Unnamed: 0.1 int64 0 41k | Unnamed: 0 int64 0 41k | author stringlengths 9 1.39k | id stringlengths 11 18 | summary stringlengths 25 3.66k | title stringlengths 4 258 | year int64 1.99k 2.02k | arxiv_url stringlengths 32 39 | info stringlengths 523 3.18k | embeddings stringlengths 16.9k 17.1k |
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1,200 | 1,200 | ['Wenlin Chen', 'James T. Wilson', 'Stephen Tyree', 'Kilian Q. Weinberger', 'Yixin Chen'] | 1506.04449v1 | Convolutional neural networks (CNN) are increasingly used in many areas of
computer vision. They are particularly attractive because of their ability to
"absorb" great quantities of labeled data through millions of parameters.
However, as model sizes increase, so do the storage and memory requirements of
the classifier... | Compressing Convolutional Neural Networks | 2,015 | http://arxiv.org/pdf/1506.04449v1 | Title Compressing Convolutional Neural Networks Summary Convolutional neural network CNN increasingly used many area computer vision particularly attractive ability absorb great quantity labeled data million parameter However model size increase storage memory requirement classifier present novel network architecture F... | [-0.04463288187980652, 0.06163084879517555, -0.012253152206540108, 0.05511300638318062, -0.0025499064940959215, -0.012141133658587933, 0.043387748301029205, 0.03678958863019943, -0.05649621784687042, 0.02461875230073929, -0.01154039055109024, 0.007315043825656176, -0.009642541408538818, 0.06366273015737534, 0.008259010... |
1,201 | 1,201 | ['Jason Yosinski', 'Jeff Clune', 'Anh Nguyen', 'Thomas Fuchs', 'Hod Lipson'] | 1506.06579v1 | Recent years have produced great advances in training large, deep neural
networks (DNNs), including notable successes in training convolutional neural
networks (convnets) to recognize natural images. However, our understanding of
how these models work, especially what computations they perform at
intermediate layers, h... | Understanding Neural Networks Through Deep Visualization | 2,015 | http://arxiv.org/pdf/1506.06579v1 | Title Understanding Neural Networks Deep Visualization Summary Recent year produced great advance training large deep neural network DNNs including notable success training convolutional neural network convnets recognize natural image However understanding model work especially computation perform intermediate layer la... | [-0.0007392374100163579, 0.012455489486455917, -0.013493246398866177, 0.059293001890182495, 0.0012556114234030247, 0.007671872619539499, 0.042305462062358856, 0.009318207390606403, -0.04370226711034775, 0.02192402444779873, -0.028857452794909477, 0.036842938512563705, 0.00389855750836432, 0.07204461097717285, 0.0410939... |
1,202 | 1,202 | ["Keiron O'Shea"] | 1507.05053v1 | Greedy Restrictive Boltzmann Machines yield an fairly low 0.72% error rate on
the famous MNIST database of handwritten digits. All that was required to
achieve this result was a high number of hidden layers consisting of many
neurons, and a graphics card to greatly speed up the rate of learning. | Massively Deep Artificial Neural Networks for Handwritten Digit
Recognition | 2,015 | http://arxiv.org/pdf/1507.05053v1 | Title Massively Deep Artificial Neural Networks Handwritten Digit Recognition Summary Greedy Restrictive Boltzmann Machines yield fairly low 072 error rate famous MNIST database handwritten digit required achieve result high number hidden layer consisting many neuron graphic card greatly speed rate learning Authors 0 A... | [-0.006818361114710569, 0.03810611739754677, -0.030558491125702858, 0.0776267796754837, 0.0075163389556109905, -0.0014100931584835052, 0.07726393640041351, 0.007971227169036865, -0.012542841956019402, 0.02090977504849434, 0.00881256815046072, -0.003433726029470563, 0.018626883625984192, 0.05653011426329613, 0.005641664... |
