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Update with 29 new publications to Deeplearning.University Bibliography

I’ve gone from infrequent (monthly’ish) to the Deeplearning.University Bibliography to more frequent updates (1-2 times per week). Underneath are 29 new deep learning papers since last update (8 days prior to this). There are many highly interesting papers, and I would in particular like to point out:

  1. Transferring Knowledge from a Rnn to a Dnn
  2. Tree-based Convolution: A New Architecture for Sentence Modeling

Best regards,

Amund Tveit

Links to Deep Learning Subtopics

[acoustic] [acoustic model] [applications] [architecture] [classification] [cnn] [cognition] [convolutional] [convolutional neural network] [deep belief network] [denoising] [dnn] [face] [face recognition] [facial] [feature] [framework] [generative] [hierarchical] [incremental] [invariant] [mobile] [natural language processing] [neuromorphic] [neuron] [parsing] [pose] [prediction] [processor] [rectified] [recurrent] [regularization] [reinforcement learning] [rnn] [robust] [segmentation] [self driving cars] [semantic] [sentiment] [sentiment analysis] [social] [spatial] [survey] [temporal] [time series][visual]

Acoustic

  1. Deep Recurrent Neural Networks for Acoustic Modelling

 

Acoustic Model

  1. Deep Recurrent Neural Networks for Acoustic Modelling

 

Applications

  1. Maximum Entropy Pdf Design Using Feature Density Constraints: Applications in Signal Processing

 

Architecture

  1. Tree-based Convolution: A New Architecture for Sentence Modeling

 

Classification

  1. A Novel Method Based on Data Visual Autoencoding for Time-Series Classification
  2. Swiss-Chocolate: Combining Flipout Regularization and Random Forests with Artificially Built Subsystems to Boost Text-Classification for Sentiment
  3. Modeling Spatial-Temporal Clues in a Hybrid Deep Learning Framework for Video Classification

 

Cnn

  1. SuperCNN: A Superpixelwise Convolutional Neural Network for Salient Object Detection
  2. Matching-CNN Meets Knn: Quasi-Parametric Human Parsing

 

Cognition

  1. Pose-Invariant Face Recognition using Facial Landmarks and Weber Local Descriptor

 

Convolutional

  1. End-to-End Training of Deep Visuomotor Policies
  2. SuperCNN: A Superpixelwise Convolutional Neural Network for Salient Object Detection

 

Convolutional Neural Network

  1. SuperCNN: A Superpixelwise Convolutional Neural Network for Salient Object Detection

 

Deep Belief Network

  1. Scene Text Analysis using Deep Belief Networks

 

Denoising

  1. A New Image Denoising Scheme Using Spiking Neuromorphic Systems

 

Dnn

  1. Transferring Knowledge from a Rnn to a Dnn

 

Face

  1. Pose-Invariant Face Recognition using Facial Landmarks and Weber Local Descriptor

 

Face Recognition

  1. Pose-Invariant Face Recognition using Facial Landmarks and Weber Local Descriptor

 

Facial

  1. Pose-Invariant Face Recognition using Facial Landmarks and Weber Local Descriptor

 

Feature

  1. Maximum Entropy Pdf Design Using Feature Density Constraints: Applications in Signal Processing

 

Framework

  1. Modeling Spatial-Temporal Clues in a Hybrid Deep Learning Framework for Video Classification
  2. Efficient and robust analysis-by-synthesis in vision: A computational framework, behavioral tests, and modeling neuronal representations

 

Generative

  1. Bidirectional Recurrent Neural Networks as Generative Models-Reconstructing Gaps in Time Series

 

Hierarchical

  1. Hierarchical Reinforcement Learning: A Survey
  2. A Doubly Hierarchical Dirichlet Process Hidden Markov Model with a Non-Ergodic Structure

 

Incremental

  1. Incremental Training of Neural Network with Knowledge Distillation

 

Invariant

  1. Pose-Invariant Face Recognition using Facial Landmarks and Weber Local Descriptor

 

Mobile

  1. A Re-configurable On-line Learning Spiking Neuromorphic Processor comprising 256 neurons and 128k synapses

 

Natural Language Processing

  1. Tree-based Convolution: A New Architecture for Sentence Modeling
  2. Deep Learning for Nlp

 

Neuromorphic

  1. A Re-configurable On-line Learning Spiking Neuromorphic Processor comprising 256 neurons and 128k synapses
  2. A New Image Denoising Scheme Using Spiking Neuromorphic Systems

 

