Deep Learning in Energy Production


This blog post has recent publications about use of Deep Learning in Energy Production context (wind, gas and oil), e.g. wind power prediction, turbine risk assessment, reservoir discovery and price forecasting.

Best regards,

Amund Tveit


Year  Title Author
2017 Short-term Wind Energy Prediction Algorithm Based on SAGA-DBNs  W Fei, WU Zhong
2017 Wind Power Prediction using Deep Neural Network based Meta Regression and Transfer Learning  AS Qureshi, A Khan, A Zameer, A Usman
2017 Wind Turbine Failure Risk Assessment Model Based on DBN  C Fei, F Zhongguang
2017 The optimization of wind power interval forecast  X Yu, H Zang
2016 Deep Learning for Wind Speed Forecasting in Northeastern Region of Brazil  AT Sergio, TB Ludermir
2016 A very short term wind power prediction approach based on Multilayer Restricted Boltzmann Machine  X Peng, L Xiong, J Wen, Y Xu, W Fan, S Feng, B Wang
2016 Short-term prediction of wind power based on deep Long Short-Term Memory  Q Xiaoyun, K Xiaoning, Z Chao, J Shuai, M Xiuda
2016 Deep belief network based deterministic and probabilistic wind speed forecasting approach  HZ Wang, GB Wang, GQ Li, JC Peng, YT Liu
2016 A hybrid wind power prediction method  Y Tao, H Chen
2016 Deep learning based ensemble approach for probabilistic wind power forecasting  H Wang, G Li, G Wang, J Peng, H Jiang, Y Liu
2016 A hybrid wind power forecasting model based on data mining and wavelets analysis  R Azimi, M Ghofrani, M Ghayekhloo
2016 ELM Based Representational Learning for Fault Diagnosis of Wind Turbine Equipment  Z Yang, X Wang, PK Wong, J Zhong
2015 Deep Neural Networks for Wind Energy Prediction  D Díaz, A Torres, JR Dorronsoro
2015 Predictive Deep Boltzmann Machine for Multiperiod Wind Speed Forecasting  CY Zhang, CLP Chen, M Gan, L Chen
2015 Resilient Propagation for Multivariate Wind Power Prediction  J Stubbemann, NA Treiber, O Kramer
2015 Transfer learning for short-term wind speed prediction with deep neural networks  Q Hu, R Zhang, Y Zhou
2014 Wind Power Prediction and Pattern Feature Based on Deep Learning Method  Y Tao, H Chen, C Qiu


Year  Title Author
2017   Sample Document–Inversion Of The Permeability Of A Tight Gas Reservoir With The Combination Of A Deep Boltzmann Kernel …  L Zhu, C Zhang, Y Wei, X Zhou, Y Huang, C Zhang
2017   Deep Learning: Chance and Challenge for Deep Gas Reservoir Identification  C Junxing, W Shikai
2016   Finite-sensor fault-diagnosis simulation study of gas turbine engine using information entropy and deep belief networks  D Feng, M Xiao, Y Liu, H Song, Z Yang, Z Hu
2015   On Accurate and Reliable Anomaly Detection for Gas Turbine Combustors: A Deep Learning Approach  W Yan, L Yu
2015   A Review of Datasets and Load Forecasting Techniques for Smart Natural Gas and Water Grids: Analysis and Experiments.  M Fagiani, S Squartini, L Gabrielli, S Spinsante
2015   Short-term load forecasting for smart water and gas grids: A comparative evaluation  M Fagiani, S Squartini, R Bonfigli, F Piazza
2015   The early-warning model of equipment chain in gas pipeline based on DNN-HMM  J Qiu, W Liang, X Yu, M Zhang, L Zhang


Year  Title Author
2017   Development of a New Correlation for Bubble Point Pressure in Oil Reservoirs Using Artificial Intelligent Technique  S Elkatatny, M Mahmoud
2017   A deep learning ensemble approach for crude oil price forecasting  Y Zhao, J Li, L Yu
2016   Automatic Detection and Classification of Oil Tanks in Optical Satellite Images Based on Convolutional Neural Network  Q Wang, J Zhang, X Hu, Y Wang
2015   A Hierarchical Oil Tank Detector With Deep Surrounding Features for High-Resolution Optical Satellite Imagery  L Zhang, Z Shi, J Wu
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Deep Learning for Protein(omics)


This blog post has recent publications related to Deep Learning for proteinomics (the study of proteins). Proteins are a set of molecules in the human (and animal) bodies (probably best known for their role related to muscle mass and in DNA replication).

