visual Data Classification
  9. Coarse-to-Fine Minimization of Some Common Nonconvexities
  10. Multiple Spatio-Temporal Scales Neural Network for Contextual Visual Recognition of Human Actions
  11. Ghent University-iMinds at MediaEval 2014 Diverse Images: Adaptive Clustering with Deep Features
  12. Statistically Adaptive Image Denoising Based on Overcomplete Topographic Sparse Coding
  13. Nonlinear Supervised Locality Preserving Projections for Visual Pattern Discrimination
  14. Supervised feature learning via â„“2-norm regularized logistic regression for 3d object recognition
  15. Adaptive learning in a compartmental model of visual cortex-how feedback enables stable category learning and refinement
  16. Semi-Supervised Learning for Rgb-d Object Recognition
  17. Efficient image representation for object recognition via pivots selection
  18. Regularized Hierarchical Feature Learning with Non-Negative Sparsity and Selectivity for Image Classification
  19. Learning deep dynamical models from image pixels
  20. DeepReID: Deep Filter Pairing Neural Network for Person Re-Identification
  21. Towards a Visual Turing Challenge
  22. Semantic parsing for priming object detection in indoors Rgb-d scenes
  23. Combining heterogenous features for 3d hand-held object recognition
  24. Rapid: Rating Pictorial Aesthetics using Deep Learning
  25. Fused one-vs-all mid-level features for fine-grained visual categorization
  26. DeepSentiBank: Visual Sentiment Concept Classification with Deep Convolutional Neural Networks
  27. Cross Modal Deep Model and Gaussian Process Based Model for MSR-Bing Challenge
  28. Inductive Transfer Deep Hashing for Image Retrieval
  29. Error-Driven Incremental Learning in Deep Convolutional Neural Network for Large-Scale Image Classification

 

Image Recognitionx

  1. Deep Convolutional Neural Network for Image Deconvolution

 

Image Segmentation

  1. Early Hierarchical Contexts Learned by Convolutional Networks for Image Segmentation

 

Information Retrieval

  1. A compositional hierarchical model for music information retrieval

 

Machine Translation

  1. Deep Learning for Natural Language Processing and Machine Translation

 

Mammogram Analysis

  1. Deep Structured learning for mass segmentation from Mammograms

 

Manufacturing

  1. Performance Prediction by Deep Learning Methods for Semiconductor Manufacturing

 

Medicine

  1. A proteomic signature predicts response to a therapeutic vaccine in pancreas cancer; analysis from the Gi-4000-02 trial
  2. Fully automatic segmentation of ultrasound common carotid artery images based on machine learning
  3. Automatic vertebrae localization, identification, and segmentation using deep learning and statistical models
  4. Testing AutoTrace: A machine-learning approach to automated tongue contour data extraction
  5. Automatic Vaginal Bacteria Segmentation and Classification Based on Superpixel and Deep Learning
  6. Predicting Likelihood of Hospitalization with Subset-Filtered Emerging Patterns
  7. Deep Structured learning for mass segmentation from Mammograms

 

Mobile

  1. Convolutional Neural Networks for Human Activity Recognition using Mobile Sensors
  2. Smartphone based visible iris recognition using deep sparse filtering
  3. Non-parametric Bayesian Learning with Deep Learning Structure and Its Applications in Wireless Networks

 

Music

  1. A compositional hierarchical model for music information retrieval
  2. Fusing Music and Video Modalities Using Multi-timescale Shared Representations

 

Natural Language Processing

  1. Coupled Projections for Semi-supervised Adaptation of Dictionaries
  2. Automatic Arabic diacritics restoration based on deep nets
  3. Transduction Recursive Auto-Associative Memory: Learning Bilingual Compositional Distributed Vector Representations of Inversion Transduction Grammars
  4. Learning Bilingual Embedding Model for Cross-Language Sentiment Classification
  5. Fuzzy Subjective Sentiment Phrases: A Context Sensitive and Self-Maintaining Sentiment Lexicon
  6. Deep Learning for Natural Language Processing and Machine Translation

 

Noise

  1. Regularizing Recurrent Networks-On Injected Noise and Norm-based Methods
  2. Statistically Adaptive Image Denoising Based on Overcomplete Topographic Sparse Coding

 

Online Learning

  1. Error-Driven Incremental Learning in Deep Convolutional Neural Network for Large-Scale Image Classification

 

Open Source

  1. Introducing CURRENNT–the Munich open-source Cuda RecurREnt Neural Network Toolkit

 

Optimization

  1. Query Optimization in Heterogeneous Cpu/gpu Environment for Time Series Databases

 

Parallelization

  1. Gaussian Process Models with Parallelization and Gpu acceleration

 

Platform

  1. Minerva: A Scalable and Highly Efficient Training Platform for Deep Learning

 

Recurrant Neural Networks

  1. Regularizing Recurrent Networks-On Injected Noise and Norm-based Methods

 

Recurrent Neural Networks

  1. Learning to Execute
  2. Conditional Computation in Deep and Recurrent Neural Networks

 

Restricted Boltzmann Machine

  1. Classification Restricted Boltzmann Machine for comprehensible credit scoring model

 

Restricted Boltzmann Machines

  1. High-Order Semi-RBMs and Deep Gated Neural Networks for Feature Interaction Identification and Non-Linear Semantic Indexing
  2. Periocular Recognition using Unsupervised Convolutional Rbm Feature Learning
  3. A 3d model recognition mechanism based on deep boltzmann machines

 