1,203 | 1,203 | ['Shuchang Zhou', 'Jia-Nan Wu'] | 1507.05775v2 | In this paper we propose and study a technique to reduce the number of
parameters and computation time in fully-connected layers of neural networks
using Kronecker product, at a mild cost of the prediction quality. The
technique proceeds by replacing Fully-Connected layers with so-called Kronecker
Fully-Connected layer... | Compression of Fully-Connected Layer in Neural Network by Kronecker
Product | 2,015 | http://arxiv.org/pdf/1507.05775v2 | Title Compression FullyConnected Layer Neural Network Kronecker Product Summary paper propose study technique reduce number parameter computation time fullyconnected layer neural network using Kronecker product mild cost prediction quality technique proceeds replacing FullyConnected layer socalled Kronecker FullyConnec... | [0.012350041419267654, 0.06303177028894424, -0.0011156087275594473, 0.06900572776794434, 0.00630797166377306, 0.010950058698654175, 0.03933585807681084, 0.03983045741915703, -0.0337163545191288, 0.034862034022808075, -0.017430569976568222, -0.009826810099184513, 0.03917694836854935, 0.019877038896083832, 0.024440456181... |
1,204 | 1,204 | ['David Held', 'Sebastian Thrun', 'Silvio Savarese'] | 1507.08286v1 | Deep learning methods have typically been trained on large datasets in which
many training examples are available. However, many real-world product datasets
have only a small number of images available for each product. We explore the
use of deep learning methods for recognizing object instances when we have only
a sin... | Deep Learning for Single-View Instance Recognition | 2,015 | http://arxiv.org/pdf/1507.08286v1 | Title Deep Learning SingleView Instance Recognition Summary Deep learning method typically trained large datasets many training example available However many realworld product datasets small number image available product explore use deep learning method recognizing object instance single training example per class sh... | [-0.03700682520866394, 0.0302085280418396, 0.07231617718935013, 0.059746719896793365, -0.007951096631586552, 0.015302322804927826, 0.06946630775928497, -0.009710571728646755, -0.008127566426992416, -0.02611890248954296, 0.00427075382322073, 0.04233561083674431, -0.01811395399272442, 0.06964488327503204, 0.0286777149885... |
1,205 | 1,205 | ['D. Garcia-Gasulla', 'J. Béjar', 'U. Cortés', 'E. Ayguadé', 'J. Labarta', 'T. Suzumura', 'R. Chen'] | 1507.08818v6 | Vector-space word representations obtained from neural network models have
been shown to enable semantic operations based on vector arithmetic. In this
paper, we explore the existence of similar information on vector
representations of images. For that purpose we define a methodology to obtain
large, sparse vector repr... | A Visual Embedding for the Unsupervised Extraction of Abstract Semantics | 2,015 | http://arxiv.org/pdf/1507.08818v6 | Title Visual Embedding Unsupervised Extraction Abstract Semantics Summary Vectorspace word representation obtained neural network model shown enable semantic operation based vector arithmetic paper explore existence similar information vector representation image purpose define methodology obtain large sparse vector re... | [0.01893017813563347, 0.0445433147251606, -0.026213865727186203, 0.07263161242008209, -0.00889803096652031, 0.019370445981621742, 0.02691350318491459, 0.02719893679022789, -0.0438244603574276, -0.02506692335009575, 0.025498639792203903, 0.02854757569730282, 0.026448871940374374, -0.0012791512999683619, 0.04734561964869... |
1,206 | 1,206 | ['Zhibin Liao', 'Gustavo Carneiro'] | 1508.00330v2 | Deep feedforward neural networks with piecewise linear activations are
currently producing the state-of-the-art results in several public datasets.
