Overview of recent Deep Learning Bibliographies

For the last couple of months I’ve been creating bibliographies of recent academic publications in various subfields of Deep Learning on this blog. This posting gives an overview of the last 25 bibliographies posted.

Best regards,

Amund Tveit (WeChat: AmundTveit – Twitter: @atveit)

1. Deep Learning with Residual Networks

This posting is recent papers related to residual networks (i.e. very deep networks). Check out Microsoft Research’s paper Deep Residual Learning for Image Recognition and Kaiming He’s ICML 2016 Tutorial Deep Residual Learning, Deep Learning Gets Way Deeper

2. Deep Learning for Traffic Sign Detection and Recognition

Traffic Sign Detection and Recognition is key functionality for self-driving cars. This posting has recent papers in this area. Check also out related posting: Deep Learning for Vehicle Detection and Classification

3. Deep Learning for Vehicle Detection and Classification

This posting has recent papers about vehicle (e.g. car) detection and classification, e.g. for selv-driving/autonomous cars. Related: check also out Nvidia‘s End-to-End Deep Learning for Self-driving Cars and Udacity‘s Self-Driving Car Engineer (Nanodegree).

4. Deep Learning with Long Short-Term Memory (LSTM)

This blog post has some recent papers about Deep Learning with Long-Short Term Memory (LSTM). To get started I recommend checking out Christopher Olah’s Understanding LSTM Networks and Andrej Karpathy’s The Unreasonable Effectiveness of Recurrent Neural Networks. This blog post is complemented by Deep Learning with Recurrent/Recursive Neural Networks (RNN) — ICLR 2017 Discoveries.

5. Deep Learning in Finance

This posting has recent publications about Deep Learning in Finance (e.g. stock market prediction)

6. Deep Learning for Information Retrieval and Learning to Rank

This posting is about Deep Learning for Information Retrieval and Learning to Rank (i.e. of interest if developing search engines). The posting is complemented by the posting Deep Learning for Question Answering. To get started I recommend checking out Jianfeng Gao‘s (Deep Learning Technology Center at Microsoft Research) presentation Deep Learning for Web Search and Natural Language Processing.

Of partial relevance is the posting Deep Learning for Sentiment Analysis, the posting about Embedding for NLP with Deep Learning, the posting about Deep Learning for Natural Language Processing (ICLR 2017 discoveries), and the posting about Deep Learning for Recommender Systems

7. Deep Learning for Question Answering

This posting presents recent publications related to Deep Learning for Question Answering. Question Answering is described as “a computer science discipline within the fields of information retrieval and natural language processing (NLP), which is concerned with building systems that automatically answer questions posed by humans in a natural language”. I’ll also publish postings about Deep Learning for Information Retrieval and Learning to Rank today.

8. 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.

9. 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).

10. 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.

11. Deep Learning for Clustering

12. Deep Learning in combination with EEG electrical signals from the brain

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

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

13. Embedding for NLP with Deep Learning

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

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

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

14. 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.

15. 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.

16. 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.

17. 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.

18. 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.

19. Regularized Deep Networks — ICLR 2017 Discoveries

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

20. Unsupervised Deep Learning — ICLR 2017 Discoveries

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

21. Autoencoders in Deep Learning — ICLR 2017 Discoveries

This blog post gives an overview of papers related to autoencoders submitted to ICLR 2017, see underneath for the list of papers. If you want to learn about autoencoders check out the Stanford (UFLDL) tutorial about Autoencoders, Carl Doersch’ Tutorial on Variational Autoencoders, DeepLearning.TV’s Video tutorial on Autoencoders, or Goodfellow, Bengio and Courville’s Deep Learning book’s chapter on Autencoders.

22. Stochastic/Policy Gradients in Deep Learning — ICLR 2017 Discoveries

This blog post gives an overview of papers related to stochastic/policy gradient submitted to ICLR 2017, see underneath for the list of papers.