Wikipedia describes proteins as:

    Proteins (/ˈproʊˌtiːnz/ or /ˈproʊti.ᵻnz/) are large biomolecules, or macromolecules, consisting of one or more long chains of amino acid residues. Proteins perform a vast array of functions within organisms, including catalysing metabolic reactions, DNA replication, responding to stimuli, and transporting molecules from one location to another. Proteins differ from one another primarily in their sequence of amino acids, which is dictated by the nucleotide sequence of their genes, and which usually results in protein folding into a specific three-dimensional structure that determines its activity.

Best regards,
Amund Tveit (WeChat ID: AmundTveit)

Year  Title Author
2017   Deep Recurrent Neural Network for Protein Function Prediction from Sequence  XL Liu
2017   Sequence-based prediction of protein protein interaction using a deep-learning algorithm  T Sun, B Zhou, L Lai, J Pei
2017   Protein Model Quality Assessment: A Machine Learning Approach  K Uziela
2017   Deep convolutional neural networks for detecting secondary structures in protein density maps from cryo-electron microscopy  R Li, D Si, T Zeng, S Ji, J He
2017   Towards recognition of protein function based on its structure using deep convolutional networks  A Tavanaei, AS Maida, A Kaniymattam
2017   Improved protein model quality prediction by changing the target function  K Uziela, D Menendez Hurtado, N Shu, B Wallner
2017   A Novel Model Based On Fcm-Lm Algorithm For Prediction Of Protein Folding Rate  L Liu, M Ma, J Cui
2017   EPSILON-CP: using deep learning to combine information from multiple sources for protein contact prediction  K Stahl, M Schneider, O Brock
2017   Prediction of protein function using a deep convolutional neural network ensemble  EI Zacharaki
2017   Protein Function Prediction using Deep Restricted Boltzmann Machines  X Zou, G Wang, G Yu
2017   Next-Step Conditioned Deep Convolutional Neural Networks Improve Protein Secondary Structure Prediction  A Busia, N Jaitly
2017   Predicting membrane protein contacts from non-membrane proteins by deep transfer learning  Z Li, S Wang, Y Yu, J Xu
2017   A Template-Based Protein Structure Reconstruction Method Using Deep Autoencoder Learning  H Li, Q Lyu, J Cheng
2017   DNpro: A Deep Learning Network Approach to Predicting Protein Stability Changes Induced by Single-Site Mutations  X Zhou, J Cheng
2017   Computational Methods for the Prediction of Drug-Target Interactions from Drug Fingerprints and Protein Sequences by Stacked Auto-Encoder Deep Neural Network  L Wang, ZH You, X Chen, SX Xia, F Liu, X Yan, Y Zhou
2017   Multi-task Deep Neural Networks in Automated Protein Function Prediction  AS Rifaioglu, T Doğan, MJ Martin, R Cetin
2016   AUC-Maximized Deep Convolutional Neural Fields for Protein Sequence Labeling  S Wang, S Sun, J Xu
2016   Evaluation of Protein Structural Models Using Random Forests  R Cao, T Jo, J Cheng
2016   A Protein Domain and Family Based Approach to Rare Variant Association Analysis  TG Richardson, HA Shihab, MA Rivas, MI McCarthy
2016   Protein Sequencing And Neural Network Classification Methods  V Indarni, SK Terala, PV Bhushan, MR Ireddy
2016   Accurate prediction of docked protein structure similarity using neural networks and restricted Boltzmann machines  R Farhoodi, B Akbal
2016   Identification of thermostabilizing mutations for a membrane protein whose three‐dimensional structure is unknown  Y Kajiwara, S Yasuda, Y Takamuku, T Murata
2016   Identification of Genetic Sequences Recognized by Human SC35 Protein Using Artificial Neural Networks: A Deep Learning Approach  AJ Luke, S Fergione
2016   MUST-CNN: A MUltilayer Shift-and-sTitch Deep Convolutional Architecture for Sequence-based Protein Structure Prediction  Z Lin, Y Qi