Search

  1. Enhanced Higgs to $\ tau^+\ tau^-$ Searches with Deep Learning
  2. Implicitly Learning a User Interest Profile for Personalization of Web Search Using Collaborative Filtering
  3. Research on deep neural network’s hidden layers in phoneme recognition
  4. Cross-modal Retrieval with Correspondence Autoencoder
  5. Deep Search with Attribute-aware Deep Network
  6. Deep learning for real-time Atari game play using offline Monte-Carlo tree search planning

 

Semantix Indexing

  1. High-Order Semi-RBMs and Deep Gated Neural Networks for Feature Interaction Identification and Non-Linear Semantic Indexing

 

Sentiment Analysis

  1. Recursive Deep Learning for Sentiment Analysis over Social Data
  2. Learning Bilingual Embedding Model for Cross-Language Sentiment Classification

 

Social Network

  1. Deep learning-based target customer position extraction on social network
  2. Recursive Deep Learning for Sentiment Analysis over Social Data

 

Spam

  1. Spammer detection on Sina Micro-Blog

 

Speech Recognition

  1. Deep Neural Networks For Spoken Dialog Systems
  2. Neural Network Based Pitch Tracking In Very Noisy Speech
  3. An Investigation of Implementation and Performance Analysis of Dnn Based Speech Synthesis System
  4. Ensemble Learning Approaches in Speech Recognition
  5. Raw Speech Signal-based Continuous Speech Recognition using Convolutional Neural Networks
  6. Feature Mapping of Multiple Beamformed Sources for Robust Overlapping Speech Recognition Using a Microphone Array
  7. Dysarthric Speech Recognition Using a Convolutive Bottleneck Network
  8. Cross-language speech attribute detection and phone recognition for Tibetan using deep learning
  9. Mapping between ultrasound and vowel speech using Dnn framework
  10. Improving generation performance of speech emotion recognition by denoising autoencoders
  11. Research on deep neural network’s hidden layers in phoneme recognition
  12. Cross-language transfer learning for deep neural network based speech enhancement
  13. Performance evaluation of deep bottleneck features for spoken language identification
  14. Multiple time-span feature fusion for deep neural network modeling
  15. Investigation of stochastic Hessian-Free optimization in Deep neural networks for speech recognition
  16. Phonotactic language recognition based on Dnn-hmm acoustic model
  17. Labeling unsegmented sequence data with Dnn-hmm and its application for speech recognition
  18. Building an ensemble of Cd-dnn-hmm acoustic model using random forests of phonetic decision trees
  19. Speaker adaptation of hybrid Nn/hmm model for speech recognition based on singular value decomposition
  20. Decision tree based state tying for speech recognition using Dnn derived embeddings
  21. Deep belief network based Crf for spoken language understanding
  22. A fusion approach to spoken language identification based on combining multiple phone recognizers and speech attribute detectors
  23. Patch-Based Models of Spectrogram Edges for Phone Classification
  24. Cross-Dialectal Voice Conversion with Neural Networks
  25. Trope
  26. Non-negative Factor Analysis of Gaussian Mixture Model Weight Adaptation for Language and Dialect Recognition
  27. Speech Emotion Recognition Using Cnn
  28. Joint Phoneme Segmentation Inference and Classification using CRFs

 

Stochastic Gradient

  1. Improving training time of deep neural networkwith asynchronous averaged stochastic gradient descent

 

Stochastic Gradient Descent

  1. Improving training time of deep neural networkwith asynchronous averaged stochastic gradient descent

 

Subspace Learning

  1. Greedy Approaches to Semi-Supervised Subspace Learning

 

Survey

  1. Visual Domain Adaptation: A Survey of Recent Advances

 

Theory

  1. Adaptive Information-Theoretical Feature Selection for Pattern Classification
  2. The atoms of neural computation
  3. On The Dynamical Nature Of Computation

 

Time Series

  1. Deep Multimodal Fusion: Combining Discrete Events and Continuous Signals
  2. Query Optimization in Heterogeneous Cpu/gpu Environment for Time Series Databases
  3. Temporal Dropout of Changes Approach to Convolutional Learning of Spatio-Temporal Features
  4. Multi-scale Temporal Modeling for Dimensional Emotion Recognition in Video

 

Tongue

  1. Testing AutoTrace: A machine-learning approach to automated tongue contour data extraction

 

Tool

  1. Introducing CURRENNT–the Munich open-source Cuda RecurREnt Neural Network Toolkit

 

Ultrasound

  1. Mapping between ultrasound and vowel speech using Dnn framework

 

Vehicle Classification

  1. Vehicle Type Classification Using Unsupervised Convolutional Neural Network

 

Vehicle Classificationx

  1. Vehicle Type Classification Using Semi-Supervised Convolutional Neural Network

 

Video

  1. Video Event Detection via Multi-modality Deep Learning
  2. Semantic parsing for priming object detection in indoors Rgb-d scenes
  3. Fusing Music and Video Modalities Using Multi-timescale Shared Representations
  4. Mask Assisted Object Coding with Deep Learning for Object Retrieval in Surveillance Videos
  5. Multi-scale Temporal Modeling for Dimensional Emotion Recognition in Video

 

Web Mining

  1. A Method For Extracting Information From The Web Using Deep Learning Algorithm

 

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@misc{2014DPalazRCollobert,
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@misc{2014DTurcsanyABargiela,
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@misc{2014DYiZLeiSLiaoSZLi,
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@misc{2014EMRehnHSprekeler,
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@misc{2014GZhongMCheriet,
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@misc{2014HKagayaKAizawaMOgawa,
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@misc{2014HLvGYuXTianGWu,
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@misc{2014HMobahiJWFisherIIICoarse-to-FineMinimizationof,
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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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