deep learning IEEE PAPER 2018






Person re-identification by deep learning multi-scale representations
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ABSTRACT Existing person re-identification (re-id) methods depend mostly on single-scale appearance information. This not only ignores the potentially useful explicit information of other different scales, but also loses the chance of mining the implicit correlated

EE-559Deep learning 3a. Linear classifiers, perceptron
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This crude algorithm works often surprisingly well. With MNISTs0s as negative class, and 1s as positive one. epoch 0 nb_changes 64 train_error 0.23%% test_error 0.19%% epoch 1 nb_changes 24 train_error 0.07%% test_error 0.00%% epoch 2 nb_changes 10

EE-559Deep learning 5. Losses, optimization, initialization.
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Page 1. EE-559 Deeplearning 5. Losses, optimization, initialization. François Fleuret https://fleuret.org/dlc/ January 18, 2018 Draft, do not distributeCOLE POLYTECHNIQUE F D RALE DE LAUSANNE Page 2. Cross-entropy François Fleuret EE-559 Deeplearning

Classification of Diabetic Retinopathy Images by Using Deep Learning Models
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ABSTRACT Diabetes or more precisely Diabetes Mellitus (DM) is a metabolic disorder happens because of high blood sugar level in the body. Over the time, diabetes creates eye deficiency also called as Diabetic Retinopathy (DR) causes major loss of vision. The

Lower jawbone data generation for deep learning tools under MeVisLab
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Segmentation is an important branch in medical image processing and the basis for further detailed investigations on computed tomography (CT), magnetic resonance imaging (MRI), X-ray, ultrasound (US) or nuclear images. Through segmentation, an image is divided into

WDL-RF: predicting bioactivities of ligand molecules acting with G protein-coupled receptors by combining weighted deep learning and random forest
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ABSTRACT Motivation: Precise assessment of ligand bioactivities (including IC50, EC50, Ki, Kd, etc.) is essential for virtual screening and lead compound identification. However, not all ligands have experimentally determined activities. In particular, many G protein-coupled

Relaxed deep learning for real-time economic generation dispatch and control with unified time scale
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ABSTRACT To solve the collaboration problem of multi-time scale economic dispatch and generation control in power system, ie, long term time scale optimization, short term time scale optimization, and real-time control, a real-time economic generation dispatch and

NMRNet: A deep learning approach to automated peak picking of protein NMR spectra
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ABSTRACT Motivation: Automated selection of signals in protein NMR spectra, known as peak picking, has been studied for over 20 years, nevertheless existing peak picking methods are still largely deficient. Accurate and precise automated peak picking would accelerate the

DeepSol: A Deep Learning Framework for Sequence-Based Protein Solubility Prediction
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ABSTRACT Motivation: Protein solubility plays a vital role in pharmaceutical research and production yield. For a given protein, the extent of its solubility can represent the quality of its function, and is ultimately defined by its sequence. Thus, it is imperative to develop novel

DeepScan: Exploiting Deep Learning for Malicious Account Detection in Location-Based Social Networks
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ABSTRACT The widespread location-based social networks (LB-SNs) have immersed into our daily life. As an open platform, LBSNs typically allow all kinds of users to register accounts. Malicious attackers can easily join and post misleading information, often with the intention

Theory of deep learning III: the non-overfitting puzzle
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ABSTRACT : A main puzzle of deep networks revolves around the apparent absence of overfitting intended as robustness of the expected error against overparametrization, despite the large capacity demonstrated by zero training error on randomly labeled data. In this note

Large Model Support for Deep Learning in Caffe and Chainer
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ABSTRACT Deep learning is both compute-and data-intense, and recent breakthroughs have largely been fueled by the fp32 compute capacity of modern GPUs. This has made GPUs the prevalent tool for training deep neural networks, but GPUs have only small

Weblogo-2m: Scalable logo detection by deep learning from the web
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ABSTRACT Existing logo detection methods usually consider a small number of logo classes and limited images per class with a strong assumption of requiring tedious object bounding box annotations, therefore not scalable to real-world applications. In this work, we tackle

A DEEP LEARNING PLATFORM FOR DIABETES BIG DATA ANALYSIS
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1Centre for Bio-Inspired Technologies, Institute of Biomedical Engineering, Imperial College London, UK 2Data Science Institute, Imperial College London, UKDeep learning has been proven to achieve stateof-art performance in modelling complex behaviour and the

