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Update with 57 papers to Deeplearning.University Bibliography

This update to Deeplearning.University has 57 new Deep Learning papers (all from 2015) collected in the period since last update (March 29th, 2015) and until today.  See the 57 new papers below.

As always if you want have suggestions to the bibliography (in particular: improved Bibtex-entries or additions), please do that as git pull-requests on the file:https://github.com/memkite/DeepLearningBibliography/blob/master/bibtex/deeplearninggpuwithkeywords2014.bib

https://github.com/memkite/DeepLearningBibliography

Best regards,

Amund Tveit

[2d] [3d] [action recognition] [ads] [aesthetic learning] [applications] [architecture] [asynchronous] [autoencoder] [back propagation] [biologically] [cascade] [cell] [character recognition] [classification] [cloud] [cnn] [cognition] [computer vision] [convolutional] [convolutional network] [convolutional neural network] [deep neural network] [denoising] [disease] [embedded] [estimation] [face] [facial] [feature] [feature selection] [features] [gaussian] [handwritten] [hierarchical] [image classification] [image recognition] [invariant] [medicine] [motion] [multi-label] [multimodal] [natural language processing] [neuron] [neuroscience] [noisy] [object recognition] [optimization] [parameter] [plankton] [pose] [recurrent] [regression] [regularization] [restricted boltzmann machine] [retina] [review] [search] [segmentation] [sketch recognition] [social] [sparse] [spatio-temporal] [spectral] [speech] [speech recognition] [strategies] [survey] [target detection] [temporal] [transfer learning] [vehicle][visual]

2D

  1. Globally Tuned Cascade Pose Regression via Back Propagation with Application in 2d Face Pose Estimation and Heart Segmentation in 3d Ct Images

 

3D

  1. Efficient Monocular Pose Estimation for Complex 3d Models
  2. Globally Tuned Cascade Pose Regression via Back Propagation with Application in 2d Face Pose Estimation and Heart Segmentation in 3d Ct Images

 

Action Recognition

  1. Learning motion and content-dependent features with convolutions for action recognition

 

Ads

  1. A Recurrent Multilayer Model with Hebbian Learning and Intrinsic Plasticity Leads to Invariant Object Recognition and Biologically Plausible Receptive Fields

 

Aesthetic Learning

  1. Deep Aesthetic Learning

 

Applications

  1. A Survey Of Applications And Human Motion Recognition With Microsoft Kinect

 

Architecture

  1. Ioda: An input/output deep architecture for image labeling
  2. Plankton Classification Using Hybrid Convolutional Network-Random Forests Architectures
  3. Reverse Scaffolding: A Constructivist Design Architecture for Mathematics Learning With Educational Technology

 

Asynchronous

  1. Evaluating Asynchronous Discussion as Social Constructivist Pedagogy in an Online Undergraduate Gerontological Social Work Course

 

Autoencoder

  1. Denoising Convolutional Autoencoders for Noisy Speech Recognition
  2. A Novel Method for Text Recognition in Natural Scene Based on Sparse Stacked Autoencoder

 

Back Propagation

  1. Globally Tuned Cascade Pose Regression via Back Propagation with Application in 2d Face Pose Estimation and Heart Segmentation in 3d Ct Images

 

Biologically

  1. A Recurrent Multilayer Model with Hebbian Learning and Intrinsic Plasticity Leads to Invariant Object Recognition and Biologically Plausible Receptive Fields

 

Cascade

  1. Globally Tuned Cascade Pose Regression via Back Propagation with Application in 2d Face Pose Estimation and Heart Segmentation in 3d Ct Images

 

Cell

  1. Method And System For Characterizing Cell Populations

 

Character Recognition

  1. Deep Convolutional Network for Handwritten Chinese Character Recognition

 

Classification

  1. Feature Fusion, Feature Selection and Local N-ary Patterns for Object Recognition and Image Classification
  2. Plankton Classification Using Hybrid Convolutional Network-Random Forests Architectures
  3. Monza: Image Classification of Vehicle Make and Model Using Convolutional Neural Networks and Transfer Learning
  4. Deep Networks for Early Stage Skin Disease and Skin Cancer Classification
  5. Beyond Short Snippets: Deep Networks for Video Classification
  6. Multi-label Classification using Labels as Hidden Nodes