The combination of deep learning models and piecewise linear activation
functions allows for the estimation of exponentially complex functions with the
use of a large numbe... | On the Importance of Normalisation Layers in Deep Learning with
Piecewise Linear Activation Units | 2,015 | http://arxiv.org/pdf/1508.00330v2 | Title Importance Normalisation Layers Deep Learning Piecewise Linear Activation Units Summary Deep feedforward neural network piecewise linear activation currently producing stateoftheart result several public datasets combination deep learning model piecewise linear activation function allows estimation exponentially ... | [-0.04846473038196564, 0.035292260348796844, -0.02174227498471737, 0.01446592714637518, 0.02305065281689167, 0.0014239975716918707, 0.09119880199432373, -0.0049864850006997585, -0.027966203168034554, 0.024907488375902176, -0.019258320331573486, -0.028377695009112358, -0.0119673702865839, 0.09649862349033356, 0.00079292... |
1,207 | 1,207 | ['Pooya Khorrami', 'Tom Le Paine', 'Thomas S. Huang'] | 1510.02969v3 | Despite being the appearance-based classifier of choice in recent years,
relatively few works have examined how much convolutional neural networks
(CNNs) can improve performance on accepted expression recognition benchmarks
and, more importantly, examine what it is they actually learn. In this work,
not only do we show... | Do Deep Neural Networks Learn Facial Action Units When Doing Expression
Recognition? | 2,015 | http://arxiv.org/pdf/1510.02969v3 | Title Deep Neural Networks Learn Facial Action Units Expression Recognition Summary Despite appearancebased classifier choice recent year relatively work examined much convolutional neural network CNNs improve performance accepted expression recognition benchmark importantly examine actually learn work show CNNs achiev... | [0.011459064669907093, 0.059427086263895035, -0.014143026433885098, 0.0437263585627079, 0.02084403857588768, 0.034001708030700684, 0.05372658371925354, 0.025187969207763672, -0.019521865993738174, 0.013856342062354088, -0.0031554359011352062, 0.015224515460431576, -0.0230898167937994, 0.070708729326725, 0.0646035298705... |
1,208 | 1,208 | ['Jure Žbontar', 'Yann LeCun'] | 1510.05970v2 | We present a method for extracting depth information from a rectified image
pair. Our approach focuses on the first stage of many stereo algorithms: the
matching cost computation. We approach the problem by learning a similarity
measure on small image patches using a convolutional neural network. Training
is carried ou... | Stereo Matching by Training a Convolutional Neural Network to Compare
Image Patches | 2,015 | http://arxiv.org/pdf/1510.05970v2 | Title Stereo Matching Training Convolutional Neural Network Compare Image Patches Summary present method extracting depth information rectified image pair approach focus first stage many stereo algorithm matching cost computation approach problem learning similarity measure small image patch using convolutional neural ... | [-0.0351475328207016, 0.05099889636039734, 0.017119554802775383, 0.0978991761803627, -0.024354256689548492, -0.015232251025736332, -0.0027228386607021093, -0.0017938062082976103, -0.04783998057246208, -0.006862140726298094, -0.014630568213760853, 0.029849525541067123, 0.05968781188130379, 0.07348904758691788, 0.0161718... |
1,209 | 1,209 | ['Aleksandar Zlateski', 'Kisuk Lee', 'H. Sebastian Seung'] | 1510.06706v1 | Convolutional networks (ConvNets) have become a popular approach to computer
vision. It is important to accelerate ConvNet training, which is
computationally costly. We propose a novel parallel algorithm based on
decomposition into a set of tasks, most of which are convolutions or FFTs.
Applying Brent's theorem to the ... | ZNN - A Fast and Scalable Algorithm for Training 3D Convolutional
Networks on Multi-Core and Many-Core Shared Memory Machines | 2,015 | http://arxiv.org/pdf/1510.06706v1 | Title ZNN Fast Scalable Algorithm Training 3D Convolutional Networks MultiCore ManyCore Shared Memory Machines Summary Convolutional network ConvNets become popular approach computer vision important accelerate ConvNet training computationally costly propose novel parallel algorithm based decomposition set task convolu... | [-0.0027172863483428955, -0.0008499150862917304, -0.0034768441691994667, 0.09231258183717728, -2.317625330761075e-05, 0.002218489535152912, 0.039021629840135574, -0.014027212746441364, -0.031511902809143066, 0.008283151313662529, -0.02940419502556324, 0.02358069457113743, 0.028758520260453224, 0.01312054879963398, 0.00... |
1,210 | 1,210 | ['Vijay Badrinarayanan', 'Alex Kendall', 'Roberto Cipolla'] | 1511.00561v3 | We present a novel and practical deep fully convolutional neural network
architecture for semantic pixel-wise segmentation termed SegNet. This core
trainable segmentation engine consists of an encoder network, a corresponding
decoder network followed by a pixel-wise classification layer. The architecture
of the encoder... | SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image
Segmentation | 2,015 | http://arxiv.org/pdf/1511.00561v3 | Title SegNet Deep Convolutional EncoderDecoder Architecture Image Segmentation Summary present novel practical deep fully convolutional neural network architecture semantic pixelwise segmentation termed SegNet core trainable segmentation engine consists encoder network corresponding decoder network followed pixelwise c... | [-0.015823449939489365, -0.006357388570904732, 0.027102001011371613, 0.08989871293306351, -0.0036129863001406193, -0.009315085597336292, 0.021208327263593674, -0.03256989270448685, -0.04699869453907013, 0.019279733300209045, -0.007104781456291676, 0.08203595876693726, -0.02864234521985054, 0.027319790795445442, 0.00020... |
1,211 | 1,211 | ['Yuke Zhu', 'Oliver Groth', 'Michael Bernstein', 'Li Fei-Fei'] | 1511.03416v4 | We have seen great progress in basic perceptual tasks such as object
recognition and detection. However, AI models still fail to match humans in
high-level vision tasks due to the lack of capacities for deeper reasoning.