2016   Protein Secondary Structure Prediction Using Deep Multi-scale Convolutional Neural Networks and Next-Step Conditioning  A Busia, J Collins, N Jaitly
2016   A computational framework for disease grading using protein signatures  E Zerhouni, B Prisacari, Q Zhong, P Wild, M Gabrani
2016   ProtPOS: a python package for the prediction of protein preferred orientation on a surface  JCF Ngai, PI Mak, SWI Siu
2016   DeepQA: Improving the estimation of single protein model quality with deep belief networks  R Cao, D Bhattacharya, J Hou, J Cheng
2016   Protein contact prediction from amino acid co-evolution using convolutional networks for graph-valued images  V Golkov, MJ Skwark, A Golkov, A Dosovitskiy, T Brox
2016   Protein Secondary Structure Prediction by using Deep Learning Method  Y Wang, H Mao, Z Yi
2016   On the importance of composite protein multiple ligand interactions in protein pockets  S Tonddast‐Navaei, B Srinivasan, J Skolnick
2016   Protein function in precision medicine: deep understanding with machine learning  B Rost, P Radivojac, Y Bromberg
2016   Protein Residue-Residue Contact Prediction Using Stacked Denoising Autoencoders  IV Luttrell, J Bailey
2016   Protein Residue Contacts and Prediction Methods  B Adhikari, J Cheng
2016   RaptorX-Property: a web server for protein structure property prediction.  S Wang, W Li, S Liu, J Xu
2016   AUCpreD: proteome-level protein disorder prediction by AUC-maximized deep convolutional neural fields  S Wang, J Ma, J Xu
2016   Benchmarking Deep Networks for Predicting Residue-Specific Quality of Individual Protein Models in CASP11  T Liu, Y Wang, J Eickholt, Z Wang
2015   Theory, Methods, and Applications of Coevolution in Protein Contact Prediction  J Ma, S Wang
2015   A topological approach for protein classification  Z Cang, L Mu, K Wu, K Opron, K Xia, GW Wei
2015   Application of Learning to Rank to protein remote homology detection  B Liu, J Chen, X Wang
2015   Improving Protein Fold Recognition by Deep Learning Networks  T Jo, J Hou, J Eickholt, J Cheng
2015   Proteins, physics and probability kinematics: a Bayesian formulation of the protein folding problem  T Hamelryck, W Boomsma, J Ferkinghoff
2015   DeepCNF-D: Predicting Protein Order/Disorder Regions by Weighted Deep Convolutional Neural Fields  S Wang, S Weng, J Ma, Q Tang
2015   A deep learning framework for modeling structural features of RNA-binding protein targets  S Zhang, J Zhou, H Hu, H Gong, L Chen, C Cheng
2015   A serum protein test for improved prognostic stratification of patients with myelodysplastic syndrome (MDS)  J Roder, J Löffler
2015   An Overview of Practical Applications of Protein Disorder Prediction and Drive for Faster, More Accurate Predictions  X Deng, J Gumm, S Karki, J Eickholt, J Cheng
2015   A panel of mass spectrometry based serum protein tests for predicting graft-versus-host disease (GvHD) and its severity  H Roder, AC Hoffmann, J Roder, M Koldehoff
2015   Learning Deep Architectures for Protein Structure Prediction  K Baek
2015   Protein secondary structure prediction using deep convolutional neural fields  S Wang, J Peng, J Ma, J Xu
2015   Protein sequence labelling by AUC-maximized Deep Convolutional Neural Fields  S Wang, J Ma, S Sun, J Xu
2015   Fast loop modeling for protein structures  J Zhang, S Nguyen, Y Shang, D Xu, I Kosztin
2015   Introducing Students to Protein Analysis Techniques: Separation and Comparative Analysis of Gluten Proteins in Various Wheat Strains  AL Pirinelli, JC Trinidad, NLB Pohl
2014   Predicting backbone Cα angles and dihedrals from protein sequences by stacked sparse auto‐encoder deep neural network  J Lyons, A Dehzangi, R Heffernan, A Sharma
2014   Improved contact predictions using the recognition of protein like contact patterns.  MJ Skwark, D Raimondi, M Michel, A Elofsson
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Deep Learning for Embedded Systems