 

Cloud

  1. Zoe: A Cloud-less Dialog-enabled Continuous Sensing Wearable Exploiting Heterogeneous Computation

 

Cnn

  1. Gaze Detection with CNNs for Linguistic Research
  2. Cnn optimizations for embedded systems and Fft
  3. Crf Learning with Cnn Features for Image Segmentation

 

Cognition

  1. A Recurrent Multilayer Model with Hebbian Learning and Intrinsic Plasticity Leads to Invariant Object Recognition and Biologically Plausible Receptive Fields
  2. A developmental where-what neural net for concurrent and interactive visual attention and recognition
  3. Feature Fusion, Feature Selection and Local N-ary Patterns for Object Recognition and Image Classification
  4. Latin Genre Recognition with Deep Learning and Spectral Periodicity
  5. A Survey Of Applications And Human Motion Recognition With Microsoft Kinect
  6. Learning motion and content-dependent features with convolutions for action recognition
  7. Deep Convolutional Network for Handwritten Chinese Character Recognition
  8. Tiny ImageNet Visual Recognition Challenge
  9. Denoising Convolutional Autoencoders for Noisy Speech Recognition
  10. Composite Sketch Recognition via Deep Network-A Transfer Learning Approach
  11. A Novel Method for Text Recognition in Natural Scene Based on Sparse Stacked Autoencoder

 

Computer Vision

  1. Bio-Inspired Computer Vision: Setting the Basis for a New Departure

 

Convolutional

  1. Probabilistic Binary-Mask Cocktail-Party Source Separation in a Convolutional Deep Neural Network
  2. Initialization Strategies of Spatio-Temporal Convolutional Neural Networks
  3. Plankton Classification Using Hybrid Convolutional Network-Random Forests Architectures
  4. Deep Convolutional Network for Handwritten Chinese Character Recognition
  5. Convolutional Networks in Scene Labelling
  6. Denoising Convolutional Autoencoders for Noisy Speech Recognition
  7. Monza: Image Classification of Vehicle Make and Model Using Convolutional Neural Networks and Transfer Learning
  8. Classifying Shadowgraph Images of Planktons Using Convolutional Neural Networks

 

Convolutional Network

  1. Plankton Classification Using Hybrid Convolutional Network-Random Forests Architectures
  2. Deep Convolutional Network for Handwritten Chinese Character Recognition
  3. Convolutional Networks in Scene Labelling

 

Convolutional Neural Network

  1. Initialization Strategies of Spatio-Temporal Convolutional Neural Networks
  2. Monza: Image Classification of Vehicle Make and Model Using Convolutional Neural Networks and Transfer Learning
  3. Classifying Shadowgraph Images of Planktons Using Convolutional Neural Networks

 

Deep Neural Network

  1. Probabilistic Binary-Mask Cocktail-Party Source Separation in a Convolutional Deep Neural Network
  2. From Neural Networks To Deep Neural Networks
  3. Regularization Of Context-dependent Deep Neural Networks With Context-independent Multi-task Training

 

Denoising

  1. Denoising Convolutional Autoencoders for Noisy Speech Recognition

 

Disease

  1. Deep Networks for Early Stage Skin Disease and Skin Cancer Classification

 

Embedded

  1. Cnn optimizations for embedded systems and Fft
  2. Learning Feature Hierarchies: A Layer-wise Tag-embedded Approach

 

Estimation

  1. Efficient Monocular Pose Estimation for Complex 3d Models
  2. Globally Tuned Cascade Pose Regression via Back Propagation with Application in 2d Face Pose Estimation and Heart Segmentation in 3d Ct Images

 

Face

  1. Globally Tuned Cascade Pose Regression via Back Propagation with Application in 2d Face Pose Estimation and Heart Segmentation in 3d Ct Images
  2. Learning predictable binary codes for face indexing

 

Facial

  1. Modeling Self-Efficacy Across Age Groups with Automatically Tracked Facial Expression

 

