ENGINEERING RESEARCH PAPERS

deep learning 2017 IEEE PAPER


Deep learning is the application of artificial neural networks to learning tasks that contain more than one hidden layer.

A Deep Learning Approach to Understanding Cloud Service Level Agreements
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Educational organizations, like Universities and School Systems, are rapidly adopting Cloud based services to provide Information Technology (IT) infrastructure to their students. These include course offerings, class materials, data storage, emailing and collaboration

Using Deep Learning to Automate Feature Modeling in Learning by Observation: A Preliminary Study
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Abstract A primary advantage of learning by observation is that it allows non-technical experts to transfer their skills to an agent. However, this requires a general-purpose learning agent that is not biased to any specific expert, domain, or behavior. Existing domain- Abstract. Translation elongation plays a crucial role in multiple aspects of protein biogenesis, eg, differential expression, cotranslational folding and secretion. However, our current understanding on the regulatory mechanisms underlying translation elongation

CEREBRO: A System to Manage Deep Learning for Relational Data Analytics.
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Deep learning aka deep neural networks (DNNs) are pushing the state of the art in AI tasks such as image and speech recognition [3]. Leading Web companies are betting big on DNNs. Beyond all the hype, a key question remains: Will deep learning transform relational

Deep Structured Learning for Facial Expression Intensity Estimation
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Abstract We consider the task of automated estimation of facial expression intensity. This involves estimation of multiple output variables (facial action units AUs) that are structurally dependent. Their structure arises from statistically induced co-occurrence patterns of AU

Deep Learning for abnormality detection in Chest X-Ray images
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Abstract Heart and lung failure account for more than 500,000 deaths annually in the United States and are most commonly screened for using plain film chest x-rays (CXR). The time constraints imposed on radiologists by their massive workload severely impede

The Design and Evolution of Deep Learning Workloads
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Deep Learning for Unsupervised Insider Threat Detection in Structured Cybersecurity Data Streams
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Abstract Analysis of an organization's computer network activity is a key component of early detection and mitigation of insider threat, a growing concern for many organizations. Raw system logs are a prototypical example of streaming data that can quickly scale beyond the Video data analysis has become an increasingly important research area with widespread applications in automatic surveillance of transportation infrastructure including roads, rail and airports. As the amount of video data collected grows, so does the opportunity for further

A Systematic Literature Review on Features of Deep Learning in Big Data Analytics.
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Abstract Context: Deep Learning (DL) is a division of machine learning techniques that based on algorithms for learning multiples level of representations. Big Data Analytics (BDA) is the process of examining large scale of data and variety of data types. Objectives: The

Deep learning on C Manifolds
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J Bian 2017 jake.run Abstract The intention of this note is to explain what it means to train a machine learning model to a mathematical audience. As a by-product we give a description of training/backprop/gradient descent in the category of smooth manifolds. I also explain

A GPU deep learning metaheuristic based model for time series forecasting
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Abstract As the new generation of smart sensors is evolving towards high sampling acquisitions systems, the amount of information to be handled by learning algorithms has been increasing. The Graphics Processing Unit (GPU) architecture provides a greener

Theory of Deep Learning II: Landscape of the Empirical Risk in Deep Learning
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Abstract: Previous theoretical work on deep learning and neural network optimization tend to focus on avoiding saddle points and local minima. However, the practical observation is that, at least in the case of the most successful Deep Convolutional Neural Networks (DCNNs),

Plant Leaf Disease Detection using Deep Learning and Convolutional Neural Network
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Abstract: When plants and crops are affected by pests it affects the agricultural production of the country. Usually farmers or experts observe the plants with naked eye for detection and identification of disease. But this method can be time processing, expens ive and inaccurate.