23. Deep Learning with Recurrent/Recursive Neural Networks (RNN) — ICLR 2017 Discoveries

This blog post gives an overview of Deep Learning with Recurrent/Recursive Neural Networks (RNN) related papers submitted to ICLR 2017, see underneath for the list of papers. If you want to learn more about RNN check out Andrej Karpathy’s The Unreasonable Effectiveness of Recurrent Neural Networks and Pascanu, Gulcehre, Cho and Bengio’s How to Construct Deep Recurrent Neural Networks.

24. Deep Learning with Generative and Generative Adverserial Networks — ICLR 2017 Discoveries

This blog post gives an overview of Deep Learning with Generative and Adverserial Networks related papers submitted to ICLR 2017, see underneath for the list of papers. Want to learn about these topics? See OpenAI’s article about Generative Models and Ian Goodfellow et.al’s paper about Generative Adversarial Networks.

25. Deep Learning for Natural Language Processing — ICLR 2017 Discoveries

This blog post gives an overview of Natural Language Processing related papers submitted to ICLR 2017, see underneath for the list of papers. If you want to learn about Deep Learning with NLP check out Stanford’s CS224d: Deep Learning for Natural Language Processing

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Deep Learning with Residual Networks

This posting is recent papers related to residual networks (i.e. very deep networks). Check out Microsoft Research’s paper Deep Residual Learning for Image Recognition and Kaiming He’s ICML 2016 Tutorial Deep Residual Learning, Deep Learning Gets Way Deeper

Best regards,
Amund Tveit

Year  Title Author
2016   Label distribution based facial attractiveness computation by deep residual learning  S Liu, B Li, Y Fan, Z Guo, A Samal
2016   Unsupervised Domain Adaptation with Residual Transfer Networks  M Long, J Wang, MI Jordan
2016   Deeper Depth Prediction with Fully Convolutional Residual Networks  I Laina, C Rupprecht, V Belagiannis, F Tombari
2016   Deep Residual Learning for Compressed Sensing CT Reconstruction via Persistent Homology Analysis  Y Han, J Yoo, JC Ye
2016   Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex  Q Liao, T Poggio
2016   Deep Cross Residual Learning for Multitask Visual Recognition  B Jou, SF Chang
2016   Identity Mappings in Deep Residual Networks  K He, X Zhang, S Ren, J Sun
2016   Brain tumor classification of microscopy images using deep residual learning  Y Ishikawa, K Washiya, K Aoki, H Nagahashi
2016   Convolutional Residual Memory Networks  J Moniz, C Pal
2016   Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes  T Pohlen, A Hermans, M Mathias, B Leibe
2016   Aggregated Residual Transformations for Deep Neural Networks  S Xie, R Girshick, P Dollár, Z Tu, K He
2016   Deep residual networks for plankton classification  X Li, Z Cui
2016   Highway and Residual Networks learn Unrolled Iterative Estimation  K Greff, RK Srivastava, J Schmidhuber
2016   Estimating Depth from Monocular Images as Classification Using Deep Fully Convolutional Residual Networks  Y Cao, Z Wu, C Shen
2016   Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction  J Zhang, Y Zheng, D Qi
2016   Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising  K Zhang, W Zuo, Y Chen, D Meng, L Zhang
2016   Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning  C Szegedy, S Ioffe, V Vanhoucke
2016   Deep Edge Guided Recurrent Residual Learning for Image Super-Resolution  W Yang, J Feng, J Yang, F Zhao, J Liu, Z Guo, S Yan
2016   FusionNet: A deep fully residual convolutional neural network for image segmentation in connectomics  TM Quan, DGC Hilderbrand, WK Jeong
2016   Deep Residual Hashing  S Conjeti, AG Roy, A Katouzian, N Navab
2016   Wide-Slice Residual Networks for Food Recognition  N Martinel, GL Foresti, C Micheloni
2016   VoxResNet: Deep Voxelwise Residual Networks for Volumetric Brain Segmentation  H Chen, Q Dou, L Yu, PA Heng
2015   Current challenges in glioblastoma: intratumour heterogeneity, residual disease and models to predict disease recurrence  HP Ellis, M Greenslade, B Powell, I Spiteri, A Sottoriva
2014   Background Prior Based Salient Object Detection via Deep Reconstruction Residual  J Han, D Zhang, X Hu, L Guo, J Ren, F Wu
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Deep Learning for Traffic Sign Detection and Recognition