AMLDDeep Learning in PyTorch 3. Multi-layer perceptrons, back-propagation, autograd
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This crude algorithm works often surprisingly well. With MNISTs0s as negative class, and 1s as positive one. epoch 0 nb_changes 64 train_error 0.23%% test_error 0.19%% epoch 1 nb_changes 24 train_error 0.07%% test_error 0.00%% epoch 2 nb_changes 10

EE-559Deep learning 2. Learning capacity, basic embeddings
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Page 1. EE-559 Deeplearning 2. Learning capacity, basic embeddings François Fleuret https://fleuret.org/dlc/ January 18, 2018 Draft, do not distributeCOLE POLYTECHNIQUE F D RALE DE LAUSANNE Page 2. Learning from data François Fleuret EE-559 Deep

Supplementary Materials for DeepHit: A Deep Learning Approach to Survival Analysis with Competing Risks
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For the comparison of DeepHit with conventional machine learning algorithms, we modified survival data to perform survival analysis based on the mortality prediction using machine learning algorithms. For every time interval m where m= 0, , Tmax, the cause-specific data

Efficient Deep Learning Inference on Edge Devices
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ABSTRACT Deploying deep learning (DL) models on edge devices is getting popular nowadays. The huge diversity of edge devices, with both computation and memory constraints, however, make efficient deployment challenging. In this paper, we propose a

AMLDDeep Learning in PyTorch 5. Going deeper
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Page 1. AMLD DeepLearning in PyTorch 5. Going deeper François Fleuret http://fleuret.org/ amld/ February 10, 2018COLE POLYTECHNIQUE F D RALE DE LAUSANNE Page 2. Batch processing François Fleuret AMLD DeepLearning in PyTorch / 5. Going deeper 2 / 64 Page

Symtosis: A liver ultrasound tissue characterization and risk stratification in optimized deep learning paradigm
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In the past two decades, liver diseases such as Fatty Liver Disease (FLD) are repeatedly listed as leading causes of liver related death in US among adults aged 45 54 years. The deposition of fat in liver cells is called steatosis. Metabolic syndrome, consumption of

EE-559Deep learning 9. Autoencoders and generative models
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François Fleuret https://fleuret.org/dlc/ January 19, 2018 François Fleuret EE-559 Deep learning / 9. Autoencoders and generative models Many applications such as image synthesis, denoising, super-resolution, speech synthesis, compression, etc. require to go beyond

AMLDDeep Learning in PyTorch 4. Convolution Neural Networks
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Page 1. AMLD DeepLearning in PyTorch 4. Convolution Neural Networks François Fleuret http://fleuret.org/amld/ February 10, 2018COLE POLYTECHNIQUE F D RALE DE LAUSANNE Page 2. Convolutional layers François Fleuret AMLD DeepLearning in PyTorch / 4. Convolution

Discrete Deep Learning for Fast Content-Aware Recommendation
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ABSTRACT Cold-start problem and recommendation efficiency have been regarded as two crucial challenges in the recommender system. In this paper, we propose a hashing based deep learning framework called Discrete Deep Learning (DDL), to map users and items to

EE-559Deep learning 6. Going deeper.
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Network Nb. layers LeNet5 (leCun et al., 1998) 5 AlexNet (Krizhevsky et al., 2012) 8 VGG (Simonyan and Zisserman, 2014) 11 19 GoogleLeNet (Szegedy et al., 2015) 22 Inception v4 (Szegedy et al., 2016) 76 Resnet (He et al., 2015) 34 152 Resnet (He et al., 2016) 1001 Resnet (Huang

Travel Behavior Classification: An Approach with Social Network and Deep Learning 2
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Uncovering human travel behavior is crucial for not only travel demand analysis but also 2 ridesharing opportunities. To group similar travelers, this paper develops a deep learning based 3 approach to classify travelers behaviors given their trip characteristics, including

Machine Translation Using Deep Learning : A Survey
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ABSTRACT Machine Translation using Deep Learning (Neural Machine Translation) is a newly proposed approach to machine translation. The term Machine Translation is used in the sense of translation of one language to another, with no human improvement. It can also