Feature

  1. Feature Fusion, Feature Selection and Local N-ary Patterns for Object Recognition and Image Classification
  2. Learning motion and content-dependent features with convolutions for action recognition
  3. Learning Feature Hierarchies: A Layer-wise Tag-embedded Approach
  4. Crf Learning with Cnn Features for Image Segmentation
  5. Visual Saliency Based on Multiscale Deep Features

 

Feature Selection

  1. Feature Fusion, Feature Selection and Local N-ary Patterns for Object Recognition and Image Classification

 

Features

  1. Learning motion and content-dependent features with convolutions for action recognition
  2. Crf Learning with Cnn Features for Image Segmentation
  3. Visual Saliency Based on Multiscale Deep Features

 

Gaussian

  1. Gaussian processes methods for nostationary regression

 

Handwritten

  1. Deep Convolutional Network for Handwritten Chinese Character Recognition
  2. Recognizing Handwritten Digits and Characters

 

Hierarchical

  1. Hierarchical Dynamical Systems

 

Image Classification

  1. Feature Fusion, Feature Selection and Local N-ary Patterns for Object Recognition and Image Classification
  2. Monza: Image Classification of Vehicle Make and Model Using Convolutional Neural Networks and Transfer Learning

 

Image Recognition

  1. Recognizing Characters From Google Street View Images
  2. Tiny Imagenet Challenge

 

Invariant

  1. A Recurrent Multilayer Model with Hebbian Learning and Intrinsic Plasticity Leads to Invariant Object Recognition and Biologically Plausible Receptive Fields

 

Medicine

  1. A Deep Learning Model of the Retina
  2. Deep Networks for Early Stage Skin Disease and Skin Cancer Classification
  3. The future of human cerebral cartography: a novel approach

 

Motion

  1. A Survey Of Applications And Human Motion Recognition With Microsoft Kinect
  2. Learning motion and content-dependent features with convolutions for action recognition

 

Multi-Label

  1. Multi-label Classification using Labels as Hidden Nodes

 

Multimodal

  1. Generalized K-fan Multimodal Deep Model with Shared Representations

 

Natural Language Processing

  1. Text Retrieval analysis based on Deep Learning

 

Neuron

  1. Gibbs Sampling with Low-Power Spiking Digital Neurons

 

Neuroscience

  1. On Simplicity and Complexity in the Brave New World of Large-Scale Neuroscience

 

Noisy

  1. Denoising Convolutional Autoencoders for Noisy Speech Recognition

 

Object Recognition

  1. A Recurrent Multilayer Model with Hebbian Learning and Intrinsic Plasticity Leads to Invariant Object Recognition and Biologically Plausible Receptive Fields
  2. Feature Fusion, Feature Selection and Local N-ary Patterns for Object Recognition and Image Classification

 

Optimization

  1. Cnn optimizations for embedded systems and Fft

 

Parameter

  1. Parameter Selection and Pre-Conditioning for a Graph Form Solver

 

Plankton

  1. Plankton Classification Using Hybrid Convolutional Network-Random Forests Architectures
  2. Classifying Shadowgraph Images of Planktons Using Convolutional Neural Networks

 

Pose

  1. Efficient Monocular Pose Estimation for Complex 3d Models
  2. Globally Tuned Cascade Pose Regression via Back Propagation with Application in 2d Face Pose Estimation and Heart Segmentation in 3d Ct Images

 

Recurrent

  1. A Recurrent Multilayer Model with Hebbian Learning and Intrinsic Plasticity Leads to Invariant Object Recognition and Biologically Plausible Receptive Fields

 

Regression

  1. Gaussian processes methods for nostationary regression
  2. Globally Tuned Cascade Pose Regression via Back Propagation with Application in 2d Face Pose Estimation and Heart Segmentation in 3d Ct Images

 

Regularization

  1. Regularization Of Context-dependent Deep Neural Networks With Context-independent Multi-task Training

 

Restricted Boltzmann Machine

  1. Scalable user intent mining using a multimodal Restricted Boltzmann Machine

 

Retina

  1. A Deep Learning Model of the Retina

 

Review

  1. A Preliminary Review of Influential Works in Data-Driven Discovery

 

Search

  1. Gaze Detection with CNNs for Linguistic Research

 