Smart Library: Identifying Books on Library Shelves using Supervised Deep Learning for Scene Text Reading
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ABSTRACT Physical library collections are valuable and long standing resources for knowledge and learning. However, managing and finding books or other volumes on a large collection of bookshelves often leads to tedious manual work, especially for large collections Abstract. Recently, the researches of image recognition have been developed remarkably by means of the deep learning. In this study, we focused on the anime storyboards and applied deep convolutional neural networks (DCNNs) to those data. There exists one

A new semantic attribute deep learning with a linguistic attribute hierarchy for spam detection
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The massive increase of spam is posing a very serious threat to email and SMS, which have become an important means of communication. Not only do spams annoy users, but they also become a security threat. Machine learning techniques have been widely used for

Plant identification based on noisy web data: the amazing performance of deep learning (LifeCLEF 2017)
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Abstract. The 2017-th edition of the LifeCLEF plant identification challenge is an important milestone towards automated plant identification systems working at the scale of continental floras with 10.000 plant species living mainly in Europe and North America illustrated by a

DEEP LEARNING FOR HUMAN ACTIVITY RECOGNITION
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AC Accuracy ADL Activities of daily living AF Average F-measure CNN Convolutional neural network CPU Central processing unit DBN Deep belief network DT Decision tree HA Hand Gesture HAR Human activity recognition KNN K-nearest neighbors LSTM Long-and short-

Deep Learning: Shaking the Founda ons
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Our interest in deep learning began during 2012. You could say it was like noticing but not quite 'reading the tea leaves'. Certainly, we shared a growing unease about the nature and performance of schools and school systems. Even those countries that were doing well on

Fall Risk Reduction for the Elderly Using Mobile Robots Based on the Deep Reinforcement Learning
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Abstract Slip-induced fall is one of the main factors causing serious fracture among the elderly. This paper proposes a deep learning based fall risk reduction measures by mobile assistant robots for the elderly. We use a deep convolutional neural network to analyze fall Membrane proteins (MPs)targeted by approximately half of current therapeutic drugs. In many genomes 20 40% of genes encode MPs. In particular, Human genome has> 5,000 reviewed MPs and more than 3000 of them

Object Classification in Images of Neoclassical Artifacts Using Deep Learning
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The transformation of aesthetic styles has been at the heart of Art History since its inception as a scholarly discipline in the late eighteenth century. Analyzing the single artifact and the carefully curated corpus have been the techniques for crafting hermeneutic understanding

Playing Othello by Deep Learning Neural Network
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Abstract Board game implementation has long been a way of showing the ability of AI. Among different board games, Othello is one of the popular subjects due to its simple rules and well-defined strategic concepts. Today, the majority of the Othello programmes at a

Deep Learning and Quantum Entanglement: Fundamental Connections with Implications to Network Design.
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Abstract Deep convolutional networks have witnessed unprecedented success in various machine learning applications. Formal understanding on what makes these networks so successful is gradually unfolding, but for the most part there are still significant mysteries to

Simulation based optimal control via deep learning
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Generally, the aim of solving an optimal decision problem, that is an optimal stopping or optimal control problem, is twofold. On the one hand, one aims at bounding its true value from below and above, and on the other hand one tries to find a good decision policy

Imbalance Aware Lithography Hotspot Detection: A Deep Learning Approach
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Page 1. Imbalance Aware Lithography Hotspot Detection: A DeepLearning Approach Haoyu Yang1, Luyang Luo1, Jing Su2, Chenxi Lin2, Bei Yu1 1The Chinese University of Hong Kong 2ASML Brion Inc. Mar. 1, 2017 1 / 34 8 / 34 Page 12. Why DeepLearning Feature Crafting

Deep learning with geodesic moments for 3D shape classification
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The availability of large 3D shape benchmarks has sparked a flurry of research activity in the development of efficient approaches for nonrigid shape analysis, including clustering, classification and retrieval [1 5]. Shape classification is a well-researched and fundamental

A deep learning-based approach to material removal rate prediction in polishing
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Chemical mechanical polishing (CMP) based on a balanced interaction of chemical reaction and mechanical abrasion enables the designed functionality of a workpiece (eg a silicon wafer) by minimizing surface defects. Typically a CMP tool consists of a rotating polishing

No! Not Another Deep Learning Framework
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ABSTRACT In recent years, deep learning has pervaded many areas of computing due to the confluence of an explosive growth of large-scale computing capabilities, availability of datasets, and advances in learning techniques. While this rapid growth has resulted in

Layout Hotspot Detection with Feature Tensor Generation and Deep Biased Learning
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ABSTRACT Detecting layout hotspots is one of the key problems in physical verification flow. Although machine learning solutions show benefits over lithography simulation and pattern matching based methods, it is still hard to select a proper model for large scale problems Abstract:Bin Packing problems have been widely studied because of their broad applications in different domains. Known as a set of NP-hard problems, they have different variations and many heuristics have been proposed for obtaining approximate solutions.