Traffic Sign Detection and Recognition is key functionality for self-driving cars. This posting has recent papers in this area. Check also out related posting: Deep Learning for Vehicle Detection and Classification

Best regards,
Amund Tveit
Amund Tveit

Year  Title Author
2016   Road surface traffic sign detection with hybrid region proposal and fast R-CNN  R Qian, Q Liu, Y Yue, F Coenen, B Zhang
2016   Traffic sign classification with deep convolutional neural networks  J CREDI
2016   Real-time Traffic Sign Recognition system with deep convolutional neural network  S Jung, U Lee, J Jung, DH Shim
2016   Traffic Sign Detection and Recognition using Fully Convolutional Network Guided Proposals  Y Zhu, C Zhang, D Zhou, X Wang, X Bai, W Liu
2016   A traffic sign recognition method based on deep visual feature  F Lin, Y Lai, L Lin, Y Yuan
2016   The research on traffic sign recognition based on deep learning  C Li, C Yang
2015   Fast Traffic Sign Recognition with a Rotation Invariant Binary Pattern Based Feature  S Yin, P Ouyang, L Liu, Y Guo, S Wei
2015   Malaysia traffic sign recognition with convolutional neural network  MM Lau, KH Lim, AA Gopalai
2015   Negative-Supervised Cascaded Deep Learning for Traffic Sign Classification  K Xie, S Ge, R Yang, X Lu, L Sun
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Deep Learning for Vehicle Detection and Classification

Update: 2017-Feb-03 – launched new service – ai.amundtveit.com (navigation and search in papers). Try e.g. out its Vehicle, Car and Driving pages.


This posting has recent papers about vehicle (e.g. car) detection and classification, e.g. for selv-driving/autonomous cars. Related: check also out Nvidia‘s End-to-End Deep Learning for Self-driving Cars and Udacity‘s Self-Driving Car Engineer (Nanodegree).

Best regards,

<a href=”https://amundtveit.com/about/”>Amund Tveit</a> (<a href=”https://twitter.com/atveit”>@atveit</a>)