This blog post has recent publications related to Deep Learning for Embedded Systems (e.g. computer systems in toys, biometrics, cars, kitchen equipment, medical equipment such as bionic eyes, etc).

Wikipedia defines Embedded systems as:

    An embedded system is a computer system with a dedicated function within a larger mechanical or electrical system, often with real-time computing constraints.[1][2] It is embedded as part of a complete device often including hardware and mechanical parts. Embedded systems control many devices in common use today.[3] Ninety-eight percent of all microprocessors are manufactured as components of embedded systems

Best regards,
Amund Tveit (WeChat ID: AmundTveit – Twitter: atveit)

Year  Title Author
2017   Six Degree-of-Freedom Localization of Endoscopic Capsule Robots using Recurrent Neural Networks embedded into a Convolutional Neural Network  M Turan, A Abdullah, R Jamiruddin, H Araujo
2017   Two-Bit Networks for Deep Learning on Resource-Constrained Embedded Devices  W Meng, Z Gu, M Zhang, Z Wu
2017   14.1 A 2.9 TOPS/W deep convolutional neural network SoC in FD-SOI 28nm for intelligent embedded systems  G Desoli, N Chawla, T Boesch, S Singh, E Guidetti
2017   Characterization of Symbolic Rules Embedded in Deep DIMLP Networks: A Challenge to Transparency of Deep Learning  G Bologna, Y Hayashi
2017   Moving Object Detection in Heterogeneous Conditions in Embedded Systems  A Garbo, S Quer
2016   Re-architecting the on-chip memory sub-system of machine-learning accelerator for embedded devices  Y Wang, H Li, X Li
2016   DELAROSE: A Case Example of the Value of Embedded Course Content and Assessment in the Workplace  JSG Wells, M Bergin, C Ryan
2016   Neurosurgery Conference Experience Embedded within PCOM’s Clinical and Basic Neuroscience Curriculum: An Active Learning Model  J Okun, S Yocom, M McGuiness, M Bell, D Appelt
2016   Scene Parsing using Inference Embedded Deep Networks  S Bu, P Han, Z Liu, J Han
2016   Improving Deep Learning Accuracy with Noisy Autoencoders Embedded Perturbative Layers  L Xia, X Zhang, B Li
2016   Noise Robust Keyword Spotting Using Deep Neural Networks For Embedded Platforms  R Abdelmoula
2016   14.1 A 126.1 mW real-time natural UI/UX processor with embedded deep-learning core for low-power smart glasses  S Park, S Choi, J Lee, M Kim, J Park, HJ Yoo
2016   A wearable mobility aid for the visually impaired based on embedded 3D vision and deep learning  M Poggi, S Mattoccia
2016   Optimizing convolutional neural networks on embedded platforms with OpenCL  A Lokhmotov, G Fursin
2016   Demonstration Abstract: Accelerating Embedded Deep Learning Using DeepX  ND Lane, S Bhattacharya, P Georgiev, C Forlivesi
2016   Feedback recurrent neural network-based embedded vector and its application in topic model  L Li, S Gan, X Yin
2016   Human Pose Estimation from Depth Images via Inference Embedded Multi-task Learning  K Wang, S Zhai, H Cheng, X Liang, L Lin
2015   Memory Heat Map: Anomaly Detection in Real-Time Embedded Systems Using Memory Behavior  MK Yoon, S Mohan, J Choi, L Sha
2015   Accelerating real-time embedded scene labeling with convolutional networks  L Cavigelli, M Magno, L Benini
2015   Business meeting training on its head: inverted and embedded learning  E Van Praet
2015   CNN optimizations for embedded systems and FFT  A Vasilyev
2015   Learning Socially Embedded Visual Representation from Scratch  S Liu, P Cui, W Zhu, S Yang
2015   Inter-Tile Reuse Optimization Applied to Bandwidth Constrained Embedded Accelerators  M Peemen, B Mesman, H Corporaal
2015   Emotion recognition from embedded bodily expressions and speech during dyadic interactions  PM Müller, S Amin, P Verma, M Andriluka, A Bulling
2015   Incremental extreme learning machine based on deep feature embedded  J Zhang, S Ding, N Zhang, Z Shi
2015   Utilizing deep neural nets for an embedded ECG-based biometric authentication system  A Page, A Kulkarni, T Mohsenin
2015   A scalable and adaptable probabilistic model embedded in an electronic nose for intelligent sensor fusion  CT Tang, CM Huang, KT Tang, H Chen
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Deep Learning for Magnetic Resonance Imaging (MRI)


Magnetic Resonance Imaging (MRI) can be used in many types of diagnosis e.g. cancer, alzheimer, cardiac and muscle/skeleton issues. This blog post has recent publications of Deep Learning applied to MRI (health-related) data, e.g. for segmentation, detection, demonising and classification.

MRI is described in Wikipedia as:

    Magnetic resonance imaging (MRI) is a medical imaging technique used in radiology to form pictures of the anatomy and the physiological processes of the body in both health and disease. MRI scanners use strong magnetic fields, radio waves, and field gradients to generate images of the organs in the body.