Deep Learning for Joint Source-Channel Coding of Text
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ABSTRACT We consider the problem of joint source and channel coding of structured data such as natural language over a noisy channel. The typical approach inspired by information theory to this problem involves performing source coding to first compress the

DeepHit: A Deep Learning Approach to Survival Analysis with Competing Risks
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ABSTRACT Survival analysis (time-to-event analysis) is widely used in economics and finance, engineering, medicine and many other areas. A fundamental problem is to understand the relationship between the covariates and the (distribution of) survival times (times-to-event) ABSTRACT High peak-to-average power ratio (PAPR) has been one of the major drawbacks of orthogonal frequency division multiplexing (OFDM) systems. In this letter, we propose a novel PAPR reduction scheme, known as PAPR reducing network (PRNet), based on the

An End-to-End Deep Learning Architecture for Graph Classification
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ABSTRACT Neural networks are typically designed to deal with data in tensor forms. In this paper, we propose a novel neural network architecture accepting graphs of arbitrary structure. Given a dataset containing graphs in the form of (G, y) where G is a graph and y is

LEARNING TO SHARE: SIMULTANEOUS PARAMETER TYING AND SPARSIFICATION IN DEEP LEARNING
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ABSTRACT Deep neural networks (DNNs) may contain millions, even billions, of parameters/weights, making storage and computation very expensive and motivating a large body of work aimed at reducing their complexity by using, eg, sparsityinducing

Deep Inductive Network Representation Learning
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ABSTRACT This paper presents a general inductive graph representation learning framework called DeepGL for learning deep node and edge features that generalize across- networks. 1 In particular, DeepGL begins by deriving a set of base features from the graph

Exploration and Tradeoffs of Different Kernels in FPGA Deep Learning Applications
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Convolution Function to compute next layer Dot Pooling or subsampling layer to reduce

A Deep Model with Local Surrogate Loss for General Cost-sensitive Multi-label Learning
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ABSTRACT Multi-label learning is an important machine learning problem with a wide range of applications. The variety of criteria for satisfying different application needs calls for costsensitive algorithms, which can adapt to different criteria easily. Nevertheless, because

Comments onNovel real-time tumor-contouring method using deep learning to prevent mistracking in X-ray fluoroscopyby Terunuma et al.
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We have several comments with regard to the research paper,Novel real-time tumor- contouring method using deep learning to prevent mistracking in X-ray fluoroscopy,by Terunuma and Endo, which was published in the December 2017 issue of Radiological

LSH Softmax: Sub-Linear Learning and Inference of the Softmax Layer in Deep Architectures
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ABSTRACT Log-linear models models are widely used in machine learning and in particular are ubiquitous in deep learning architectures in the form of the softmax. While exact inference and learning of these requires linear time, it can be done approximately in sub

Deep Learning for Matching in Search and Recommendation
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ABSTRACT Matching is the key problem in both search and recommendation, that is to measure the relevance of a document to a query or the interest of a user on an item. Previously, machine learning methods have been exploited to address the problem, which

Deep Learning for Remote Sensing Scene Classification: A Simple and High-Performance Architecture
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Scene classification is an important and challenging problem in Earth observation remote sensing. The goal is to assign labels to satellite or airborne images according to a set of semantic categories (eg land-cover and land-use categories), and the difficulty comes from

Perception-Action- Learning System for Mobile Social-Service Robots using Deep Learning
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ABSTRACT We introduce a robust integrated perception-action- learning system for mobile social-service robots. The state-of-the-art deep learning techniques were incorporated into each module which significantly improves the performance in solving social service tasks

Estimating Aspect Score for Stroke from Brain CT Images Based-on Deep Learning
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ABSTRACT . Stroke is a very dangerous disease and CT image is a good way to quickly diagnose the disease. Aspect Score is a tool to measure the severity of the stroke but it has a problem of the scoring variability among experts. This study proposed an objective and

Deep Learning AAN
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9.11 Convolutional Networks and the History of Deep Learning . 365. 10 Sequence Modeling: Recurrent and Recursive Nets. 367. 10.1 Unfolding

Deep Learning CS229
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Andrew Ng. Data and machine learning . Amount of data. Performance. Most learning algorithms. New AI methods. ( deep learning )