Year  Title Author
2016   Vehicle Classification using Transferable Deep Neural Network Features  Y Zhou, NM Cheung
2016   A Hybrid Fuzzy Morphology And Connected Components Labeling Methods For Vehicle Detection And Counting System  C Fatichah, JL Buliali, A Saikhu, S Tena
2016   Evaluation of vehicle interior sound quality using a continuous restricted Boltzmann machine-based DBN  HB Huang, RX Li, ML Yang, TC Lim, WP Ding
2016   An Automated Traffic Surveillance System with Aerial Camera Arrays: Data Collection with Vehicle Tracking  X Zhao, D Dawson, WA Sarasua, ST Birchfield
2016   Vehicle type classification via adaptive feature clustering for traffic surveillance video  S Wang, F Liu, Z Gan, Z Cui
2016   Vehicle Detection in Satellite Images by Incorporating Objectness and Convolutional Neural Network  S Qu, Y Wang, G Meng, C Pan
2016   DAVE: A Unified Framework for Fast Vehicle Detection and Annotation  Y Zhou, L Liu, L Shao, M Mellor
2016   3D Fully Convolutional Network for Vehicle Detection in Point Cloud  B Li
2016   A Deep Learning-Based Approach to Progressive Vehicle Re-identification for Urban Surveillance  X Liu, W Liu, T Mei, H Ma
2016   TraCount: a deep convolutional neural network for highly overlapping vehicle counting  S Surya, RV Babu
2016   Pedestrian, bike, motorcycle, and vehicle classification via deep learning: Deep belief network and small training set  YY Wu, CM Tsai
2016   Fast Vehicle Detection in Satellite Images Using Fully Convolutional Network  J Hu, T Xu, J Zhang, Y Yang
2016   Local Tiled Deep Networks for Recognition of Vehicle Make and Model  Y Gao, HJ Lee
2016   Vehicle detection based on visual saliency and deep sparse convolution hierarchical model  Y Cai, H Wang, X Chen, L Gao, L Chen
2016   Sound quality prediction of vehicle interior noise using deep belief networks  HB Huang, XR Huang, RX Li, TC Lim, WP Ding
2016   Accurate On-Road Vehicle Detection with Deep Fully Convolutional Networks  Z Jie, WF Lu, EHF Tay
2016   Fault Detection and Identification of Vehicle Starters and Alternators Using Machine Learning Techniques  E Seddik
2016   Fault diagnosis network design for vehicle on-board equipments of high-speed railway: A deep learning approach  J Yin, W Zhao
2016   Real-time state-of-health estimation for electric vehicle batteries: A data-driven approach  G You, S Park, D Oh
2016   The Precise Vehicle Retrieval in Traffic Surveillance with Deep Convolutional Neural Networks  B Su, J Shao, J Zhou, X Zhang, L Mei, C Hu
2016   Online vehicle detection using deep neural networks and lidar based preselected image patches  S Lange, F Ulbrich, D Goehring
2016   A closer look at Faster R-CNN for vehicle detection  Q Fan, L Brown, J Smith
2016   Appearance-based Brake-Lights recognition using deep learning and vehicle detection  JG Wang, L Zhou, Y Pan, S Lee, Z Song, BS Han
2016   Night time vehicle detection algorithm based on visual saliency and deep learning  Y Cai, HW Xiaoqiang Sun, LCH Jiang
2016   Vehicle classification in WAMI imagery using deep network  M Yi, F Yang, E Blasch, C Sheaff, K Liu, G Chen, H Ling
2015   VeTrack: Real Time Vehicle Tracking in Uninstrumented Indoor Environments  M Zhao, T Ye, R Gao, F Ye, Y Wang, G Luo
2015   Vehicle Color Recognition in The Surveillance with Deep Convolutional Neural Networks  B Su, J Shao, J Zhou, X Zhang, L Mei
2015   Vehicle Speed Prediction using Deep Learning  J Lemieux, Y Ma
2015   Monza: Image Classification of Vehicle Make and Model Using Convolutional Neural Networks and Transfer Learning  D Liu, Y Wang
2015   Night Time Vehicle Sensing in Far Infrared Image with Deep Learning  H Wang, Y Cai, X Chen, L Chen
2015   A Vehicle Type Recognition Method based on Sparse Auto Encoder  HL Rong, YX Xia
2015   Occluded vehicle detection with local connected deep model  H Wang, Y Cai, X Chen, L Chen
2015   Performance Evaluation of the Neural Network based Vehicle Detection Models  K Goyal, D Kaur
2015   A Smartphone-based Connected Vehicle Solution for Winter Road Surface Condition Monitoring  MA Linton
2015   Vehicle Logo Recognition System Based on Convolutional Neural Networks With a Pretraining Strategy  Y Huang, R Wu, Y Sun, W Wang, X Ding
2015   SiftKeyPre: A Vehicle Recognition Method Based on SIFT Key-Points Preference in Car-Face Image  CY Zhang, XY Wang, J Feng, Y Cheng
2015   Vehicle Detection in Aerial Imagery: A small target detection benchmark  S Razakarivony, F Jurie
2015   Vehicle license plate recognition using visual attention model and deep learning  D Zang, Z Chai, J Zhang, D Zhang, J Cheng
2015   Domain adaption of vehicle detector based on convolutional neural networks  X Li, M Ye, M Fu, P Xu, T Li
2015   Trainable Convolutional Network Apparatus And Methods For Operating A Robotic Vehicle  P O’connor, E Izhikevich
2015   Vehicle detection and classification based on convolutional neural network  D He, C Lang, S Feng, X Du, C Zhang
2015   The AdaBoost algorithm for vehicle detection based on CNN features  X Song, T Rui, Z Zha, X Wang, H Fang
2015   Deep neural networks-based vehicle detection in satellite images  Q Jiang, L Cao, M Cheng, C Wang, J Li
2015   Vehicle License Plate Recognition Based on Extremal Regions and Restricted Boltzmann Machines  C Gou, K Wang, Y Yao, Z Li
2014   Multi-modal Sensor Registration for Vehicle Perception via Deep Neural Networks  M Giering, K Reddy, V Venugopalan
2014   Mooting within the curriculum as a vehicle for learning: student perceptions  L Jones, S Field
2014   Vehicle Type Classification Using Semi-Supervised Convolutional Neural Network  Z Dong, Y Wu, M Pei, Y Jia
2014   Vehicle License Plate Recognition With Random Convolutional Networks  D Menotti, G Chiachia, AX Falcao, VJO Neto
2014   Vehicle Type Classification Using Unsupervised Convolutional Neural Network  Z Dong, M Pei, Y He, T Liu, Y Dong, Y Jia
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Deep Learning with Long Short-Term Memory (LSTM)