Segmentation

  1. Crf Learning with Cnn Features for Image Segmentation
  2. Globally Tuned Cascade Pose Regression via Back Propagation with Application in 2d Face Pose Estimation and Heart Segmentation in 3d Ct Images

 

Sketch Recognition

  1. Composite Sketch Recognition via Deep Network-A Transfer Learning Approach

 

Social

  1. Evaluating Asynchronous Discussion as Social Constructivist Pedagogy in an Online Undergraduate Gerontological Social Work Course

 

Sparse

  1. A Novel Method for Text Recognition in Natural Scene Based on Sparse Stacked Autoencoder

 

Spatio-Temporal

  1. Initialization Strategies of Spatio-Temporal Convolutional Neural Networks

 

Spectral

  1. Latin Genre Recognition with Deep Learning and Spectral Periodicity

 

Speech

  1. Denoising Convolutional Autoencoders for Noisy Speech Recognition

 

Speech Recognition

  1. Denoising Convolutional Autoencoders for Noisy Speech Recognition

 

Strategies

  1. Initialization Strategies of Spatio-Temporal Convolutional Neural Networks

 

Survey

  1. A Survey Of Applications And Human Motion Recognition With Microsoft Kinect

 

Target Detection

  1. A Novel method for Target Detection

 

Temporal

  1. Initialization Strategies of Spatio-Temporal Convolutional Neural Networks

 

Transfer Learning

  1. Composite Sketch Recognition via Deep Network-A Transfer Learning Approach
  2. Monza: Image Classification of Vehicle Make and Model Using Convolutional Neural Networks and Transfer Learning

 

Vehicle

  1. Monza: Image Classification of Vehicle Make and Model Using Convolutional Neural Networks and Transfer Learning

 

Video

  1. Beyond Short Snippets: Deep Networks for Video Classification

 

Visual

  1. A developmental where-what neural net for concurrent and interactive visual attention and recognition
  2. Tiny ImageNet Visual Recognition Challenge
  3. Implications of Multimodal Deep Learning for Textual and Visual Data
  4. Visual Saliency Based on Multiscale Deep Features

 

BIBLIOGRAPHY

@misc{2015ABanerjeeVIyer,
  title = {Tiny Imagenet Challenge},
  author = {A Banerjee, V Iyer}
}


@misc{2015AEsteva,
  title = {Deep Networks for Early Stage Skin Disease and Skin Cancer Classification},
  author = {A Esteva}
}


@misc{2015AJRSimpson,
  title = {Probabilistic Binary-Mask Cocktail-Party Source Separation in a Convolutional Deep Neural Network},
  author = {AJR Simpson}
}


@misc{2015AParanjapeAMudassir,
  title = {Convolutional Networks in Scene Labelling},
  author = {A Paranjape, A Mudassir}
}


@misc{2015ARubioMVillamizarLFerrazAPenateSanchez,
  title = {Efficient Monocular Pose Estimation for Complex 3d Models},
  author = {A Rubio, M Villamizar, L Ferraz, A Penate-Sanchez}
}


@misc{2015AVasilyev,
  title = {Cnn optimizations for embedded systems and Fft},
  author = {A Vasilyev}
}


@misc{2015BLSturmCKireliukJLarsen,
  title = {Latin Genre Recognition with Deep Learning and Spectral Periodicity},
  author = {BL Sturm, C Kireliuk, J Larsen}
}


@misc{2015CFougnerSBoyd,
  title = {Parameter Selection and Pre-Conditioning for a Graph Form Solver},
  author = {C Fougner, S Boyd}
}


@misc{2015CGulbrandsenCAWalshAEFultonAAzulaiHTong,
  title = {Evaluating Asynchronous Discussion as Social Constructivist Pedagogy in an Online Undergraduate Gerontological Social Work Course},
  author = {C Gulbrandsen, CA Walsh, AE Fulton, A Azulai, H Tong}
}


@misc{2015CHUANGLZHONGYHUANGGZHANGXZHONG,
  title = {A Novel Method for Text Recognition in Natural Scene Based on Sparse Stacked Autoencoder},
  author = {C HUANG, L ZHONG, Y HUANG, G ZHANG, X ZHONG}
}