Best regards,
Amund Tveit

Year  Title Author
2017   Residual and Plain Convolutional Neural Networks for 3D Brain MRI Classification  S Korolev, A Safiullin, M Belyaev, Y Dodonova
2017   Automatic segmentation of the right ventricle from cardiac MRI using a learning‐based approach  MR Avendi, A Kheradvar, H Jafarkhani
2017   Learning a Variational Network for Reconstruction of Accelerated MRI Data  K Hammernik, T Klatzer, E Kobler, MP Recht
2017   A 2D/3D Convolutional Neural Network for Brain White Matter Lesion Detection in Multimodal MRI  L Roa
2017   On hierarchical brain tumor segmentation in MRI using fully convolutional neural networks: A preliminary study  S Pereira, A Oliveira, V Alves, CA Silva
2017   Classification of breast MRI lesions using small-size training sets: comparison of deep learning approaches  G Amit, R Ben
2017   A deep learning network for right ventricle segmentation in short-axis MRI  GN Luo, R An, KQ Wang, SY Dong, HG Zhang
2017   A novel left ventricular volumes prediction method based on deep learning network in cardiac MRI  GN Luo, GX Sun, KQ Wang, SY Dong, HG Zhang
2017   Classification of MRI data using Deep Learning and Gaussian Process-based Model Selection  H Bertrand, M Perrot, R Ardon, I Bloch
2017   Using Deep Learning to Segment Breast and Fibroglanduar Tissue in MRI Volumes  MU Dalmş, G Litjens, K Holland, A Setio, R Mann
2017   Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks  PF Christ, F Ettlinger, F Grün, MEA Elshaera, J Lipkova
2017   Automated Segmentation of Hyperintense Regions in FLAIR MRI Using Deep Learning  P Korfiatis, TL Kline, BJ Erickson
2017   Automatic segmentation of left ventricle in cardiac cine MRI images based on deep learning  T Zhou, I Icke, B Dogdas, S Parimal, S Sampath
2017   Deep artifact learning for compressed sensing and parallel MRI  D Lee, J Yoo, JC Ye
2017   Deep Generative Adversarial Networks for Compressed Sensing Automates MRI  M Mardani, E Gong, JY Cheng, S Vasanawala
2017   3D Motion Modeling and Reconstruction of Left Ventricle Wall in Cardiac MRI  D Yang, P Wu, C Tan, KM Pohl, L Axel, D Metaxas
2017   Estimation of the volume of the left ventricle from MRI images using deep neural networks  F Liao, X Chen, X Hu, S Song
2017   A fully automatic deep learning method for atrial scarring segmentation from late gadolinium-enhanced MRI images  G Yang, X Zhuang, H Khan, S Haldar, E Nyktari, X Ye
2017   Age estimation from brain MRI images using deep learning  TW Huang, HT Chen, R Fujimoto, K Ito, K Wu, K Sato
2017   Segmenting Atrial Fibrosis from Late Gadolinium-Enhanced Cardiac MRI by Deep-Learned Features with Stacked Sparse Auto-Encoders  S Haldar, E Nyktari, X Ye, G Slabaugh, T Wong
2017   Deep Residual Learning For Compressed Sensing Mri  D Lee, J Yoo, JC Ye
2017   Prostate cancer diagnosis using deep learning with 3D multiparametric MRI  S Liu, H Zheng, Y Feng, W Li
2017   Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions  Z Akkus, A Galimzianova, A Hoogi, DL Rubin
2016   Classification of Alzheimer’s Disease Structural MRI Data by Deep Learning Convolutional Neural Networks  S Sarraf, G Tofighi
2016   De-noising of Contrast-Enhanced MRI Sequences by an Ensemble of Expert Deep Neural Networks  A Benou, R Veksler, A Friedman, TR Raviv
2016   A Combined Deep-Learning and Deformable-Model Approach to Fully Automatic Segmentation of the Left Ventricle in Cardiac MRI  MR Avendi, A Kheradvar, H Jafarkhani
2016   Applying machine learning to automated segmentation of head and neck tumour volumes and organs at risk on radiotherapy planning CT and MRI scans  C Chu, J De Fauw, N Tomasev, BR Paredes, C Hughes
2016   A Fully Convolutional Neural Network for Cardiac Segmentation in Short-Axis MRI  PV Tran
2016   An Overview of Techniques for Cardiac Left Ventricle Segmentation on Short-Axis MRI  A Krasnobaev, A Sozykin
2016   Stacking denoising auto-encoders in a deep network to segment the brainstem on MRI in brain cancer patients: a clinical study  J Dolz, N Betrouni, M Quidet, D Kharroubi, HA Leroy
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   Mental Disease Feature Extraction with MRI by 3D Convolutional Neural Network with Multi-channel Input  L Cao, Z Liu, X He, Y Cao, K Li
2016   Deep learning trends for focal brain pathology segmentation in MRI  M Havaei, N Guizard, H Larochelle, PM Jodoin
2016   Identification of Water and Fat Images in Dixon MRI Using Aggregated Patch-Based Convolutional Neural Networks  L Zhao, Y Zhan, D Nickel, M Fenchel, B Kiefer, XS Zhou
2016   Deep MRI brain extraction: A 3D convolutional neural network for skull stripping  J Kleesiek, G Urban, A Hubert, D Schwarz
2016   Active appearance model and deep learning for more accurate prostate segmentation on MRI  R Cheng, HR Roth, L Lu, S Wang, B Turkbey
2016   Recurrent Fully Convolutional Neural Networks for Multi-slice MRI Cardiac Segmentation  RPK Poudel, P Lamata, G Montana
2016   Deep learning predictions of survival based on MRI in amyotrophic lateral sclerosis  HK van der Burgh, R Schmidt, HJ Westeneng
2016   Semantic-Based Brain MRI Image Segmentation Using Convolutional Neural Network  Y Chou, DJ Lee, D Zhang
2016   Abstract WP41: Predicting Acute Ischemic Stroke Tissue Fate Using Deep Learning on Source Perfusion MRI  KC Ho, S El
2016   A new ASM framework for left ventricle segmentation exploring slice variability in cardiac MRI volumes  C Santiago, JC Nascimento, JS Marques
2015   Crohn’s disease segmentation from mri using learned image priors  D Mahapatra, P Schüffler, F Vos, JM Buhmann
2015   Discovery Radiomics for Multi-Parametric MRI Prostate Cancer Detection  AG Chung, MJ Shafiee, D Kumar, F Khalvati
2015   Real-time Dynamic MRI Reconstruction using Stacked Denoising Autoencoder  A Majumdar
2015   q-Space Deep Learning for Twelve-Fold Shorter and Model-Free Diffusion MRI Scans  V Golkov, A Dosovitskiy, P Sämann, JI Sperl
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Deep Learning for Image Super-Resolution (Scale Up)