This blog post has some recent papers about Deep Learning with Long-Short Term Memory (LSTM). To get started I recommend checking out Christopher Olah’s Understanding LSTM Networks and Andrej Karpathy’s The Unreasonable Effectiveness of Recurrent Neural Networks. This blog post is complemented by Deep Learning with Recurrent/Recursive Neural Networks (RNN) – ICLR 2017 Discoveries.

Best regards,
Amund Tveit

Year  Title Author
2016   Look, Listen and Learn-A Multimodal LSTM for Speaker Identification  J Ren, Y Hu, YW Tai, C Wang, L Xu, W Sun, Q Yan
2016   Leveraging Sentence-level Information with Encoder LSTM for Natural Language Understanding  G Kurata, B Xiang, B Zhou, M Yu
2016   Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition  FJ Ordóñez, D Roggen
2016   Exploiting LSTM structure in deep neural networks for speech recognition  T He, J Droppo
2016   A Comprehensive Study of Deep Bidirectional LSTM RNNs for Acoustic Modeling in Speech Recognition  A Zeyer, P Doetsch, P Voigtlaender, R Schlüter, H Ney
2016   Geometric Scene Parsing with Hierarchical LSTM  Z Peng, R Zhang, X Liang, L Lin
2016   LSTM Networks for Mobile Human Activity Recognition  Y Chen, K Zhong, J Zhang, Q Sun, X Zhao
2016   Learning Natural Language Inference using Bidirectional LSTM model and Inner-Attention  Y Liu, C Sun, L Lin, X Wang
2016   Facing Realism in Spontaneous Emotion Recognition from Speech: Feature Enhancement by Autoencoder with LSTM Neural Networks  Z Zhang, F Ringeval, J Han, J Deng, E Marchi
2016   Contextual LSTM (CLSTM) models for Large scale NLP tasks  S Ghosh, O Vinyals, B Strope, S Roy, T Dean, L Heck
2016   Bidirectional LSTM Networks Employing Stacked Bottleneck Features for Expressive Speech-Driven Head Motion Synthesis  K Haag, H Shimodaira
2016   Beyond Frame-level CNN: Saliency-aware 3D CNN with LSTM for Video Action Recognition  J Song, H Shen
2015   Learning Statistical Scripts with LSTM Recurrent Neural Networks  K Pichotta, RJ Mooney
2015   A deep bidirectional LSTM approach for video-realistic talking head  B Fan, L Xie, S Yang, L Wang, FK Soong
2015   Maxout neurons for deep convolutional and LSTM neural networks in speech recognition  M Cai, J Liu
2015   Scene Analysis by Mid-level Attribute Learning using 2D LSTM networks and an Application to Web-image Tagging  W Byeon, M Liwicki, TM Breuel
2015   Learning to Diagnose with LSTM Recurrent Neural Networks  ZC Lipton, DC Kale, C Elkan, R Wetzell
2015   Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting  SHI Xingjian, Z Chen, H Wang, DY Yeung, W Wong
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Deep Learning in Finance