Audio Event Classification using Deep Learning in an End-to-End Approach
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Abstract: The goal of the master thesis is to study the task of Sound Event Classification using Deep Neural Networks in an endto-end approach. Sound Event Classification it is a multi-label classification problem of sound sources originated from everyday environments.

Using Deep Learning to Automate Feature Modeling in Learning by Observation
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Abstract Learning by observation allows non-technical experts to transfer their skills to an agent by shifting the knowledge-transfer task to the agent. However, for the agent to learn regardless of expert, domain, or observed behavior, it must learn in a general-purpose ABSTRACT We introduce the Concurrent Activity Recognizer (CAR) an ecient deep learning structure that recognizes complex concurrent teamwork activities from multimodal data. We implemented the system in a challenging medical setting, where it recognizes 35 di Abstract. The growth in the amount of multimedia content available online supposes a challenge for search and recommender systems. This information in the form of visual elements is of great value to a variety of web mining tasks; however, the mining of these

VISUAL TRACKING UTILIZING OBJECT CONCEPT FROM DEEP LEARNING NETWORK.
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ABSTRACT: Despite having achieved good performance, visual tracking is still an open area of research,target undergoes serious appearance changes which are not included in the model. So, in this paper, we replace the appearance model by a concept

L2-Net: Deep learning of discriminative patch descriptor in euclidean space
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Abstract The research focus of designing local patch descriptors has gradually shifted from handcrafted ones (eg, SIFT) to learned ones. In this paper, we propose to learn high performance descriptor in Euclidean space via the Convolutional Neural Network (CNN).

Deep learning and neutrino physics
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The Particle Physics Project Prioritization Panel (P5) report [1] highlights the physics of neutrino mass as one it it's five Science Drivers for organizing the activities of high energy physics (HEP). Because neutrino masses are so anomalously small, they may provide a

Characterization of errors in deep learning-based brain MRI segmentation
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Abstract With ever-increasing data in the field of medical imaging, the availability of robust methods for quantitative analysis in large-scale studies is the need of the hour. In recent times, there has been a significant increase in the use of deep learning, in particular of

Description of Images Related To Haze Crisis in Indonesia Using Deep Learning
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ABSTRACT Image description for haze images using deep learning method is proposed in this article. The description of haze images which is obtained from the social media is a crucial information for the government, in order to give an early warning for the people about

Application of deep learning neural network for classification of TB lung CT images based on patches
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Abstract. In this work, convolutional neural network (CNN) is applied to classify the five types of Tuberculosis (TB) lung CT images. In doing so, each image has been segmented into rectangular patches with side width and high varying between 20 and 55 pixels, which are ABSTRACT Many phenomena taking place in the solar photosphere are controlled by plasma motions. Although the line-of-sight component of the velocity can be estimated using the Doppler effect, we do not have direct spectroscopic access to the components that are

Forecasting Real Time Series Data using Deep Belief Net and Reinforcement Learning
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Abstract Hinton's deep auto-encoder (DAE) with multiple restricted Boltzmann machines (RBMs) is trained by the unsupervised learning of RBMs and fine-tuned by the supervised learning with error-backpropagation (BP). Kuremoto et al. proposed a deep belief network

Scaling Up Deep Learning on Clusters
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Executive Summary This capstone project focuses on developing a cost-effective, energy- effective and computationally-powerful distributed machine learning library (BIDMach), to catch and lead the 1 current trend of big data. We are collaborating with our industry partner

On deep learning as a remedy for the curse of dimensionality in nonparametric regression
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Abstract Assuming that a smoothness condition and a suitable restriction on the structure of the regression function hold, it is shown that least squares estimates based on multilayer feedforward neural networks are able to circumvent the curse of dimensionality in

Statistical data cleaning for deep learning of automation tasks from demonstrations
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Abstract:Automation using deep learning from demonstrations requires many training examples. Gathering this data is time consuming and expensive, and human demonstrators are prone to inconsistencies and errors that can delay or degrade learning. This paper

Unsupervised multi-manifold clustering by learning deep representation
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Abstract In this paper, we propose a novel deep manifold clustering (DMC) method for learning effective deep representations and partitioning a dataset into clusters where each cluster contains data points from a single nonlinear manifold. Different from other previous