Deep Learning
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this reason, we call this approach to AI deep learning . Many of the early successes of AI took place in relatively sterile and formal environments and did not

Introduction to Deep Learning TI training
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Deep learning creates many layers of neurons, attempting to learn structured representation of big data, layer by layer. Page 8. Multi Layer. Perceptron. (MLP).

deep learning explained Nvidia
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Since an early flush of optimism in the 1950s, smaller subsets of artificial intelligence the first machine learning , then deep learning , a subset of machine learning

Deep Learning for Scientific Discovery National Academies
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Machine learning is a subfield of computer science and artificial intelligence that enables machines to learn without being explicitly programmed

The unreasonable effectiveness of deep learning in PNAS
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2 2020 deep learning | artificial intelligence | neural networks. In 188 Edwin Abbott wrote Flatland: A Romance of Many. Dimensions (1) (Fig. 1).

Deep Learning Now publishers
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Deep Learning : Methods and Applications. Li Deng. Microsoft Research. One Microsoft Way. Redmond, WA 98052; USA deng@microsoft.com. Dong Yu.

The Deep Learning Revolution and Its Implications for arXiv
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The past decade has seen a remarkable series of advances in machine learning , and in particular deep learning approaches based on artificial neural networks,

Deep Learning in Bioinformatics arXiv
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Phone: +82-2-880-1401. Page 2. Keywords. Deep learning , neural network, machine learning , bioinformatics, omics

Deep Learning with PyTorch
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Deep Learning with PyTorch. ELI STEVENS, LUCA ANTIGA,. AND THOMAS VIEHMANN. FOREWORD BY SOUMITH CHINTALA. MANNING. SHELTER

A Brief Introduction to Deep Learning
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A Brief Introduction to Deep Learning . Artificial Neural Network. Back-propagation. Fully Connected Layer. Convolutional Layer. Overfitting

Deep Learning of Representations for Unsupervised and
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Machine learning algorithms attempt to discover structure in data. In their simpler forms, that often means discovering a predictive relationship between variables.

Riding the Wave of Machine Learning Deep Learning Dell
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Artificial intelligence (AI) and machine learning decades-old technologies that are now electrifying the computing industry for all intents and purposes, seem

Deep Learning Tutorial
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2015 DEEP LEARNING TUTORIALS. Deep Learning is a new area of Machine Learning research, which has been introduced with the objective of.

Deep learning in neural networks: An overview Joint Institute
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In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning .

Deep Learning Microsoft
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Deep learning is in the intersections among the research areas of neural networks, artificial intelligence, graphical modeling, optimization, pattern recognition, and Within the field of data analytics, machine learning is a method used to devise complex models and algorithms that lend themselves to prediction; in commercial

Pseudo-Label Deep Learning
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Pseudo-Label : The Simple and Efficient Semi-Supervised Learning. Method for Deep Neural Networks . Dong-Hyun Lee sayit78@gmail.com. Nangman

Deep Learning I Supervised Learning CMU School of
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Representacon Learning : Can we automaccally learn these representacons Audio Features. Page 15. Neural Networks Online Course. Hugos class

Datasheet: Intercept X Deep Learning Sophos
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Deep learning , an advanced form of machine learning , is helping to change the way we approach endpoint security, and Intercept X is leading the charge. By

Small Data Deep Learning: AI Applied to Domain NIST
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2019supervised AI-based models instead of using the traditional machine learning (ML) based models. Artificial. Intelligence. Image. Collections.

Reporting on deep learning algorithms in health The Lancet
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2019 Deep learning using convolutional neural networks has gained substantial traction in medicine; however, inconsistencies exist in the literature on

Deep learning in neural networks Department of Economics
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In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning .

On Optimization Methods for Deep Learning ICML
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The predominant methodology in training deep learning advocates the use of stochastic gradient descent methods (SGDs). Despite its ease of implementation,

The Applications of Deep Learning on Traffic Black Hat
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To solve these problems, we propose a method that is based on neural network and deep learning a hotspot of research in machine learning . The results show

Neural Networks and Deep Learning
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learning. Theyve been developed further, and today deep neural core principles of neural networks and deep learning , rather than a hazy understanding.

making sense out of machine learning deep learning AMD
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Deep Learning (DL) focuses on a subset of machine learning that goes even further to solve problems, inspired by how the human brain recognizes and recalls

Deep Learning Delivers Advanced Analytics for Intel
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In contrast, deep learning an artificial intelligence (AI) discipline that uses deep neural networks is perfect for making sense out of big data. Deep learning

Deep Learning with H2O H2O.ai Documentation
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Unlike the neural networks of the past, modern Deep Learning provides training stability, generalization, and scalability with big data. Since it performs quite well in

Uncertainty in Deep Learning Cambridge Machine Learning
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Uncertainty in Deep Learning . Yarin Gal. Department of Engineering. University of Cambridge. This dissertation is submitted for the degree of. Doctor of

Building Trust in Deep Learning System towards Automated
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We propose to use un- certainty estimation as a measure to know when to trust the deep learning system output. A well-calibrated deep neural network be

Opportunities and obstacles for deep learning in biology and
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The term deep learning has come to refer to a collection of new techniques that, together, have demonstrated break- through gains over existing best-in-class

Learning Deep Neural Networks on the Fly IJCAI
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Deep Neural Networks (DNNs) are typically trained by backpropagation in a batch setting, re- quiring the entire training data to be made available prior to the

Convolutional-Recursive Deep Learning for 3D Object
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We in- troduce a model based on a combination of convolutional and recursive neural networks (CNN and RNN) for learning features and classifying RGB-D

An Introduction to Deep Learning SAS
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Machine Learning is a type of Artificial Intelligence that provides computers with the ability to learn without being explicitly programmed. Machine Learning . by L Khan

Deep learning with COTS HPC systems Computer Science
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Scaling up deep learning algorithms has been shown to lead to increased performance in benchmark tasks and to enable discovery of complex high-level

Why is Deep Learning so effective Caltech
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The unreasonable effectiveness of deep learning . This lecture is based entirely on the paper: Reference: Henry W. Lin and Max Tegmark, Why does deep and

A review of deep learning in the study of materials Nature
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Background on Deep Learning . Recently, advances in Artificial Intelligence (A.I.) seem to be broadcast weekly, even daily. To a large extent the burgeoning A.I

Using deep learning to quantify the beauty of outdoor places
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convolutional neural networks, deep learning , online data. Author for using the Places Convolutional Neural Network, might help us understand what beautiful

Deep Learning
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Deep Learning . Yann LeCun, Yoshua Bengio Geoffrey Hinton. Nature 52 436 444 (28 2015) doi:10.1038/nature14539

Mathematics of Deep Learning The Center for Imaging Science
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Opening the black box of deep neural networks via information. arXiv:1703.008 2017. Achille, Soatto. Information dropout: Learning optimal

Why Does Unsupervised Pre-training Help Deep Learning
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They include learning methods for a wide array of deep architectures (Bengio provides a survey), including neural networks with many hidden layers (

An Empirical Study of Common Challenges in Developing
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Deep learning has been successfully applied to many do- mains and has gained a lot of attention from both industry and academia. In the software engineering

Deep Learning: An Introduction for Applied SIAM
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Multilayered artificial neural networks are becoming a pervasive tool in a host of application fields. At the heart of this deep learning revolution are familiar

Deep Learning in Radiology Academic Radiology
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In this work, the Association of University. Radiologists Radiology Research Alliance Task Force on Deep Learning provides an overview of DL for the radiologist.

A comparison of deep learning performance against health
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2 2019 Deep learning is a form of artificial intelligence (AI) that offers considerable promise for improving the accuracy and speed of diagnosis through

Do Deep Nets Really Need to be Deep Data Science
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learn ; e) current learning algorithms and regularization methods work better with deep architectures than shallow architectures ; f) all or some of the above;

Deep Learning Theory and Practice HPE Education Services
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Deep Learning Theory and. Practice. HU0F0S. This training covers the foundations of artificial intelligence and machine learning , with special focus on deep

Deep Learning osu.edu
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Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction.

