Ensemble Deep Learning

Ensemble Based Machine Learning has been used with success in several Kaggle competitions, and this year also the Imagenet competition was dominated by ensembles in Deep Learning, e.g. Trimps-Soushen team from 3rd Research Institute of the Ministry of Public Security (China) used a combination of Inception, Inception-Resnet, Resnet and Wide Residual Network to win the Object Classification/localization challenge. This blog post has recent papers related to Ensembles in Deep Learning

Best regards,

Amund Tveit

Year  Title Author
2016   A Deep Learning Approach to Unsupervised Ensemble Learning  U Shaham, X Cheng, O Dror, A Jaffe, B Nadler
2016   Distributed Ensemble Learning for Analyzing Nationwide Health-Insurance Databases  H Ha, J Kim, J Park, S Yoon
2016   When Ensemble Learning Meets Deep Learning: a New Deep Support Vector Machine for Classification  Z Qi, B Wang, Y Tian, P Zhang
2016   Group Dropout Inspired by Ensemble Learning  K Hara, D Saitoh, T Kondou, S Suzuki, H Shouno
2016   Analysis of Dropout Learning Regarded as Ensemble Learning  K Hara, D Saitoh, H Shouno
2016   A novel methodology to predict urban traffic congestion with ensemble learning  G Asencio
2016   Glaucoma Detection Using Entropy Sampling And Ensemble Learning For Automatic Optic Cup And Disc Segmentation  J Zilly, JM Buhmann, D Mahapatra
2015   Soft sensor development for nonlinear and time‐varying processes based on supervised ensemble learning with improved process state partition  W Shao, X Tian, P Wang
2015   Face hallucination through ensemble learning  CT Tu, MC Ho, JR Luo
2015   A novel multistage deep belief network based extreme learning machine ensemble learning paradigm for credit risk assessment  L Yu, Z Yang, L Tang
2015   An Efficient Concept Detection System Via Sparse Ensemble Learning  S Tang, ZX Xu, YD Zhang, HJ Li, YT Zheng, JT Li
2015   Intro to Practical Ensemble Learning  E LeDell
2014   Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning  S Paisitkriangkrai, C Shen, A Hengel
2014   Ensemble Learning Approaches in Speech Recognition  Y Zhao, J Xue, X Chen
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Deep Learning for Sentiment Analysis

Recently I published Embedding for NLP with Deep Learning (e.g. word2vec and follow-ups) and Deep Learning for Natural Language Processing – ICLR 2017 Discoveries – this posting is also mostly NLP-related since it provides recent papers related to Deep Learning for Sentiment Analysis, but also has examples of other types of sentiment (e.g. image sentiment).