@misc{2015CLiuWXuQWuGYang,
  title = {Learning motion and content-dependent features with convolutions for action recognition},
  author = {C Liu, W Xu, Q Wu, G Yang}
}


@misc{2015DLiuYWang,
  title = {Monza: Image Classification of Vehicle Make and Model Using Convolutional Neural Networks and Transfer Learning},
  author = {D Liu, Y Wang}
}


@misc{2015EGong,
  title = {Deep Aesthetic Learning},
  author = {E Gong}
}


@misc{2015EMansimovNSrivastavaRSalakhutdinov,
  title = {Initialization Strategies of Spatio-Temporal Convolutional Neural Networks},
  author = {E Mansimov, N Srivastava, R Salakhutdinov}
}


@misc{2015FLiuGLinCShen,
  title = {Crf Learning with Cnn Features for Image Segmentation},
  author = {F Liu, G Lin, C Shen}
}


@misc{2015GChenSNSrihari,
  title = {Generalized K-fan Multimodal Deep Model with Shared Representations},
  author = {G Chen, SN Srihari}
}


@misc{2015GLiYYu,
  title = {Visual Saliency Based on Multiscale Deep Features},
  author = {G Li, Y Yu}
}


@misc{2015GWangJZhang,
  title = {Recognizing Characters From Google Street View Images},
  author = {G Wang, J Zhang}
}


@misc{2015HPham,
  title = {Implications of Multimodal Deep Learning for Textual and Visual Data},
  author = {H Pham}
}


@misc{2015JFGrafsgaardSYLeeBWMottKEBoyerJCLester,
  title = {Modeling Self-Efficacy Across Age Groups with Automatically Tracked Facial Expression},
  author = {JF Grafsgaard, SY Lee, BW Mott, KE Boyer, JC Lester}
}


@misc{2015JLerougeRHeraultCChatelainFJardin,
  title = {Ioda: An input/output deep architecture for image labeling},
  author = {J Lerouge, R Herault, C Chatelain, F Jardin}
}


@misc{2015JReadJHollmén,
  title = {Multi-label Classification using Labels as Hidden Nodes},
  author = {J Read, J Hollmén}
}


@misc{2015JYHNgMHausknechtSVijayanarasimhanOVinyals,
  title = {Beyond Short Snippets: Deep Networks for Video Classification},
  author = {JYH Ng, M Hausknecht, S Vijayanarasimhan, O Vinyals}
}


@misc{2015KChaseDAbrahamson,
  title = {Reverse Scaffolding: A Constructivist Design Architecture for Mathematics Learning With Educational Technology},
  author = {K Chase, D Abrahamson}
}


@misc{2015KLoewkeSMMaddah,
  title = {Method And System For Characterizing Cell Populations},
  author = {K Loewke, SM Maddah}
}


@misc{2015LIUKaiLZhangYSun,
  title = {Text Retrieval analysis based on Deep Learning},
  author = {LIU Kai, L Zhang, Y Sun}
}


@misc{2015LMcIntoshNMaheswaranathan,
  title = {A Deep Learning Model of the Retina},
  author = {L McIntosh, N Maheswaranathan}
}


@misc{2015LMuñozGonzález,
  title = {Gaussian processes methods for nostationary regression},
  author = {L Muñoz González}
}


@misc{2015MKayserVZhong,
  title = {Denoising Convolutional Autoencoders for Noisy Speech Recognition},
  author = {M Kayser, V Zhong}
}


@misc{2015MStalzerCMentzel,
  title = {A Preliminary Review of Influential Works in Data-Driven Discovery},
  author = {M Stalzer, C Mentzel}
}


@misc{2015MTeichmannFHHamker,
  title = {A Recurrent Multilayer Model with Hebbian Learning and Intrinsic Plasticity Leads to Invariant Object Recognition and Biologically Plausible Receptive Fields},
  author = {M Teichmann, FH Hamker}
}


@misc{2015NDLanePGeorgievCMascoloYGao,
  title = {Zoe: A Cloud-less Dialog-enabled Continuous Sensing Wearable Exploiting Heterogeneous Computation},
  author = {ND Lane, P Georgiev, C Mascolo, Y Gao}
}