Neuron

  1. A Re-configurable On-line Learning Spiking Neuromorphic Processor comprising 256 neurons and 128k synapses
  2. Efficient and robust analysis-by-synthesis in vision: A computational framework, behavioral tests, and modeling neuronal representations

 

Parsing

  1. Matching-CNN Meets Knn: Quasi-Parametric Human Parsing

 

Pose

  1. Pose-Invariant Face Recognition using Facial Landmarks and Weber Local Descriptor

 

Prediction

  1. Prediction of changes in the stock market using twitter and sentiment analysis

 

Processor

  1. A Re-configurable On-line Learning Spiking Neuromorphic Processor comprising 256 neurons and 128k synapses

 

Rectified

  1. A Simple Way to Initialize Recurrent Networks of Rectified Linear Units

 

Recurrent

  1. Recurrent neural network
  2. Deep Recurrent Neural Networks for Acoustic Modelling
  3. A Simple Way to Initialize Recurrent Networks of Rectified Linear Units
  4. Bidirectional Recurrent Neural Networks as Generative Models-Reconstructing Gaps in Time Series

 

Regularization

  1. Swiss-Chocolate: Combining Flipout Regularization and Random Forests with Artificially Built Subsystems to Boost Text-Classification for Sentiment

 

Reinforcement Learning

  1. Hierarchical Reinforcement Learning: A Survey

 

Rnn

  1. Transferring Knowledge from a Rnn to a Dnn

 

Robust

  1. Efficient and robust analysis-by-synthesis in vision: A computational framework, behavioral tests, and modeling neuronal representations

 

Segmentation

  1. Tree Re-weighted Belief Propagation Using Deep Learning Potentials For Mass Segmentation From Mammograms
  2. Efficient piecewise training of deep structured models for semantic segmentation

 

Self Driving Cars

  1. An Empirical Evaluation of Deep Learning on Highway Driving

 

Semantic

  1. Efficient piecewise training of deep structured models for semantic segmentation

 

Sentiment

  1. Swiss-Chocolate: Combining Flipout Regularization and Random Forests with Artificially Built Subsystems to Boost Text-Classification for Sentiment
  2. Prediction of changes in the stock market using twitter and sentiment analysis

 

Sentiment Analysis

  1. Prediction of changes in the stock market using twitter and sentiment analysis

 

Social

  1. Multi-scale compositionality: identifying the compositional structures of social dynamics using deep learning.

 

Spatial

  1. Modeling Spatial-Temporal Clues in a Hybrid Deep Learning Framework for Video Classification

 

Survey

  1. Hierarchical Reinforcement Learning: A Survey

 

Temporal

  1. Modeling Spatial-Temporal Clues in a Hybrid Deep Learning Framework for Video Classification

 

Time Series

  1. Bidirectional Recurrent Neural Networks as Generative Models-Reconstructing Gaps in Time Series

 

Video

  1. Modeling Spatial-Temporal Clues in a Hybrid Deep Learning Framework for Video Classification

 

Visual

  1. A Novel Method Based on Data Visual Autoencoding for Time-Series Classification

 

BIBLIOGRAPHY

@misc{2015AHHNTorbatiJPicone,
  title = {A Doubly Hierarchical Dirichlet Process Hidden Markov Model with a Non-Ergodic Structure},
  author = {AHHN Torbati, J Picone}
}


@misc{2015ARaySRajeswarSChaudhury,
  title = {Scene Text Analysis using Deep Belief Networks},
  author = {A Ray, S Rajeswar, S Chaudhury}
}


@misc{2015BHuvalTWangSTandonJKiskeWSong,
  title = {An Empirical Evaluation of Deep Learning on Highway Driving},
  author = {B Huval, T Wang, S Tandon, J Kiske, W Song}
}


@misc{2015BRNN,
  title = {Recurrent neural network},
  author = {B RNN}
}


@misc{2015CQianYWangLGuo,
  title = {A Novel Method Based on Data Visual Autoencoding for Time-Series Classification},
  author = {C Qian, Y Wang, L Guo}
}


@misc{2015DHeryantoTSChua,
  title = {Incremental Training of Neural Network with Knowledge Distillation},
  author = {D Heryanto, TS Chua}
}


@misc{2015FUzdilliMJaggiDEggerPJulmyLDerczynski,
  title = {Swiss-Chocolate: Combining Flipout Regularization and Random Forests with Artificially Built Subsystems to Boost Text-Classification for Sentiment},
  author = {F Uzdilli, M Jaggi, D Egger, P Julmy, L Derczynski}
}