An Extensive Survey on Deep Learning Applications
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ABSTRACT Deep learning (DL) is a branch of machine learning based on a set of algorithms that attempt to model high level abstractions in data. It is a new area of Machine Learning research, which has been presented with the goal of drawing Machine Learning

Computational single-cell classification using deep learning on bright-field and phase images
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Abstract Automated cell classification is an important machine vision problem with significant benefits to biomedicine. We propose an efficient high-accuracy framework to classify cells based on bright-field and phase images using deep learning. With carefully designed

Are Deep Learning Methods Better for Twitter Sentiment Analysis
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Many applications based on sentiment analysis on social media, such as Twitter, have been developed by researchers. Recently, during the Unites States presidential election of 2016, politicians, including president-elect Donald J. Trump, have been using Twitter as a mean of Abstract. We build a privacy-preserving deep learning system in which many learning participants perform neural network-based deep learning over a combined dataset of all, without actually revealing the participants' local data to a curious server. To that end, we

Linear Models and Deep Learning: Learning In Sequential Domains
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Abstract With the diffusion of cheap sensors, sensor-equipped devices (eg, drones), and sensor networks (such as Internet of Things), as well as the development of inexpensive human-machine interaction interfaces, the ability to quickly and effectively process

A Quick Review of Deep Learning in Facial Expression
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Abstract: Over the last few years, deep artificial neural networks have gotten the most attention in computer science, especially in pattern recognition, machine vision and machine learning. One of its excellent applications is in the emotion recognition via facial expression

Deep learning in assessment of drill condition on the basis of images of drilled holes
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ABSTRACT This paper presents novel approach to drill condition assessment using deep learning. The assessment regarding level of the drill wear is done on the basis of the drilled hole images. Two states of the drill are taken into account: the sharp enough to continue

Deep learning based action recog-nition with application to dogs
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Abstract The last decade has seen a booming of Human Action Recognition technologies and algorithms. Accurate recognition of human action would impact plenty of areas including medical, security and entertainment. However, the need for pets health increased in the

Deep Active Learning for Short-Text Classification
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Abstract In this paper, we propose a novel active learning algorithm for short-text (Chinese) classification applied to a deep learning architecture. This topic thus belongs to a cross research area between acitve learning and deep learning. One of the bottlenecks of deep

A comprehensive deep learning approach to end-to-end language identification
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Abstract: A new machine learning paradigm, called deep learning, has accelerated the development of state-of-the-art systems in various research domains. Deep learning leverages a sophisticated network of non-linear units and their connections to learn multiple

Developing the course Nature and Landscape: Politics with a focus on deep learning
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Department of Food and Resource Economics A core characteristic of graduates from natural resources management is that in their professional career they come to work interdisciplinary in their own problem solving and through communicating and collaborating

Query by Singing/Humming System Based on Deep Learning
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Abstract With the proliferation of digital music, efficient indexing and retrieval tools are required for searching the desired music in a large digital music database (DB). Traditional text-based information retrieval methods (titles, lyrics, singers, etc.) cannot meet people's

A Deep Learning-based Approach for Banana Leaf Diseases Classification.
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Abstract: Plant diseases are important factors as they result in serious reduction in quality and quantity of agriculture products. Therefore, early detection and diagnosis of these diseases are important. To this end, we propose a deep learning-based approach that

Deep Learning
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Abstract:Deep learning refers to a family of approaches that have taken machine learning to a new level, helping computers make sense out of vast amounts of data in the form of text, images, and sound. Deep learning algorithms are used to train deep networks with large

Deep Learning for Predictions in Emerging Currency Markets.
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1Department of Accounting and Audit, Ternopil National Economic University, Ternopil, Ukraine 2Laboratoire d'Informatique de Grenoble, Universit Grenoble Alpes, Grenoble, France 3College of Engineering and Computing, Nova Southeastern University, Fort

Recognition of species of Triglidae Family using Deep Learning
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J. Black Sea/Mediterranean Environment Vol. 23, No. 1: 56-65 (2017) Recognition of species of Triglidae Family using Deep Learning Yakup Kutlu1, Gokhan Altan2,*, Bilal Is imen3, Servet A. Dogdu4, Cemal Turan4 1 Department of Computer Engineering, Faculty of Electric and