Best regards,
Amund Tveit

Year  Title Author
2016   Image Sentiment Analysis using Deep Convolutional Neural Networks with Domain Specific Fine Tuning  S Jindal, S Singh
2016   Multi-Objective Model Selection (MOMS)-based Semi-Supervised Framework for Sentiment Analysis  FH Khan, U Qamar, S Bashir
2016   Real-Time Topic and Sentiment Analysis in Human-Robot Conversation  E Russell
2016   Finki at SemEval-2016 Task 4: Deep Learning Architecture for Twitter Sentiment Analysis  D Stojanovski, G Strezoski, G Madjarov, I Dimitrovski
2016   Design of Sentiment Analysis System for Hindi Content  M Yadav, V Bhojane
2016   Visual Sentiment Analysis for Social Images Using Transfer Learning Approach  J Islam, Y Zhang
2016   Unsupervised Feature Learning Assisted Visual Sentiment Analysis  Z Li, Y Fan, F Wang, W Liu
2016   Context-Aware Text Representation for Social Relation Aided Sentiment Analysis  LM Nguyen
2016   Select-Additive Learning: Improving Cross-individual Generalization in Multimodal Sentiment Analysis  H Wang, A Meghawat, LP Morency, EP Xing
2016   A semi-supervised approach to sentiment analysis using revised sentiment strength based on SentiWordNet  FH Khan, U Qamar, S Bashir
2016   SWIMS: Semi-Supervised Subjective Feature Weighting and Intelligent Model Selection for Sentiment Analysis  FH Khan, U Qamar, S Bashir
2016   Cross-modality Consistent Regression for Joint Visual-Textual Sentiment Analysis of Social Multimedia  Q You, J Luo, H Jin, J Yang
2016   INSIGHT-1 at SemEval-2016 Task 5: Deep Learning for Multilingual Aspect-based Sentiment Analysis  S Ruder12, P Ghaffari, JG Breslin
2016   SENSEI-LIF at SemEval-2016 Task 4: Polarity embedding fusion for robust sentiment analysis  M Rouvier, B Favre
2016   Leveraging Multimodal Information for Event Summarization and Concept-level Sentiment Analysis  RR Shah, Y Yu, A Verma, S Tang, AD Shaikh
2016   Visual and Textual Sentiment Analysis of a Microblog Using Deep Convolutional Neural Networks  Y Yu, H Lin, J Meng, Z Zhao
2016   Sentiment Analysis for Chinese Microblog based on Deep Neural Networks with Convolutional Extension Features  SUN Xiao, LI Chengcheng, REN Fuji
2016   PotTS at SemEval-2016 Task 4: Sentiment Analysis of Twitter Using Character-level Convolutional Neural Networks.  U Sidarenka, KL Straße
2016   Recursive Neural Conditional Random Fields for Aspect-based Sentiment Analysis  W Wang, SJ Pan, D Dahlmeier, X Xiao
2016   eSAP: A Decision Support Framework for Enhanced Sentiment Analysis and Polarity Classification  FH Khan, U Qamar, S Bashir
2016   Paragraph2Vec-based sentiment analysis on social media for business in Thailand  P Sanguansat
2016   Sentiment Analysis of Chinese Micro Blog Based on DNN and ELM and Vector Space Model  H Liu, S Li, C Jiang, H Liu
2016   Sentiment Analysis in Finance Market Forcast  D Wang
2016   Visual Sentiment Analysis with Network in Network  Z Li, Y Fan, F Wang
2015   Convolutional Neural Networks for Multimedia Sentiment Analysis  G Cai, B Xia
2015   Sentiment Analysis with Incremental Human-in-the-Loop Learning and Lexical Resource Customization  S Mishra, J Diesner, J Byrne, E Surbeck
2015   Parallel Recursive Deep Model for Sentiment Analysis  G Tian, Y Zhou
2015   Unsupervised Sentiment Analysis for Social Media Images  Y Wang, S Wang, J Tang, H Liu, B Li
2015   Prediction of changes in the stock market using twitter and sentiment analysis  IV Serban, DS González, X Wu
2015   Review Sentiment Analysis Based on Deep Learning  Z Hu, J Hu, W Ding, X Zheng
2015   Twitter Sentiment Analysis Using Deep Convolutional Neural Network  D Stojanovski, G Strezoski, G Madjarov, I Dimitrovski
2015   Sentiment-Specific Representation Learning for Document-Level Sentiment Analysis  D Tang
2015   Sentiment Analysis via Integrating Distributed Representations of Variable-length Word Sequence  Z Cui, X Shi, Y Chen
2015   Learning Higher-Level Features with Convolutional Restricted Boltzmann Machines for Sentiment Analysis  T Huynh, Y He, S Rüger
2015   Recursive Autoencoder with HowNet Lexicon for Sentence-Level Sentiment Analysis  X Fu, Y Xu
2014   Recursive Deep Learning for Sentiment Analysis over Social Data  C Li, B Xu, G Wu, S He, G Tian, H Hao
2014   Robust Image Sentiment Analysis Using Progressively Trained and Domain Transferred Deep Networks  Q You, J Luo, H Jin, J Yang
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Deep Learning with Gaussian Process

Gaussian Process is a statistical model where observations are in the continuous domain, to learn more check out a tutorial on gaussian process (by Univ.of Cambridge’s Zoubin G.). Gaussian Process is an infinite-dimensional generalization of multivariate normal distributions.

Researchers from University of Sheffield – Andreas C. Damanianou and Neil D. Lawrence – started using Gaussian Process with Deep Belief Networks (in 2013). This Blog post contains recent papers related to combining Deep Learning with Gaussian Process.