Model-driven deep-learning Oxford Academic
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Deep learning has been widely recog- nized as the representative advances of machine learning or artificial intelligence in general nowadays [ 2]. This can be.

deep learning with logged bandit feedback Cornell
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Our approach called BanditNet follows the view of a deep neural network as a stochastic policy. We propose a counterfactual risk minimization (CRM) objective

EARTH SCIENCE DEEP LEARNING NTRS NASA
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This paper provides examples of the applications and also addresses some of the challenges that were encountered. Index Terms Deep learning , neural

Machine Learning For Dummies, IBM Limited Edition
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Machine learning uses a variety of algorithms that iteratively learn from data to improve, describe data, and predict outcomes. As the algorithms ingest training data

Multimodal Deep Learning People MIT
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In this work, we propose a novel application of deep networks to learn features over multiple modalities. We present a series of tasks for multimodal learning and

Deep Learning of Graph Matching CVF Open Access
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The problem of graph matching under node and pair- wise constraints is fundamental in areas as diverse as com- binatorial optimization, machine learning or

Survey on Deep Learning Computer Science Duke University
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Some key enabler deep learning algorithms such as generative adversarial networks, convolutional neural networks, and model transfers have completely

Incorporating Prior Domain Knowledge into Deep People
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Keywords-Noisy Data; Domain Knowledge; Neural Networks;. Deep Learning ; Limited Training Data;. I. INTRODUCTION. Deep learning has witnessed

Deep Learning PhilSci-Archive
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Figure 1. The generic processing flow of a Deep Convolutional Neural Network , whose architecture involves transforming input signals through many sequences

Big Data Deep Learning Faculty Web Pages
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Deep learning refers to a set of machine learning techniques that learn multiple levels of representations in deep archi- tectures. In this section, we will present a

deep learning needs a prefrontal cortex Bridging AI and
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dence between some of the current open questions in deep learning research and the functions of the human pre- frontal cortex (PFC), a brain area known to be

Expert Feature-Engineering vs. Deep Neural Networks: Which
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This feature set is then used within. Page 2. 2 machine learning algorithms to learn the mapping from those features to examples of the construct being modeled,

understanding deep learning requires re- thinking
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Deep artificial neural networks often have far more trainable model parameters than the number of samples they are trained on. Nonetheless, some of these

Deep Learning: An Exposition Scholar Commons University
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Figure 1.1 Illustration of a biological neuron . Deep learning is a type of neural network learning that has gained substantial popularity over the past decade for

A tutorial survey of architectures, algorithms, and applications
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Deep learning refers to a class of machine learning techniques, where many layers of information-processing stages in hierarchical architectures are exploited for

Artificial Intelligence, Machine Learning, Deep Learning, Cisco
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2 2019Subset of AI techniques which use algorithms to enable machines to learn from data. Deep Learning . Subset of ML techniques which uses multi

DeepDecision: A Mobile Deep Learning Cs.ucr.edu
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Deep learning shows great promise to provide more intel- ligent video analytics to augmented reality (AR) devices. For example, real-time object recognition tools

DLU: Deep Learning Unit Fujitsu
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DPU consists of 16 DPEs connected with on-chip network. ▫ DPE incudes large RF and wide SIMD execution units to realize an efficient Deep Learning engine.

Marginal Space Deep Learning Dorin Comaniciu
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Marginal Space Deep Learning : Efficient. Architecture for Detection in Volumetric Image. Data. Florin C. Ghesu Bogdan Georgescu Yefeng Zheng

Deep Learning Annual Reviews
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2019 deep learning , neural networks, artificial intelligence, computational neuroscience, object recognition, face recognition, action recognition,.

An End-to-End Deep Learning Architecture for Graph
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Neural networks are typically designed to deal with data in tensor forms. In this paper, we propose a novel neural network architecture accepting graphs of

A Deep Learning Framework for Predicting Response to
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2019A machine learning (ML) workflow is designed to predict drug response in cancer patients d. Deep neural networks (DNNs) surpass current ML

LEMNA: Explaining Deep Learning based Xinyu Xing
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ABSTRACT. While deep learning has shown a great potential in various domains, the lack Explainable AI, Binary Analysis, Deep Recurrent Neural Networks.

A CPAs Introduction to AI: From Algorithms to Deep Learning
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Where do machine learning and deep learning fit into the world of AI This publication was developed by Chartered Professional Accountants of Canada (CPA.

Deep Learning Data Platform White Paper Pure Storage
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Data, machine learning and AI are a must for financial services businesses. Through generating intelligence, accelerating innovation and fully enabling mobility,.