Deep Learning Approach for Secondary Structure Protein Prediction based on First Level Features Extraction using a Latent CNN Structure
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Prediction (PSSP) has been considered as one of the main challenging tasks in this field. Today, secondary structure protein prediction approaches have been categorized into three groups (Neighbor-based, model-based, and meta predicator-based model). The main

Deep Learning for Compilers
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Abstract Continued advancements in machine learning have increasingly extended the state- of-the art in language modelling for natural language processing. Coupled with the increasing popularity of websites such as GitHub for hosting software projects, this raises the

Deep Learning for Intelligent Transportation
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Abstract The goal of this project is to advance WPI's intelligent transportation program through the creation of a data collection system, a Convolutional Neural Network (CNN) model for intelligent transportation, and a simulator to test the trained CNN model. The data

Learning Word Vectors in Deep Walk Using Convolution
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Abstract Textual queries in networks such as Twitter can have more than one label, resulting in a multi-label classification problem. To reduce computational costs, a low-dimensional representation of a large network is learned that preserves proximity among nodes in the

Deep reinforcement learning for dynamic multichannel access
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Abstract:We consider the problem of dynamic multichannel access in a Wireless Sensor Network (WSN) containing N correlated channels, where the states of these channels follow a joint Markov model. A user at each time slot selects a channel to transmit a packet and

Automated Feature Selection and Churn Prediction using Deep Learning Models
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Abstract In this competitive world, mobile telecommunications market tends to reach a saturation state and faces a fierce competition. This situation forces the telecom companies to focus their attention on keeping the customers intact instead of building a large customer

Deep Learning for Natural Language Processing
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Given data that has hard to describe complex intrinsic structure Important: DL usually works well only when data has unknown hidden structure. Not a silver bullet for all tasks Learn hierarchical representations of data points to perform a task Input representation Hidden

Learning How to Drive in a Real World Simulation with Deep Q-Networks
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Abstract:We present a reinforcement learning approach using Deep Q-Networks to steer a vehicle in a 3D physics simulation. Relying solely on camera image input the approach directly learns steering the vehicle in an end-to-end manner. The system is able to learn

Development and Applications of Deep Learning Structures for Point Cloud Data
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Abstract This thesis makes three main contributions on a generalized method by using a surface common feature based Deep Learning struture, that amis to acheve object recognition task and emotional facial expression recognition task. Because the popularity of

Deep Metric Learning via Facility Location
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Abstract Learning image similarity metrics in an end-to-end fashion with deep networks has demonstrated excellent results on tasks such as clustering and retrieval. However, current methods, all focus on a very local view of the data. In this paper, we propose a new metric

International Workshop on Deep Learning and Music
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In this paper, we explore the semantic similarity that can be derived by looking solely at the context in which a musical slice appears. In past research, music has often been modeled through Recursive Neural Networks (RNNs) combined with Restricted Bolzmann Machines

Deep Learning Binary Neural Network on an FPGA
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Abstract In recent years, deep neural networks have attracted lots of attentions in the field of computer vision and artificial intelligence. Convolutional neural network exploits spatial correlations in an input image by performing convolution operations in local receptive fields.

Dynamic A ention Deep Model for Article Recommendation by Learning Human Editors' Demonstration
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ABSTRACT As aggregators, online news portals face great challenges in continuously selecting a pool of candidate articles to be shown to their users. Typically, those candidate articles are recommended manually by platform editors from a much larger pool of articles

Wildcat: Weakly supervised learning of deep convnets for image classification, pointwise localization and segmentation
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Abstract This paper introduces WILDCAT, a deep learning method which jointly aims at aligning image regions for gaining spatial invariance and learning strongly localized features. Our model is trained using only global image labels and is devoted to three main

Deep co-occurrence feature learning for visual object recognition
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Abstract This paper addresses three issues in integrating partbased representations into convolutional neural networks (CNNs) for object recognition. First, most part-based models rely on a few pre-specified object parts. However, the optimal object parts for recognition

Deep Learning with Deep Water
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WPMSM Dymczyk, ACQ Kou 2017 docs.h2o.ai This booklet introduces the reader to H2O Deep Water, a framework for GPU-accelerated deep learning on H2O. H2O Deep Water leverages prominent open source deep learning frameworks, such MXNet, TensorFlow, and Caffe, as backends. Throughout the booklet,