Best regards,
Amund Tveit

Year  Title Author
2016   Inverse Reinforcement Learning via Deep Gaussian Process  M Jin, C Spanos
2016   Annealing Gaussian into ReLU: a New Sampling Strategy for Leaky-ReLU RBM  CL Li, S Ravanbakhsh, B Poczos
2016   Large Scale Gaussian Process for Overlap-based Object Proposal Scoring  SL Pintea, S Karaoglu, JC van Gemert
2016   Gaussian Neuron in Deep Belief Network for Sentiment Prediction  Y Jin, D Du, H Zhang
2016   Fast, Exact and Multi-Scale Inference for Semantic Image Segmentation with Deep Gaussian CRFs  S Chandra, I Kokkinos
2016   The Variational Gaussian Process  D Tran, R Ranganath, DM Blei
2016   Probabilistic Feature Learning Using Gaussian Process Auto-Encoders  S Olofsson
2016   Sequential Inference for Deep Gaussian Process  Y Wang, M Brubaker, B Chaib
2016   Gaussian Copula Variational Autoencoders for Mixed Data  S Suh, S Choi
2016   Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising  K Zhang, W Zuo, Y Chen, D Meng, L Zhang
2016   Image super-resolution using non-local Gaussian process regression  H Wang, X Gao, K Zhang, J Li
2016   Gaussian Conditional Random Field Network for Semantic Segmentation  R Vemulapalli, O Tuzel, MY Liu, R Chellappa
2016   Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors  C Louizos, M Welling
2016   Deep Gaussian Processes for Regression using Approximate Expectation Propagation  TD Bui, D Hernández
2015   Learning to Assess Terrain from Human Demonstration Using an Introspective Gaussian Process Classifier  LP Berczi, I Posner, TD Barfoot
2015   Assessing the Degree of Nativeness and Parkinson’s Condition Using Gaussian Processes and Deep Rectifier Neural Networks  T Grósz, R Busa
2015   Gaussian processes methods for nostationary regression  L Muñoz González
2015   Deep Neural Networks with Random Gaussian Weights: A Universal Classification Strategy?  R Giryes, G Sapiro, AM Bronstein
2015   Nonlinear Gaussian Belief Network based fault diagnosis for industrial processes  H Yu, F Khan, V Garaniya
2015   Interactions Between Gaussian Processes and Bayesian Estimation  YL Wang
2015   Gaussian discrete restricted Boltzmann machine: theory and its applications: a thesis presented in partial fulfilment of the requirements for the degree of Master of …  S Manoharan
2015   Prosody Generation Using Frame-based Gaussian Process Regression  T Koriyama, T Kobayashi
2015   Mean-Field Inference in Gaussian Restricted Boltzmann Machine  C Takahashi, M Yasuda
2015   Variational Auto-encoded Deep Gaussian Processes  Z Dai, A Damianou, J González, N Lawrence
2015   Training Deep Gaussian Processes using Stochastic Expectation Propagation and Probabilistic Backpropagation  TD Bui, JM Hernández
2015   Accurate Object Detection and Semantic Segmentation using Gaussian Mixture Model and CNN  S Jain, S Dehriya, YK Jain
2014   Cross Modal Deep Model and Gaussian Process Based Model for MSR-Bing Challenge  J Wang, C Kang, Y He, S Xiang, C Pan
2014   Non-negative Factor Analysis of Gaussian Mixture Model Weight Adaptation for Language and Dialect Recognition  J Glass
2014   Gaussian Process Models with Parallelization and GPU acceleration  Z Dai, A Damianou, J Hensman, N Lawrence
2014   Parametric Speech Synthesis Using Local and Global Sparse Gaussian  T Koriyama, T Nose, T Kobayashi
2014   On the Link Between Gaussian Homotopy Continuation and Convex Envelopes  H Mobahi, JW Fisher III
2014   Improving Deep Neural Networks Using State Projection Vectors Of Subspace Gaussian Mixture Model As Features  M Karthick, S Umesh
2014   A Theoretical Analysis of Optimization by Gaussian Continuation  H Mobahi, JW Fisher III
2014   Factoring Variations in Natural Images with Deep Gaussian Mixture Models  A van den Oord, B Schrauwen
2014   Feature representation with Deep Gaussian processes
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Deep Learning for Clustering

Previously I published an ICLR 2017 discoveries blog post about Unsupervised Deep Learning – a subset of Unsupervised methods is Clustering, and this blog post has recent publications about Deep Learning for Clustering.

Best regards,

Amund Tveit

 