@misc{2015NVKMedathatiHNeumannGMassonPKornprobst,
  title = {Bio-Inspired Computer Vision: Setting the Basis for a New Departure},
  author = {NVK Medathati, H Neumann, G Masson, P Kornprobst}
}


@misc{2015PBellSRenals,
  title = {Regularization Of Context-dependent Deep Neural Networks With Context-independent Multi-task Training},
  author = {P Bell, S Renals}
}


@misc{2015PGaoSGanguli,
  title = {On Simplicity and Complexity in the Brave New World of Large-Scale Neuroscience},
  author = {P Gao, S Ganguli}
}


@misc{2015PJindalRMundra,
  title = {Plankton Classification Using Hybrid Convolutional Network-Random Forests Architectures},
  author = {P Jindal, R Mundra}
}


@misc{2015PMNSequeira,
  title = {Hierarchical Dynamical Systems},
  author = {PMN Sequeira}
}


@misc{2015PMittalMVatsaRSingh,
  title = {Composite Sketch Recognition via Deep Network-A Transfer Learning Approach},
  author = {P Mittal, M Vatsa, R Singh}
}


@misc{2015PSunJKMinGXiong,
  title = {Globally Tuned Cascade Pose Regression via Back Propagation with Application in 2d Face Pose Estimation and Heart Segmentation in 3d Ct Images},
  author = {P Sun, JK Min, G Xiong}
}


@misc{2015RFrackowiakHMarkram,
  title = {The future of human cerebral cartography: a novel approach},
  author = {R Frackowiak, H Markram}
}


@misc{2015RHeYCaiTTanLDavis,
  title = {Learning predictable binary codes for face indexing},
  author = {R He, Y Cai, T Tan, L Davis}
}


@misc{2015RLUNWZHAO,
  title = {A Survey Of Applications And Human Motion Recognition With Microsoft Kinect},
  author = {R LUN, W ZHAO}
}


@misc{2015RVoigt,
  title = {Gaze Detection with CNNs for Linguistic Research},
  author = {R Voigt}
}


@misc{2015SDasBUPedroniPMerollaJArthurASCassidy,
  title = {Gibbs Sampling with Low-Power Spiking Digital Neurons},
  author = {S Das, BU Pedroni, P Merolla, J Arthur, AS Cassidy}
}


@misc{2015SFeng,
  title = {A New Method of Multi-Scale Receptive Fields Learning},
  author = {S Feng}
}


@misc{2015SSoh,
  title = {Classifying Shadowgraph Images of Planktons Using Convolutional Neural Networks},
  author = {S Soh}
}


@misc{2015TNikoskinen,
  title = {From Neural Networks To Deep Neural Networks},
  author = {T Nikoskinen}
}


@misc{2015VSundaresanJLin,
  title = {Recognizing Handwritten Digits and Characters},
  author = {V Sundaresan, J Lin}
}


@misc{2015WSheng,
  title = {Feature Fusion, Feature Selection and Local N-ary Patterns for Object Recognition and Image Classification},
  author = {W Sheng}
}


@misc{2015XLiSZhuLChen,
  title = {A Novel method for Target Detection},
  author = {X Li, S Zhu, L Chen}
}


@misc{2015XWang,
  title = {MultiLayer Neural Networks},
  author = {X Wang}
}


@misc{2015YLeXYang,
  title = {Tiny ImageNet Visual Recognition Challenge},
  author = {Y Le, X Yang}
}


@misc{2015YShangWDingMLiuXSongTHuYAnHWang,
  title = {Scalable user intent mining using a multimodal Restricted Boltzmann Machine},
  author = {Y Shang, W Ding, M Liu, X Song, T Hu, Y An, H Wang}
}


@misc{2015YZhang,
  title = {Deep Convolutional Network for Handwritten Chinese Character Recognition},
  author = {Y Zhang}
}


@misc{2015ZJiJWeng,
  title = {A developmental where-what neural net for concurrent and interactive visual attention and recognition},
  author = {Z Ji, J Weng}
}


@misc{2015ZYuanCXuJSangSYanMHossain,
  title = {Learning Feature Hierarchies: A Layer-wise Tag-embedded Approach},
  author = {Z Yuan, C Xu, J Sang, S Yan, M Hossain}
}

, , , , ,

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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