Deep 360 Pilot: Learning a Deep Agent for Piloting through 360 Sports Video.
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Abstract Watching a 360 sports video requires a viewer to continuously select a viewing angle, either through a sequence of mouse clicks or head movements. To relieve the viewer from this 360 piloting task, we propose deep 360 pilot a deep learning-based agent for

Deep Learning Website Fingerprinting
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Abstract Anonymity networks like Tor enable Internet users to browse the web anonymously. This helps citizens circumvent censorship from repressive governments, journalists communicate with anonymous sources or regular users to avoid tracking online. However,

Towards a Legal Definition of Machine Intelligence: The Argument for Artificial Personhood in the Age of Deep Learning
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ABSTRACT The paper dissects the intricacies of Automated Decision Making (ADM) and urges for refining the current legal definition of AI when pinpointing the role of algorithms in the advent of ubiquitous computing, data analytics and deep learning. ADM relies upon a

Traffic flow forecasting with deep learning
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P Kanestr m 2017 brage.bibsys.no In recent years there has been a vast increase in available data with the ad-vancement of smart cities. In the domain of Intelligent Transportation Systems (ITS) this modernisation can positively effect transportation networks, thus cut-ting down travel time, increase efficacy,

Learning utterance-level representations for speech emotion and age/gender recognition using deep neural networks
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ABSTRACT Accurately recognizing speaker emotion and age/gender from speech can provide better user experience for many spoken dialogue systems. In this study, we propose to use deep neural networks (DNNs) to encode each utterance into a fixed-length vector by

Cooperative Multi-Agent Control Using Deep Reinforcement Learning
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ABSTRACT This work considers the problem of learning cooperative policies in complex, partially observable domains without explicit communication. We extend three classes of single-agent deep reinforcement learning algorithms based on policy gradient, temporal-

Supervised and Reinforcement Learning for Fighting Game AIs using Deep Convolutional Neural Network []
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Abstract AI has become important for human life since its application can help human in problemsolving. Imaging a world, when workers in dangerous environment are replaced by Robot, oldsters are taken care by automated and comfortable services, self-driving cars

Cooperative Learning of Deep Generative Models with Application in Sound Synthesis
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In recent years, deep learning (DL)[GBC16] methods have achieved remarkable success in supervised learning or predicative learning on varieties of computer vision and natural language processing tasks. The current most prevailing architecture of neural networks-

Lesion Detection in CT Images Using Deep Learning Semantic Segmentation Technique
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ABSTRACT: In this paper, the problem of automatic detection of tuberculosis lesion on 3D lung CT images is considered as a benchmark for testing out algorithms based on a modern concept of Deep Learning. For training and testing of the algorithms a domestic dataset of

ELiRF-UPV at SemEval-2017 Task 4: Sentiment Analysis using Deep Learning
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Abstract This paper describes the participation of ELiRF-UPV team at task 4 of SemEval2017. Our approach is based on the use of convolutional and recurrent neural networks and the combination of general and specific word embeddings with polarity

Multi-Focus Attention Network for Efficient Deep Reinforcement Learning
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Abstract Deep reinforcement learning (DRL) has shown incredible performance in learning various tasks to the human level. However, unlike human perception, current DRL models connect the entire low-level sensory input to the state-action values rather than exploiting

Design strategy for optimal iterative learning control applied on a deep drawing process
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Abstract Metal forming processes can general be characterised as repetitive processes, this work will take advantage of this characteristic by developing an algorithm or control system which transfers process information from part to part, reducing the impact of repetitive

Impact of Deep Learning in Big Data Analytics
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ABSTRACT New technologies enable us to collect more data than ever before. With an overwhelming amount of web-based, mobile, and sensor-generated data arriving at a terabyte and even zeta byte scale, new science and insights can be discovered from the

END-TO-END SPEECH RECOGNITION APPLIED TO BRAZILIAN PORTUGUESE USING DEEP LEARNING
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Since 2006, the world has drastically changed, but unfortunately, only a few people have noticed. In that year, a game-changing algorithm was (re) born: deep learning [3]. After that, the artificial intelligence (AI) field has conquered research and industry, from pedestrian

A deep learning framework for the automated inspection of complex dual-energy x-ray cargo imagery
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ABSTRACT Previously, we investigated the use of Convolutional Neural Networks (CNNs) to detect so-called Small Metallic Threats (SMTs) hidden amongst legitimate goods inside a cargo container. We trained a CNN from scratch on data produced by a Threat Image

Feature extraction using MPEG-CDVS and Deep Learning with application to robotic navigation and image classification
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The main contributions of this thesis are the evaluation of MPEG Compact Descriptor for Visual Search in the context of indoor robotic navigation and the introduction of a new method for training Convolutional Neural Networks with applications to object classification.

big data and deep learning IEEE PAPER


Big Data Analytics and Deep Learning are two high-focus of data science.