@misc{2015GLinCShenIReid,
  title = {Efficient piecewise training of deep structured models for semantic segmentation},
  author = {G Lin, C Shen, I Reid}
}


@misc{2015HKPengRMarculescu,
  title = {Multi-scale compositionality: identifying the compositional structures of social dynamics using deep learning.},
  author = {HK Peng, R Marculescu}
}


@misc{2015IVSerbanDSGonzálezXWu,
  title = {Prediction of changes in the stock market using twitter and sentiment analysis},
  author = {IV Serban, DS González, X Wu}
}


@misc{2015IYildirimTDKulkarniWAFreiwaldJBTenenbaum,
  title = {Efficient and robust analysis-by-synthesis in vision: A computational framework, behavioral tests, and modeling neuronal representations},
  author = {I Yildirim, TD Kulkarni, WA Freiwald, JB Tenenbaum}
}


@misc{2015JOhJGwakARafiqueꜟMJeon,
  title = {A New Image Denoising Scheme Using Spiking Neuromorphic Systems},
  author = {J Oh, J Gwak, A Rafiqueꜟ, M Jeon}
}


@misc{2015KVodrahalli,
  title = {Deep Learning for Nlp},
  author = {K Vodrahalli}
}


@misc{2015LMouHPengGLiYXuLZhangZJin,
  title = {Tree-based Convolution: A New Architecture for Sentence Modeling},
  author = {L Mou, H Peng, G Li, Y Xu, L Zhang, Z Jin}
}


@misc{2015MAlEmran,
  title = {Hierarchical Reinforcement Learning: A Survey},
  author = {M Al-Emran}
}


@misc{2015MBerglundTRaikoMHonkalaLKärkkäinen,
  title = {Bidirectional Recurrent Neural Networks as Generative Models-Reconstructing Gaps in Time Series},
  author = {M Berglund, T Raiko, M Honkala, L Kärkkäinen}
}


@misc{2015NDhungelGCarneiroAPBradley,
  title = {Tree Re-weighted Belief Propagation Using Deep Learning Potentials For Mass Segmentation From Mammograms},
  author = {N Dhungel, G Carneiro, AP Bradley}
}


@misc{2015NQiaoHMostafaFCorradiMOsswaldFStefanini,
  title = {A Re-configurable On-line Learning Spiking Neuromorphic Processor comprising 256 neurons and 128k synapses},
  author = {N Qiao, H Mostafa, F Corradi, M Osswald, F Stefanini}
}


@misc{2015PBaggenstoss,
  title = {Maximum Entropy Pdf Design Using Feature Density Constraints: Applications in Signal Processing},
  author = {P Baggenstoss}
}


@misc{2015QVLeNJaitlyGEHinton,
  title = {A Simple Way to Initialize Recurrent Networks of Rectified Linear Units},
  author = {QV Le, N Jaitly, GE Hinton}
}


@misc{2015SHeRWHLauWLiuZHuangQYang,
  title = {SuperCNN: A Superpixelwise Convolutional Neural Network for Salient Object Detection},
  author = {S He, RWH Lau, W Liu, Z Huang, Q Yang}
}


@misc{2015SKimYChoiMLee,
  title = {Deep Learning with Support Vector Data Description},
  author = {S Kim, Y Choi, M Lee}
}


@misc{2015SLevineCFinnTDarrellPAbbeel,
  title = {End-to-End Training of Deep Visuomotor Policies},
  author = {S Levine, C Finn, T Darrell, P Abbeel}
}


@misc{2015SLiuXLiangLLiuXShenJYangCXuLLin,
  title = {Matching-CNN Meets Knn: Quasi-Parametric Human Parsing},
  author = {S Liu, X Liang, L Liu, X Shen, J Yang, C Xu, L Lin}
}


@misc{2015WChanILane,
  title = {Deep Recurrent Neural Networks for Acoustic Modelling},
  author = {W Chan, I Lane}
}


@misc{2015WChanNRKeILane,
  title = {Transferring Knowledge from a Rnn to a Dnn},
  author = {W Chan, NR Ke, I Lane}
}


@misc{2015ZWuXWangYGJiangHYeXXue,
  title = {Modeling Spatial-Temporal Clues in a Hybrid Deep Learning Framework for Video Classification},
  author = {Z Wu, X Wang, YG Jiang, H Ye, X Xue}
}


@misc{2015ZZhangLWangQZhuSKChenYChen,
  title = {Pose-Invariant Face Recognition using Facial Landmarks and Weber Local Descriptor},
  author = {Z Zhang, L Wang, Q Zhu, SK Chen, Y Chen}
}

, , ,

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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