Recently the new task of visual question answering (QA) has been proposed to
evaluate a model's cap... | Visual7W: Grounded Question Answering in Images | 2,015 | http://arxiv.org/pdf/1511.03416v4 | Title Visual7W Grounded Question Answering Images Summary seen great progress basic perceptual task object recognition detection However AI model still fail match human highlevel vision task due lack capacity deeper reasoning Recently new task visual question answering QA proposed evaluate model capacity deep image und... | [0.05107489973306656, -0.0074351998046040535, 0.004608576186001301, 0.061327192932367325, 0.006238750647753477, 0.014390667900443077, 0.004267389886081219, 0.018421735614538193, 0.018133854493498802, -0.034159690141677856, -0.02181611768901348, 0.021694866940379143, -0.005826032254844904, 0.0415089875459671, 0.02757996... |
1,212 | 1,212 | ['Lionel Pibre', 'Pasquet Jérôme', 'Dino Ienco', 'Marc Chaumont'] | 1511.04855v2 | Since the BOSS competition, in 2010, most steganalysis approaches use a
learning methodology involving two steps: feature extraction, such as the Rich
Models (RM), for the image representation, and use of the Ensemble Classifier
(EC) for the learning step. In 2015, Qian et al. have shown that the use of a
deep learning... | Deep learning is a good steganalysis tool when embedding key is reused
for different images, even if there is a cover source-mismatch | 2,015 | http://arxiv.org/pdf/1511.04855v2 | Title Deep learning good steganalysis tool embedding key reused different image even cover sourcemismatch Summary Since BOSS competition 2010 steganalysis approach use learning methodology involving two step feature extraction Rich Models RM image representation use Ensemble Classifier EC learning step 2015 Qian et al ... | [0.0007217373349703848, 0.03198404982686043, -0.004846740979701281, 0.06166112422943115, -0.06095695123076439, -0.033450644463300705, 0.08221230655908585, 0.033968161791563034, -0.015449803322553635, -0.011888159438967705, 0.03574667125940323, 0.04193343222141266, -0.0034147421829402447, 0.11006923764944077, 0.04086233... |
1,213 | 1,213 | ['Ashesh Jain', 'Amir R. Zamir', 'Silvio Savarese', 'Ashutosh Saxena'] | 1511.05298v3 | 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 can benefit from it. Spatio-temporal graphs are a
popular tool for i... | Structural-RNN: Deep Learning on Spatio-Temporal Graphs | 2,015 | http://arxiv.org/pdf/1511.05298v3 | Title StructuralRNN Deep Learning SpatioTemporal Graphs Summary Deep Recurrent Neural Network architecture though remarkably capable modeling sequence lack intuitive highlevel spatiotemporal structure many problem computer vision inherently underlying highlevel structure benefit Spatiotemporal graph popular tool imposi... | [-0.027727581560611725, 0.011094358749687672, 0.00748787447810173, 0.016364485025405884, -0.0006045926711522043, 0.0014146527973935008, 0.03624793142080307, -0.014842328615486622, -0.0188528373837471, -0.014680354855954647, 0.03022313304245472, -0.04048275202512741, 0.06506609171628952, 0.0663042962551117, 0.0269631296... |
1,214 | 1,214 | ['Suraj Srinivas', 'R. Venkatesh Babu'] | 1511.05497v2 | Deep neural networks with millions of parameters are at the heart of many
state of the art machine learning models today. However, recent works have
shown that models with much smaller number of parameters can also perform just
as well. In this work, we introduce the problem of architecture-learning, i.e;
learning the ... | Learning Neural Network Architectures using Backpropagation | 2,015 | http://arxiv.org/pdf/1511.05497v2 | Title Learning Neural Network Architectures using Backpropagation Summary Deep neural network million parameter heart many state art machine learning model today However recent work shown model much smaller number parameter also perform well work introduce problem architecturelearning ie learning architecture neural ne... | [0.009205243550240993, 0.007152481470257044, -0.030795007944107056, 0.010060195811092854, 0.050279419869184494, -0.05333491414785385, 0.04178154468536377, -0.002634103875607252, -0.022237518802285194, 0.011714121326804161, -0.004620780702680349, 0.007795977406203747, 0.0006253026658669114, 0.034281179308891296, 0.05595... |