Scaling down images is a craft, scaling up images is an art

Since in the scaling down to a lower resolution you typically need to remove pixels, but in the case of scaling up you need to invent new pixels. But some Deep Learning models with Convolutional Neural Networks (and frequently Deconvolutional layers) has shown successful to scale up images, this is called Image Super-Resolution. These models are typically trained by taking high resolution images and reducing them to lower resolution and then train in the opposite way. Partially related: Recommend also checking out Odeon et. al’s’s publication: Deconvolution and Checkerboard Artifacts that goes into more detail about the one the core operators used in Image Super-Resolution.

Blog post Illustration Source: Eric Esteve’s 2013 article: Super Resolution bring high end camera image quality to smartphone.

Best regards,

Amund Tveit

Year  Title Author
2017   GUN: Gradual Upsampling Network for single image super-resolution  Y Zhao, R Wang, W Dong, W Jia, J Yang, X Liu, W Gao
2017   Dual Recovery Network with Online Compensation for Image Super-Resolution  S Xia, W Yang, T Zhao, J Liu
2017   A New Single Image Super-resolution Method Based on the Infinite Mixture Model  P Cheng, Y Qiu, X Wang, K Zhao
2017   Underwater Image Super-resolution by Descattering and Fusion  H Lu, Y Li, S Nakashima, H Kim, S Serikawa
2017   Single Image Super-Resolution with a Parameter Economic Residual-Like Convolutional Neural Network  Z Yang, K Zhang, Y Liang, J Wang
2017   Single Image Super-Resolution via Adaptive Transform-Based Nonlocal Self-Similarity Modeling and Learning-Based Gradient Regularization  H Chen, X He, L Qing, Q Teng
2017   Ensemble Based Deep Networks for Image Super-Resolution  Z Huang, L Wang, Y Gong, C Pan
2017   Single Image Super-Resolution Using Multi-Scale Convolutional Neural Network  X Jia, X Xu, B Cai, K Guo
2017   Hyperspectral image super-resolution using deep convolutional neural network  Y Li, J Hu, X Zhao, W Xie, JJ Li
2016   Research on the Natural Image Super-Resolution Reconstruction Algorithm based on Compressive Perception Theory and Deep Learning Model  G Duan, W Hu, J Wang
2016   Image super-resolution with multi-channel convolutional neural networks  Y Kato, S Ohtani, N Kuroki, T Hirose, M Numa
2016   Image super-resolution reconstruction via RBM-based joint dictionary learning and sparse representation  Z Zhang, A Liu, Q Lei
2016   End-to-End Image Super-Resolution via Deep and Shallow Convolutional Networks  Y Wang, L Wang, H Wang, P Li
2016   Single image super-resolution using regularization of non-local steering kernel regression  K Zhang, X Gao, J Li, H Xia
2016   Single image super-resolution via blind blurring estimation and anchored space mapping  X Zhao, Y Wu, J Tian, H Zhang
2016   A Versatile Sparse Representation Based Post-Processing Method for Improving Image Super-Resolution  J Yang, J Guo, H Chao
2016   Robust Single Image Super-Resolution via Deep Networks with Sparse Prior.  D Liu, Z Wang, B Wen, J Yang, W Han, T Huang
2016   EnhanceNet: Single Image Super-Resolution through Automated Texture Synthesis  MSM Sajjadi, B Schölkopf, M Hirsch
