DEEP LEARNING-TECHNOLOGY-RESEARCH PAPER-SOFTWARE


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is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, semi-supervised or unsupervised. Machine learning is one of the fastest-growing and most exciting fields out there, and deep learning represents its true bleeding edge. In this course, you'll develop a clear understanding of the motivation for deep learning, and design intelligent systems that learn from complex and/or large-scale datasets

Multimodaldeep learning
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Abstract Deep networks have been successfully applied to unsupervised feature learning for single modalities (eg, text, images or audio). In this work, we propose a novel application of deep networks to learn features over multiple modalities. We present a series of tasks for

Domain adaptation for large-scale sentiment classification: Adeep learningapproach
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Abstract The exponential increase in the availability of online reviews and recommendations makes sentiment classification an interesting topic in academic and industrial research. Reviews can span so many different domains that it is difficult to gather annotated training

Deep learningface representation from predicting 10,000 classes
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Abstract This paper proposes to learn a set of high-level feature representations through deep learning referred to as Deep hidden IDentity features (DeepID), for face verification. We argue that DeepID can be effectively learned through challenging multi-class face

Deep learningvia Hessian-free optimization.
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Suppose we have 2 nearly identical units (ie nearly identical weights and biases). Let i and j be the two red weights. Let d direction with dk=0 But so is the curvature:

Deep learningface attributes in the wild
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Abstract Predicting face attributes in the wild is challenging due to complex face variations. We propose a noveldeep learningframework for attribute prediction in the wild. It cascades two CNNs, LNet and ANet, which are finetuned jointly with attribute tags, but pre-trained

Deep learningwith COTS HPC systems
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Abstract Scaling updeep learningalgorithms has been shown to lead to increased performance in benchmark tasks and to enable discovery of complex high-level features. Recent efforts to train extremely large networks (with over 1 billion parameters) have relied

An improveddeep learningarchitecture for person re-identification
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Abstract In this work, we propose a method for simultaneously learning features and a corresponding similarity metric for person re-identification. We present a deep convolutional architecture with layers specially designed to address the problem of re-identification. Given

Convolutional-recursivedeep learningfor 3d object classification
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Abstract Recent advances in 3D sensing technologies make it possible to easily record color and depth images which together can improve object recognition. Most current methods rely on very well-designed features for this new 3D modality. We introduce a model based on a

Jointdeep learningfor pedestrian detection
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Abstract Feature extraction, deformation handling, occlusion handling, and classification are four important components in pedestrian detection. Existing methods learn or design these components either individually or sequentially. The interaction among these components is

Learning fine-grained image similarity with deep ranking
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differences. This paper proposes a deep ranking model that employsdeep learning techniques to learn similarity metric directly from images features.Deep learningmodels have achieved great success on image classification tasks [15]

Chainer: a next-generation open source framework fordeep learning
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Abstract Software frameworks for neural networks play key roles in the development and application ofdeep learningmethods. However, as new types ofdeep learningmodels are developed, existing frameworks designed for convolutional neural networks are becoming

Discriminative deep metric learning for face verification in the wild
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Deep Learning : In recent years,deep learninghas received increasing interests in computer vision and machine learning, and a number ofdeep learningmethods have been proposed in the literature

Deep learningwith limited numerical precision
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Abstract Training of large-scale deep neural networks is often constrained by the available computational resources. We study the effect of limited precision data representation and computation on neural network training. Within the context of lowprecision fixed-point

Learning Features from Music Audio with Deep Belief Networks.
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3. DEEP BELIEF NETWORKS In the last few years, a large amount of research has been conducted arounddeep learning . The goal ofdeep learningis to learn more abstract representations of the in- put data in a layer-wise fashion using unsupervised learn- ing

Saliency detection by multi-contextdeep learning
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Abstract Low-level saliency cues or priors do not produce good enough saliency detection results especially when the salient object presents in a low-contrast background with confusing visual appearance. This issue raises a serious problem for conventional

Project Adam: Building an Efficient and ScalableDeep LearningTraining System.
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ABSTRACT Large deep neural network models have recently demonstrated state-of-the-art accuracy on hard visual recognition tasks. Unfortunately such models are extremely time consuming to train and require large amount of compute cycles. We describe the design and

DEEP LEARNINGPROPERTIES OF GOOD VIDEO GAMES: HOW FAR CAN THEY GO
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In earlier work) I argued that good commercial video games provide players withgood learning (see Gee, 2003, 2005, 2007). Bygood learningI mean learning that is guided by and organized by principles empirically confirmed by systematic research on effective and

Deep learningidentity-preserving face space
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Abstract Face recognition with large pose and illumination variations is a challenging problem in computer vision. This paper addresses this challenge by proposing a new learningbased face representation: the face identity-preserving (FIP) features. Unlike

Hybriddeep learningfor face verification
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Abstract This paper proposes a hybrid convolutional network (ConvNet)-Restricted Boltzmann Machine (RBM) model for face verification in wild conditions. A key contribution of this work is to directly learn relational visual features, which indicate identity similarities,

Deep hashing for compact binary codes learning.
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which cannot well capture the nonlinear structure of samples.Deep Learning :Deep learningaims to learn hierarchi- cal feature representations by building high-level features from raw data. In recent years, a variety of deep learn

Unsupervised feature learning anddeep learning : A review and new perspectives
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AbstractThe success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory factors of variation behind the data.