1,215 | 1,215 | ['George Toderici', "Sean M. O'Malley", 'Sung Jin Hwang', 'Damien Vincent', 'David Minnen', 'Shumeet Baluja', 'Michele Covell', 'Rahul Sukthankar'] | 1511.06085v5 | A large fraction of Internet traffic is now driven by requests from mobile
devices with relatively small screens and often stringent bandwidth
requirements. Due to these factors, it has become the norm for modern
graphics-heavy websites to transmit low-resolution, low-bytecount image
previews (thumbnails) as part of th... | Variable Rate Image Compression with Recurrent Neural Networks | 2,015 | http://arxiv.org/pdf/1511.06085v5 | Title Variable Rate Image Compression Recurrent Neural Networks Summary large fraction Internet traffic driven request mobile device relatively small screen often stringent bandwidth requirement Due factor become norm modern graphicsheavy website transmit lowresolution lowbytecount image preview thumbnail part initial ... | [0.0018737510545179248, 0.028686663135886192, -0.019925139844417572, 0.042102329432964325, -0.015018021687865257, -0.018420454114675522, 0.04429816082119942, 0.02109697461128235, -0.0697450265288353, -0.020246099680662155, 0.01047572772949934, -0.018706675618886948, 0.0055426787585020065, 0.060231972485780716, 0.024832... |
1,216 | 1,216 | ['Stefan Lee', 'Senthil Purushwalkam', 'Michael Cogswell', 'David Crandall', 'Dhruv Batra'] | 1511.06314v1 | Convolutional Neural Networks have achieved state-of-the-art performance on a
wide range of tasks. Most benchmarks are led by ensembles of these powerful
learners, but ensembling is typically treated as a post-hoc procedure
implemented by averaging independently trained models with model variation
induced by bagging or... | Why M Heads are Better than One: Training a Diverse Ensemble of Deep
Networks | 2,015 | http://arxiv.org/pdf/1511.06314v1 | Title Heads Better One Training Diverse Ensemble Deep Networks Summary Convolutional Neural Networks achieved stateoftheart performance wide range task benchmark led ensemble powerful learner ensembling typically treated posthoc procedure implemented averaging independently trained model model variation induced bagging... | [-0.030866816639900208, 0.05782420560717583, -0.004393278621137142, 0.02829958312213421, 0.03423222899436951, 0.00753226550295949, 0.03818124160170555, -0.025428064167499542, -0.042238421738147736, 0.020469633862376213, -0.008617752231657505, -0.01240132749080658, 0.013209804892539978, 0.03680972754955292, 0.0112457703... |
1,217 | 1,217 | ['Yani Ioannou', 'Duncan Robertson', 'Jamie Shotton', 'Roberto Cipolla', 'Antonio Criminisi'] | 1511.06744v3 | We propose a new method for creating computationally efficient convolutional
neural networks (CNNs) by using low-rank representations of convolutional
filters. Rather than approximating filters in previously-trained networks with
more efficient versions, we learn a set of small basis filters from scratch;
during traini... | Training CNNs with Low-Rank Filters for Efficient Image Classification | 2,015 | http://arxiv.org/pdf/1511.06744v3 | Title Training CNNs LowRank Filters Efficient Image Classification Summary propose new method creating computationally efficient convolutional neural network CNNs using lowrank representation convolutional filter Rather approximating filter previouslytrained network efficient version learn set small basis filter scratc... | [0.013833130709826946, 0.0065141646191477776, -0.0036438454408198595, 0.09312603622674942, 0.04233706742525101, -0.01859935373067856, 0.018550774082541466, 0.02881435863673687, 0.027457937598228455, -0.017694061622023582, -0.02891833893954754, -0.0006739930831827223, -0.00835569016635418, 0.01039261743426323, 0.0296271... |