2016   Is Image Super-resolution Helpful for Other Vision Tasks?  D Dai, Y Wang, Y Chen, L Van Gool
2016   Cluster-Based Image Super-resolution via Jointly Low-rank and Sparse Representation  N Han, Z Song, Y Li
2016   Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network  C Ledig, L Theis, F Huszar, J Caballero, A Aitken
2016   Image super-resolution using non-local Gaussian process regression  H Wang, X Gao, K Zhang, J Li
2016   A hybrid wavelet convolution network with sparse-coding for image super-resolution  X Gao, H Xiong
2016   Amortised MAP Inference for Image Super-resolution  CK Sønderby, J Caballero, L Theis, W Shi, F Huszár
2016   X-Ray fluorescence image super-resolution using dictionary learning  Q Dai, E Pouyet, O Cossairt, M Walton, F Casadio
2016   Image super-resolution based on convolution neural networks using multi-channel input  GY Youm, SH Bae, M Kim
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   Image Super-Resolution by PSOSEN of Local Receptive Fields Based Extreme Learning Machine  Y Song, B He, Y Shen, R Nian, T Yan
2016   Incorporating Image Priors with Deep Convolutional Neural Networks for Image Super-Resolution  Y Liang, J Wang, S Zhou, Y Gong, N Zheng
2015   Single Image Super-Resolution Via Bm3D Sparse Coding  K Egiazarian, V Katkovnik
2015   Learning a Deep Convolutional Network for Light-Field Image Super-Resolution  Y Yoon, HG Jeon, D Yoo, JY Lee, I Kweon
2015   Single Image Super-Resolution via Image Smoothing  Z Liu, Q Huang, J Li, Q Wang
2015   Deeply Improved Sparse Coding for Image Super-Resolution  Z Wang, D Liu, J Yang, W Han, T Huang
2015   Conditioned Regression Models for Non-Blind Single Image Super-Resolution  GRSSM Rüther, H Bischof
2015   How Useful Is Image Super-resolution to Other Vision Tasks?  D Dai, Y Wang, Y Chen, L Van Gool
2015   Learning Hierarchical Decision Trees for Single Image Super-Resolution  JJ Huang, WC Siu
2015   Single image super-resolution by approximated Heaviside functions  LJ Deng, W Guo, TZ Huang
2015   Jointly Optimized Regressors for Image Super-resolution  D Dai, R Timofte, L Van Gool
2015   Single Image Super-Resolution via Internal Gradient Similarity  Y Xian, Y Tian
2015   Image Super-Resolution Using Deep Convolutional Networks  C Dong, CC Loy, K He, X Tang
2015   Coupled Deep Autoencoder for Single Image Super-Resolution  K Zeng, J Yu, R Wang, C Li, D Tao
2015   Single Image Super-Resolution Using Maximizing Self-Similarity Prior  J Li, Y Wu, X Luo
2015   Accurate Image Super-Resolution Using Very Deep Convolutional Networks  J Kim, JK Lee, KM Lee
2015   Deeply-Recursive Convolutional Network for Image Super-Resolution  J Kim, JK Lee, KM Lee
2015   Single Face Image Super-Resolution via Solo Dictionary Learning  F Juefei
2014   Single image super-resolution via L0 image smoothing  Z Liu, Q Huang, J Li, Q Wang
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Deep Learning for Acoustic Modelling


This blog post has an overview papers related to acoustic modelling primarily for speech recognition but also speech generation (synthesis). See also for a broader set of (at the time of writing 73) recent Deep Learning papers related to acoustics for speech recognition and other applications of acoustics.