Large-Scale Deep Learning for Intelligent Computer Systems
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Google Brain project started in 201 with a focus on pushing state-of-the-art in neural networks . Initial emphasis: ○ use large datasets, and. ○ large amounts of

A Survey and Critique of Deep Learning on BDSC Lab
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However, applications of deep learning in recommender systems have not been well explored yet. In this article, we firstly introduce traditional techniques involved

A Primer on Machine Learning
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Using algorithms that continuously learn from data, Machine Learning allows computers to recognize hidden patterns without actually being programmed to do so.

Introduction to Deep Learning MIT 6.S191
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Neural Networks date back decades, so why the resurgence Page 13. The Perceptron. The structural building block of deep learning

Machine Learning Tutorial Part 1 Hot Chips conference
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Architectures for Accelerating Deep Neural Networks . ˃ Part 1: Overview of Deep Learning and Computer Architectures for Accelerating DNNs. Michaela Blott

Inconsistent Performance of Deep Learning Models on
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determine whether the high performance of deep learning on mammograms can be transferred to external data with a different data distribution. Materials and

Deep Learning CEDAR
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Machine Learning . Srihari. Topics. Multilayer Neural Networks. Deep Convolutional Nets. Recurrent Nets. Deep Belief Networks. 2

analytical variables on deep learning accuracy in histopathology
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Aims: Machine learning (ML) binary classification in diagnostic histopathology is an area of intense inves- tigation. Several assumptions, including training.

Security Machine Learning Deep Learning
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Password Cracking. Sometimes, people gain access to encrypted password file. Can spend days/weeks trying out passwords to see if they match. Modern

Introduction to Machine Learning, Neural Networks TVST
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2 2020ML contains many models and methods, including deep learning (DL) and artifi- cial neural networks (ANN). Although symbolic AI is proficient

Deep Learning to Solve Challenging Problems
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Deep Learning . Modern Reincarnation of Artificial Neural Networks. Collection of simple trainable mathematical units, organized in layers, that work together to

List of Deep Learning Models Preprints.org
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1 2019Keywords: Deep learning , machine learning , convolutional neural networks. (CNN) recurrent neural networks (RNN), denoising autoencoder

guidebook deep learning on aws Awsstatic
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Machine learning encompasses the technology where a computer analyzes data to learn from experience without human involvement. Deep learning is a subset

Information theory and Deep learning University of Washington
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Deep learning for statistical inference. 2a. Theory for GAN. 2b. Learning Gated. Neural Networks. Part-1: Deep learning for information theory. Part-2: Information

A deep learning algorithm using CT images to medRxiv
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1 2020A deep learning algorithm using CT images to screen for Corona Virus. Disease (COVID-19). Shuai Wang. . *, Bo Kang. . *, Jinlu Ma. .

Computational Linguistics and Deep Learning MIT Press
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Christopher D. Manning∗. Stanford University. 1. The Deep Learning Tsunami. Deep Learning waves have lapped at the shores of computational linguistics for

Applying Deep Learning to the Cache Replacement Problem
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machine learning algorithms, such as a neural network, a Support Vector Machine, or a perceptron. Page 2. MICRO-5 October 12 1 201 Columbus, OH, USA.

Deep Learning for Intelligent Wireless Networks: A Fei Hu
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Abstract As a promising machine learning tool to handle the accurate pattern recognition from complex raw data, deep learning (DL) is becoming a powerful

Review of Vibration-Based Structural Health MDPI
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2020 machine learning algorithms for fault monitoring. The vibration factors were used to categorize the studies. A brief interpretation of deep neural

Learning Deep Architectures for AI Universit de Montr al
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We also discuss convo- lutional neural networks , the oldest successful example of deep architecture, specialized for vision and signal processing tasks (LeCun,

Towards Ultra-High Performance and Energy ALCHEM
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Hardware accelerations of deep learning systems have been extensively performance for hardware implementations of deep neural networks (DNNs).

Scalable Provably-Secure Deep Learning Cryptology ePrint
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Deep Neural Networks (DNNs) and Convolutional Neural Net- works (CNNs) are the two main categories of neuron networks widely used in deep learning domain

Three Classes of Deep Learning Architectures and Their
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Deep learning refers to a class of machine learning techniques, where many layers of information processing stages in hierarchical architectures are exploited for


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