From facial parts responses to face detection: Adeep learningapproach
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Abstract In this paper, we propose a novel deep convolutional network (DCN) that achieves outstanding performance on FDDB, PASCAL Face, and AFW. Specifically, our method achieves a high recall rate of 90.99% on the challenging FDDB benchmark, outperforming

Deep learningof binary hash codes for fast image retrieval
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Abstract Approximate nearest neighbor search is an efficient strategy for large-scale image retrieval. Encouraged by the recent advances in convolutional neural networks (CNNs), we propose an effectivedeep learningframework to generate binary hash codes for fast image

Deep learningfor efficient discriminative parsing
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Abstract We propose a new fast purely discriminative algorithm for natural language parsing, based on adeeprecurrent convolutional graph transformer network (GTN). Assuming a decomposition of a parse tree into a stack oflevels , the network predicts a level of the tree

Deep learningof invariant features via simulated fixations in video
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Abstract We apply salient feature detection and tracking in videos to simulate fixations and smooth pursuit in human vision. With tracked sequences as input, a hierarchical network of modules learns invariant features using a temporal slowness constraint. The network

Pedestrian detection aided bydeep learningsemantic tasks
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AbstractDeep learningmethods have achieved great successes in pedestrian detection, owing to its ability to learn discriminative features from raw pixels. However, they treat pedestrian detection as a single binary classification task, which may confuse positive with

Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
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Why does unsupervised pre-training helpdeep learning

Deep learning
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Multi-sourcedeep learningfor human pose estimation
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Abstract Visual appearance score, appearance mixture type and deformation are three important information sources for human pose estimation. This paper proposes to build a multi-source deep model in order to extract non-linear representation from these different

Xception:Deep learningwith depthwise separable convolutions
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Abstract We present an interpretation of Inception modules in convolutional neural networks as being an intermediate step in-between regular convolution and the depthwise separable convolution operation (a depthwise convolution followed by a pointwise convolution). In this

Deep multiple instance learning for image classification and auto-annotation
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However, there has been little investigation on how we could build up adeep learning framework in a weakly supervised setting. In this paper, we attempt to modeldeep learning in a weakly supervised learning (multiple instance learning) framework

Measuring deep approaches to learning using the National Survey of Student Engagement
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Student Engagement The concept ofdeep learningis not new to higher education. However,deep learninghas drawn more attention in recent years as institutions attempt to tap their students full learning potential. To more

Multi-stage contextualdeep learningfor pedestrian detection
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Abstract Cascaded classifiers1 have been widely used in pedestrian detection and achieved great success. These classifiers are trained sequentially without joint optimization. In this paper, we propose a new deep model that can jointly train multi-stage classifiers through

Hyper-class augmented and regularizeddeep learningfor fine-grained image classification
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Fine-grained image classification (FGIC) is challenging because (i) finegrained labeled data is much more expensive to acquire (usually requiring domain expertise);(ii) there exists large intra-class and small interclass variance. In this paper, we propose a systematic

Towards a new end: New pedagogies fordeep learning
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The crisisand there is no other word for itin public schooling is a function of the interaction of an enormous push-pull dynamic. The push factor is that students find schooling increasingly boring as they proceed across the grades. Studies from many

Usingdeep learningto enhance cancer diagnosis and classification
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Abstract Using automated computer tools and in particular machine learning to facilitate and enhance medical analysis and diagnosis is a promising and important area. In this paper, we show that how unsupervised feature learning can be used for cancer detection and

Deep learningstrong parts for pedestrian detection
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Abstract Recent advances in pedestrian detection are attained by transferring the learned features of Convolutional Neural Network (ConvNet) to pedestrians. This ConvNet is typically pre-trained with massive general object categories (eg ImageNet). Although these

Efficientdeep learningfor stereo matching
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Abstract In the past year, convolutional neural networks have been shown to perform extremely well for stereo estimation. However, current architectures rely on siamese networks which exploit concatenation followed by further processing layers, requiring a

Pointnet:Deep learningon point sets for 3d classification and segmentation
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Abstract Point cloud is an important type of geometric data structure. Due to its irregular format, most researchers transform such data to regular 3D voxel grids or collections of images. This, however, renders data unnecessarily voluminous and causes issues. In this

The effect of engagement and perceived course value on deep and surface learning strategies
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Abstract This study investigated the relationships among perceived course value, student engagement,deep learningstrategies, and surface learning strategies. The study relied on constructs from pre- vious studies to measure

Multi-manifold deep metric learning for image set classification
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Motivated by the fact that manifold can be effectively used to model the nonlinearity of sam- ples in each image set anddeep learninghas demonstrated superb capability to model the nonlinearity of samples, we propose a MMDML method to learn multiple sets of nonlin- ear