Acoustic Modelling is described in Wikipedia as: “An acoustic model is used in Automatic Speech Recognition to represent the relationship between an audio signal and the phonemes or other linguistic units that make up speech. The model is learned from a set of audio recordings and their corresponding transcripts”. 

Blog Post Illustration Photo Source: Professor Mark Gales‘ (University of Cambridge) 2009 presentation Acoustic Modelling for Speech Recognition: Hidden Markov Models and Beyond?

Best regards,

Amund Tveit

Year  Title Author
2017   Investigation on acoustic modeling with different phoneme set for continuous Lhasa Tibetan recognition based on DNN method  H Wang, K Khyuru, J Li, G Li, J Dang, L Huang
2017   Personalized Acoustic Modeling By Weakly Supervised Multi-Task Deep Learning Using Acoustic Tokens  CK Wei, CT Chung, HY Lee, LS Lee
2017   I-vector estimation as auxiliary task for multi-task learning based acoustic modeling for automatic speech recognition  G Pironkov, S Dupont, T Dutoit
2016   Graph-based Semi-Supervised Learning in Acoustic Modeling for Automatic Speech Recognition  Y Liu
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   Improvements in IITG Assamese Spoken Query System: Background Noise Suppression and Alternate Acoustic Modeling  S Shahnawazuddin, D Thotappa, A Dey, S Imani
2016   DNN-Based Acoustic Modeling for Russian Speech Recognition Using Kaldi  I Kipyatkova, A Karpov
2015   Doubly Hierarchical Dirichlet Process Hmm For Acoustic Modeling  AHHN Torbati, J Picone
2015   Deep Learning for Acoustic Modeling in Parametric Speech Generation: A systematic review of existing techniques and future trends  ZH Ling, SY Kang, H Zen, A Senior, M Schuster
2015   Acoustic Modeling In Statistical Parametric Speech Synthesis–From Hmm To Lstm-Rnn  H Zen
2015   Acoustic Modeling of Bangla Words using Deep Belief Network  M Ahmed, PC Shill, K Islam, MAH Akhand
2015   Unified Acoustic Modeling using Deep Conditional Random Fields  Y Hifny
2015   Exploiting Low-Dimensional Structures To Enhance Dnn Based Acoustic Modeling In Speech Recognition  P Dighe, G Luyet, A Asaei, H Bourlard
2015   Ensemble Acoustic Modeling for CD-DNN-HMM Using Random Forests of Phonetic Decision Trees  T Zhao, Y Zhao, X Chen
2015   Deep Neural Networks for Acoustic Modeling  V from Embeds, G Hinton, L Deng, D Yu, G Dahl
2015   Integrating Articulatory Data in Deep Neural Network-based Acoustic Modeling  L Badino, C Canevari, L Fadiga, G Metta
2015   Deep learning in acoustic modeling for Automatic Speech Recognition and Understanding-an overview  I Gavat, D Militaru
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Deep Learning for Authentication

This blog post has recent papers about Deep Learning for authentication, e.g. iris (eye), fingerprint and various other patterns of the user, e.g. behavior writing style (stylometry) and other user patterns. Partially related is the Quora question and answer: How can Deep Learning be used for Computer Security?

Best regards,
Amund Tveit

Year  Title Author
2016   Deep-Learning-Based Security Evaluation on Authentication Systems Using Arbiter PUF and Its Variants  R Yashiro, T Machida, M Iwamoto, K Sakiyama
2016   Touch based active user authentication using Deep Belief Networks and Random Forests  YS Lee, W Hetchily, J Shelton, D Gunn, K Roy
2016   System And Method For Applying Digital Fingerprints In Multi-Factor Authentication  J Oberheide, D Song
2016   Optimized Features Extraction of IRIS Recognition by Using MADLA to Ensure Secure Authentication  S Pravinthraja, K Umamaheswari
2015   Continuous Authentication using Stylometry  ML Brocardo
2015   Smart Kiosk with Gait-Based Continuous Authentication  DT Phan, NNT Dam, MP Nguyen, MT Tran, TT Truong
2015   Keystroke Dynamics User Authentication Using Advanced Machine Learning Methods  Y Deng, Y Zhong
2015   Utilizing deep neural nets for an embedded ECG-based biometric authentication system  A Page, A Kulkarni, T Mohsenin
2014   Improved Perception-Based Spiking Neuron Learning Rule for Real-Time User Authentication  H Qu, X Xie, Y Liu, M Zhang, L Lu
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