This posting has recent publications about Deep Learning in Finance (e.g. stock market prediction)

Best regards,
Amund Tveit

Year  Title Author
2016   Genetic deep neural networks using different activation functions for financial data mining  LM Zhang
2016   Computational Intelligence and Financial Markets: A Survey and Future Directions  RC Cavalcante, RC Brasileiro, VLF Souza, JP Nobrega
2016   Classification-based Financial Markets Prediction using Deep Neural Networks  M Dixon, D Klabjan, JH Bang
2016   Exploiting Twitter Moods to Boost Financial Trend Prediction Based on Deep Network Models  Y Huang, K Huang, Y Wang, H Zhang, J Guan, S Zhou
2016 Deep Learning in Finance J. B. Heaton, N. G. Polson, J. H. Witte
2016   Deep Direct Reinforcement Learning for Financial Signal Representation and Trading.  Y Deng, F Bao, Y Kong, Z Ren, Q Dai
2015   Improving Decision Analytics with Deep Learning: The Case of Financial Disclosures  R Fehrer, S Feuerriegel
2015   An application of deep learning for trade signal prediction in financial markets  AC Turkmen, AT Cemgil
2015   Deep Learning for Multivariate Financial Time Series  G BATRES
2015   Deep Modeling Complex Couplings within Financial Markets  W Cao, L Hu, L Cao
2015   Leverage Financial News to Predict Stock Price Movements Using Word Embeddings and Deep Neural Networks  Y Peng, H Jiang
2014   GPU Implementation of a Deep Learning Network for Financial Prediction  R Kumar, AK Cheema
2016   Non-Conformity Detection in High-Dimensional Time Series of Stock Market Data  A Kasuga, Y Ohsawa, T Yoshino, S Ashida
2016   Artificial neural networks approach to the forecast of stock market price movements  L Di Persio, O Honchar
2016   Forecasting Trade Direction and Size of Future Contracts Using Deep Belief Network  A Lai, MK Li, FW Pong
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Deep Learning for Information Retrieval and Learning to Rank

This posting is about Deep Learning for Information Retrieval and Learning to Rank (i.e. of interest if developing search engines). The posting is complemented by the posting Deep Learning for Question Answering. To get started I recommend checking out Jianfeng Gao‘s (Deep Learning Technology Center at Microsoft Research) presentation Deep Learning for Web Search and Natural Language Processing.

Of partial relevance is the posting Deep Learning for Sentiment Analysis, the posting about Embedding for NLP with Deep Learning, the posting about Deep Learning for Natural Language Processing (ICLR 2017 discoveries), and the posting about Deep Learning for Recommender Systems

Best regards,
Amund Tveit

 