Structural-RNN:Deep learningon spatio-temporal graphs
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Abstract Deep Recurrent Neural Network architectures, though remarkably capable at modeling sequences, lack an intuitive high-level spatio-temporal structure. That is while many problems in computer vision inherently have an underlying high-level structure and

Deep learning
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Insufficient depth can hurt: Depth 2 can be enough but with a price (eg the required number of nodes may grow very large) The brain has a deep architecture: Hierarchical feature representation

When face recognition meets withdeep learning : an evaluation of convolutional neural networks for face recognition
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AbstractDeep learning in particular Convolutional Neural Network (CNN), has achieved promising results in face recognition recently. However, it remains an open question: why CNNs work well and how to design a goodarchitecture. Existing studies tend to focus on

Deep fisher kernels-end to end learning of the fisher kernel gmm parameters
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Abstract Fisher Kernels andDeep Learningwere two developments with significant impact on large-scale object catego- rization in the last years. Both approaches were shown to achieve state-of-the-art results on large-scale object categorization datasets, such as ImageNet

Deep residual learning for image recognition
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Page 1. Deep Residual Learning for Image Recognition Kaiming He Microsoft Research com Abstract Deeper neural networks are more difficult to train

Deep learningshape priors for object segmentation
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Abstract In this paper we introduce a new shape-driven approach for object segmentation. Given a training set of shapes, we first use deep Boltzmann machine to learn the hierarchical architecture of shape priors. This learned hierarchical architecture is then used

Deep learningfor time series modeling
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Demand forecasting is crucial to electricity providers because their ability to produce energy exceeds their ability to store it. Excess demand can causebrown outs,while excess supply ends in waste. In an industry worth over $1 trillion in the US alone, almost 9% of GDP,

Deep learningfor spoken language identification
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Abstract Empirical results have shown that many spoken language identification systems based on hand-coded features perform poorly on small speech samples where a human would be successful. A hypothesis for this low performance is that the set of extracted

The use of formative quizzes fordeep learning
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AbstractSimple formative quizzes are widely used for assessment. These have traditionally tested knowledge and simple comprehension. This paper describes how to construct questions to test all cognitive levels of learning for a course in introductory

Deep learningas an opportunity in virtual screening
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AbstractDeep learningexcels in vision and speech applications where it pushed the stateof- the-art to a new level. However its impact on other fields remains to be shown. The Merck Kaggle challenge on chemical compound activity was won by Hintons group with deep

Uncertainty indeep learning
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AbstractDeep learninghas attracted tremendous attention from researchers in various fields of information engineering such as AI, computer vision, and language processing [Kalchbrenner and Blunsom, 2013; Krizhevsky et al., 2012; Mnih et al., 2013], but also from

UsingDeep LearningNeural Networks To Find Best Performing Audience Segments
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Abstract: Finding the appropriate mobile audience for mobile advertising is always challenging since there are many data points that need to be considered and assimilated before a target segment can be created and used in ad serving by any ad server.Deep

Deep learningand college outcomes: Do fields of study differ
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Students have more learning potential than traditional pedagogical methods often tap. To more fully develop student talents, many campuses are shifting from a passive, instructor- dominated pedagogy to active, learner-centered activities. This study uses data from the

Deep learningwith H2O
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A Candel, V Parmar, E LeDell, A Arora H2O. ai Inc, 2016 docs.h2o.ai This document introduces the reader toDeep Learningwith H2O. Examples are written in R and Python. Topics include:installation of H2ObasicDeep Learningconceptsbuilding deep neural nets in H2Ohow to interpret model outputhow to make predictions as well as

An overview of deep-structured learning for information processing
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for unsupervised feature learning. First, the brief history ofdeep learningis discussed. Then, I develop a classificatory scheme to analyze and summarize major work reported in thedeep learningliterature. Using this scheme

Deep learningand attentional bias in human category learning
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Abstract: Human category learning is known to be a function of both the complexity of the category rule and attentional bias. A classic and critically diagnostic human category problem involves learning integral stimuli (correlated features) using a condensation rule, or

Modeling local and global deformations indeep learning : Epitomic convolution, multiple instance learning, and sliding window detection
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Abstract Deep Convolutional Neural Networks (DCNNs) achieve invariance to domain transformations (deformations) by using multiple max-pooling(MP) layers. In this work we show that alternative methods of modeling deformations can improve the accuracy and

Learning compact binary descriptors with unsupervised deep neural networks
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In this work, we raise a question can we learn binary descriptor from data without labels Inspiring from the re- cent advancement ofdeep learning we propose an effectivedeep learningapproach, dubbed DeepBit, to learn compact binary descriptors

Combining multiple dynamic models anddeep learningarchitectures for tracking the left ventricle endocardium in ultrasound data
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Abstract We present a new statistical pattern recognition approach for the problem of the left ventricle endocardium tracking in ultrasound data. The problem is formulated as a sequential importance resampling algorithm such that the expected segmentation of the -SOFTWARE SALES SERVICE-https://www.engpaper.net--