Big data and deep learning for understanding DoD data
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Abstract. Today, Big Data infrastructure and analytics intervene with traditional data sciences. We are compelled to ask-What is new In this article, the authors provide a pragmatic context for how Big Data infrastructure and analytics are related to traditional data

Convolutional data: Towards deep audio learning from big data
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Department of Computer Science, City University London, United Kingdom hazrat. ali. 1@ city. ac. uk, a. garcez@ city. ac. uk Deep Learning has become a popular approach for unsupervised feature learning [3]. It is now used extensively for object, face and speech

Deep Learning: Effective Tool for Big Data Analytics
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Abstract:Currently, Deep Learning is a very active research area in pattern recognition and machine learning society. It has achieved unprecedented success in applications of essential fields such as Computer Vision, Speech and Audio Processing, and Natural

Deep Learning in Big Image Data: Histology IMage Classification for Breast Cancer Diagnosis
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This paper present results of the use of Deep Learning approach and Convolutional Neural Networks (CNN) for the problem of breast cancer diagnosis. Specifically, the main goal of this particular study was to detect and to segment (ie delineate) regions of micro- and macro-metastases in

Efficient Deep Learning for Big Data: A Review
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Abstract:The data science is composed of Big Data Analytics (BDA) and Deep Learning (DL). Apart from this Big Data (BD) has got popularity due to its importance in the present genre for both the public and private organizations, as this applies to collection of huge data.

Impact of Deep Learning in Big Data Analytics
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ABSTRACT New technologies enable us to collect more data than ever before. With an overwhelming amount of web-based, mobile, and sensor-generated data arriving at a terabyte and even zeta byte scale, new science and insights can be discovered from the

AN ARCHITECTURE OF DEEP LEARNING METHOD TO PREDICT TRAFFIC FLOW IN BIG DATA
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Abstract The invent of IEEE 802.11 p as a communication standard, specific network protocol called vehicular adhoc network (VANET) based on mobile adhoc network (MANET) along with sensor technology has put a strong foundation to visualize as well as make a

A Systematic Literature Review on Features of Deep Learning in Big Data Analytics.
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Abstract Context: Deep Learning (DL) is a division of machine learning techniques that based on algorithms for learning multiples level of representations. Big Data Analytics (BDA) is the process of examining large scale of data and variety of data types. Objectives: The Abstract:Bin Packing problems have been widely studied because of their broad applications in different domains. Known as a set of NP-hard problems, they have different variations and many heuristics have been proposed for obtaining approximate solutions.

Building A Deep Learning Classifier for Enhancing a Biomedical Big Data Service
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Abstract:Providing an easily accessible data service with high quality data is important for building big data applications. In this paper, we introduce a big data service for managing and accessing massive-scale biomedical image data. The service includes three major

Deep Learning Neural Networks: Challenges and Perspective for Big-Data Processing
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Rome, 13 July 2016 M. Scarpiniti DeepLearning Neural Networks: Challenges and Perspective for Big-Data Processing Rome, 13 July 2016 Page 2. Aims of this presentation The aims of this second part of the talk is to provide: 2 possible solutions to open questions on which our research

Spatiotemporal Modeling and Prediction in Cellular Networks: A Big Data Enabled Deep Learning Approach
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Abstract:In this paper, we propose to leverage the emerging deep learning techniques for spatiotemporal modeling and prediction in cellular networks, based on big system data. First, we perform a preliminary analysis for a big dataset from China Mobile, and use traffic

Study of Challenges in Big Data Analytics and Deep learning Applications
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Abstract Big Data has become important as many organizations both public and private have been collecting massive amounts of domain-specific information, which can contain useful information about problems such as national intelligence, cyber security, fraud

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