Year  Title Author
2016   Efficient Inference, Search and Evaluation for Latent Variable Models of Text with Applications to Information Retrieval and Machine Translation  K Krstovski
2016   Content-based Information Retrieval via Nearest Neighbor Search  Y Huang
2016   Information retrieval in instant messaging platforms using Recurrent Neural Networks  JHP Suorra
2016   Information Retrieval with Dimensionality Reduction using Deep Belief Networks  V Slot
2015   Deep Sentence Embedding Using the Long Short Term Memory Network: Analysis and Application to Information Retrieval  H Palangi, L Deng, Y Shen, J Gao, X He, J Chen
2014   A compositional hierarchical model for music information retrieval  M Pesek, A Leonardis, M Marolt
2014   Log-Bilinear Document Language Model for Ad-hoc Information Retrieval  X Tu, J Luo, B Li, T He
2016   Learning to rank chemical compounds based on their multiprotein activity using Random Forests  D Lesniak, M l Kowalik, P Kruk
2016   Multilevel Syntactic Parsing Based on Recursive Restricted Boltzmann Machines and Learning to Rank  J Xu, H Chen, S Zhou, B He
2016   Automatic Face Recognition Based On Learning to Rank for Image Quality Assessment  K Busa, G Tejaswi
2015   Application of Learning to Rank to protein remote homology detection  B Liu, J Chen, X Wang
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Deep Learning for Question Answering

This posting presents recent publications related to Deep Learning for Question Answering. Question Answering is described as “a computer science discipline within the fields of information retrieval and natural language processing (NLP), which is concerned with building systems that automatically answer questions posed by humans in a natural language”. I’ll also publish postings about Deep Learning for Information Retrieval and Learning to Rank today.

Best regards,
Amund Tveit

Year  Title Author
2016   Skipping Word: A Character-Sequential Representation based Framework for Question Answering  L Meng, Y Li, M Liu, P Shu
2016   Semantic computation in geography question answering  S Zhao, Y Zheng, C Zhu, T Zhao, S Li
2016   Open-ended visual question answering  I Masuda Mora
2016   Creating Causal Embeddings for Question Answering with Minimal Supervision  R Sharp, M Surdeanu, P Jansen, P Clark, M Hammond
2016   Deep Feature Fusion Network for Answer Quality Prediction in Community Question Answering  SP Suggu, KN Goutham, MK Chinnakotla
2016   ECNU at SemEval-2016 Task 3: Exploring traditional method and deep learning method for question retrieval and answer ranking in community question answering  G Wu, M Lan
2016   Ask Your Neurons: A Deep Learning Approach to Visual Question Answering  M Malinowski, M Rohrbach, M Fritz
2016   Revisiting Visual Question Answering Baselines  A Jabri, A Joulin, L van der Maaten
2016   End to End Long Short Term Memory Networks for Non-Factoid Question Answering  D Cohen, WB Croft
2016   Question Answering on Linked Data: Challenges and Future Directions  S Shekarpour, D Lukovnikov, AJ Kumar, K Endris
2016   Extracting Medical Knowledge from Crowdsourced Question Answering Website  Y Li, C Liu, N Du, W Fan, Q Li, J Gao, C Zhang, H Wu
2015   Building a Large-scale Multimodal Knowledge Base for Visual Question Answering  Y Zhu, C Zhang, C Ré, L Fei
2015   Answer Sequence Learning with Neural Networks for Answer Selection in Community Question Answering  X Zhou, B Hu, Q Chen, B Tang, X Wang
2015   Empirical Study on Deep Learning Models for Question Answering  Y Yu, W Zhang, CW Hang, B Zhou
2015   Simple Baseline for Visual Question Answering  B Zhou, Y Tian, S Sukhbaatar, A Szlam, R Fergus
2015   Chess Q&A: Question Answering on Chess Games  V Cirik, LP Morency, E Hovy
2015   Predicting the Quality of User-Generated Answers Using Co-Training in Community-based Question Answering Portals  B Liu, J Feng, M Liu, H Hu, X Wang
2015   Open Domain Question Answering via Semantic Enrichment  H Sun, H Ma, W Yih, CT Tsai, J Liu, MW Chang
2015   Visual Question Answering using Deep Learning  S Agrawal, A Mukherjee
2015   Learning Semantic Representation with Neural Networks for Community Question Answering Retrieval  G Zhou, Y Zhou, T He, W Wu
2015   WIKIQA: A Challenge Dataset for Open-Domain Question Answering  Y Yang, W Yih, C Meek
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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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