Year  Title Author
2016   An Intention-Topic Model Based on Verbs Clustering and Short Texts Topic Mining  T Lu, S Hou, Z Chen, L Cui, L Zhang
2016   Speaker Identification And Clustering Using Convolutional Neural Networks  Y Lukic, C Vogt, O Dürr, T Stadelmann
2016   Building energy modeling (BEM) using clustering algorithms and semi-supervised machine learning approaches  H Naganathan, WO Chong, X Chen
2016   Clustering Based Feature Learning on Variable Stars  C Mackenzie, K Pichara, P Protopapas
2016   Deep Belief Networks Oriented Clustering  Q Yang, H Wang, T Li, Y Yang
2016   Clustering Non-Stationary Data Streams with Online Deep Learning  A Hontabat, M Rising
2016   Fast image clustering based on convolutional neural network and binary K-means  S Ke, Y Zhao, B Li, Z Wu, X Liu
2016   Extracting Bottlenecks for Reinforcement Learning Agent by Holonic Concept Clustering and Attentional Functions  B Ghazanfari, N Mozayani
2016   A Hybrid Spectral Clustering and Deep Neural Network Ensemble Algorithm for Intrusion Detection in Sensor Networks  T Ma, F Wang, J Cheng, Y Yu, X Chen
2016   Single-Channel Multi-Speaker Separation using Deep Clustering  Y Isik, JL Roux, Z Chen, S Watanabe, JR Hershey
2016   Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering  B Yang, X Fu, ND Sidiropoulos, M Hong
2016   A Personalized Markov Clustering and Deep Learning Approach for Arabic Text Categorization  V Jindal
2016   Clustering the seoul metropolitan area by travel patterns based on a deep belief network  G Han, K Sohn
2016   An Empirical Investigation of Word Clustering Techniques for Natural Language Understanding  DA Shunmugam, P Archana
2016   Infinite Ensemble for Image Clustering  H Liu, M Shao, S Li, Y Fu
2015   Theoretical Analysis-based Distributed Load Balancing over Dynamic Overlay Clustering  H Lee, B Kwon, S Kim, I Lee, S Lee
2015   Competitive and Penalized Clustering Auto-encoder  Z Wang, Y Cheung
2015   Learning A Task-Specific Deep Architecture For Clustering  Z Wang, S Chang, J Zhou, TS Huang
2015   Pedestrian detection in thermal images using adaptive fuzzy C-means clustering and convolutional neural networks  V John, S Mita, Z Liu, B Qi
2015   Active Learning with Clustering and Unsupervised Feature Learning  S Berardo, E Favero, N Neto
2015   Clustering Noisy Signals with Structured Sparsity Using Time-Frequency Representation  T Hope, A Wagner, O Zuk
2015   Neuron Clustering for Mitigating Catastrophic Forgetting in Supervised and Reinforcement Learning  BF Goodrich
2015   Clustering Data of Mixed Categorical and Numerical Type with Unsupervised Feature Learning  D Lam, M Wei, D Wunsch
2015   Semi-supervised Hierarchical Clustering Ensemble and Its Application  W Xiao, Y Yang, H Wang, T Li, H Xing
2015   FaceNet: A Unified Embedding for Face Recognition and Clustering  F Schroff, D Kalenichenko, J Philbin
2015   Deep Transductive Semi-supervised Maximum Margin Clustering  G Chen
2015   Max-Entropy Feed-Forward Clustering Neural Network  H Xiao, X Zhu
2015   Overview of the ImageCLEF 2015 medical clustering task  MA Amin, MK Mohammed
2015   Joint Image Clustering and Labeling by Matrix Factorization  S Hong, J Choi, J Feyereisl, B Han, LS Davis
2015   Combining deep learning and unsupervised clustering to improve scene recognition performance  A Kappeler, RD Morris, AR Kamat, N Rasiwasia
2015   Experimental Study of Unsupervised Feature Learning for HEp-2 Cell Images Clustering  Y Zhao, Z Gao, L Wang, L Zhou
2015   Soft context clustering for F0 modeling in HMM-based speech synthesis  S Khorram, H Sameti, S King
2015   Semi-automatic ground truth generation using unsupervised clustering and limited manual labeling: Application to handwritten character recognition  S Vajda, Y Rangoni, H Cecotti
2015   Language discrimination and clustering via a neural network approach  A Mariano, G Parisi, S Pascazio
2015   Active Distance-Based Clustering using K-medoids  A Aghaee, M Ghadiri, MS Baghshah
2015   Convolutional Clustering for Unsupervised Learning  A Dundar, J Jin, E Culurciello
2015   Deep Learning with Nonparametric Clustering  G Chen
2015   Multi-view clustering via structured low-rank representation  D Wang, Q Yin, R He, L Wang, T Tan
2014   A Convex Formulation for Spectral Shrunk Clustering  X Chang, F Nie, Z Ma, Y Yang, X Zhou
2014   Ghent University-iMinds at MediaEval 2014 Diverse Images: Adaptive Clustering with Deep Features  B Vandersmissen, A Tomar, F Godin, W De Neve
2014   Features in Concert: Discriminative Feature Selection meets Unsupervised Clustering  M Leordeanu, A Radu, R Sukthankar
2014   Deep Embedding Network for Clustering  P Huang, Y Huang, W Wang, L Wang
2014   SoF: Soft-Cluster Matrix Factorization for Probabilistic Clustering  H Zhao, P Poupart, Y Zhang, M Lysy
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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)

Emotion

  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)

Classification

  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

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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