1,218 | 1,218 | ['Leslie N. Smith', 'Emily M. Hand', 'Timothy Doster'] | 1511.06951v1 | We introduce the concept of dynamically growing a neural network during
training. In particular, an untrainable deep network starts as a trainable
shallow network and newly added layers are slowly, organically added during
training, thereby increasing the network's depth. This is accomplished by a new
layer, which we c... | Gradual DropIn of Layers to Train Very Deep Neural Networks | 2,015 | http://arxiv.org/pdf/1511.06951v1 | Title Gradual DropIn Layers Train Deep Neural Networks Summary introduce concept dynamically growing neural network training particular untrainable deep network start trainable shallow network newly added layer slowly organically added training thereby increasing network depth accomplished new layer call DropIn DropIn ... | [0.007731180172413588, 0.05761919915676117, -0.04624626040458679, 0.021057158708572388, -5.801829684060067e-05, 0.006422598380595446, 0.0274411179125309, 0.0029014733154326677, -0.008146865293383598, 0.045804914087057114, 0.02467726357281208, 0.06007247790694237, -0.023067601025104523, 0.0986555814743042, 0.00203208439... |
1,219 | 1,219 | ['Spyros Gidaris', 'Nikos Komodakis'] | 1511.07763v2 | We propose a novel object localization methodology with the purpose of
boosting the localization accuracy of state-of-the-art object detection
systems. Our model, given a search region, aims at returning the bounding box
of an object of interest inside this region. To accomplish its goal, it relies
on assigning conditi... | LocNet: Improving Localization Accuracy for Object Detection | 2,015 | http://arxiv.org/pdf/1511.07763v2 | Title LocNet Improving Localization Accuracy Object Detection Summary propose novel object localization methodology purpose boosting localization accuracy stateoftheart object detection system model given search region aim returning bounding box object interest inside region accomplish goal relies assigning conditional... | [0.006463375873863697, -0.017804117873311043, 0.006045058835297823, 0.0758776143193245, -0.011595061048865318, -0.02267525903880596, 0.015514788217842579, 0.01747891493141651, 0.014340521767735481, 0.006594728212803602, -0.027386587113142014, 0.03774680197238922, -0.009540103375911713, 0.047759633511304855, 0.000552312... |
1,220 | 1,220 | ['Haibing Wu', 'Xiaodong Gu'] | 1512.00242v1 | Recently, dropout has seen increasing use in deep learning. For deep
convolutional neural networks, dropout is known to work well in fully-connected
layers. However, its effect in convolutional and pooling layers is still not
clear. This paper demonstrates that max-pooling dropout is equivalent to
randomly picking acti... | Towards Dropout Training for Convolutional Neural Networks | 2,015 | http://arxiv.org/pdf/1512.00242v1 | Title Towards Dropout Training Convolutional Neural Networks Summary Recently dropout seen increasing use deep learning deep convolutional neural network dropout known work well fullyconnected layer However effect convolutional pooling layer still clear paper demonstrates maxpooling dropout equivalent randomly picking ... | [0.005901362746953964, 0.0003758076927624643, -0.0017538379179313779, 0.043250299990177155, 0.02279280126094818, -0.01044152770191431, 0.0548686645925045, -0.012765503488481045, -0.013536915183067322, 0.010258102789521217, -0.007797673344612122, 0.027180422097444534, -0.008747848682105541, 0.05980594456195831, 0.012807... |
1,221 | 1,221 | ['Edward Grant', 'Stephan Sahm', 'Mariam Zabihi', 'Marcel van Gerven'] | 1512.01289v2 | Judgments about personality based on facial appearance are strong effectors
in social decision making, and are known to have impact on areas from
presidential elections to jury decisions. Recent work has shown that it is
possible to predict perception of memorability, trustworthiness, intelligence
and other attributes ... | Predicting and visualizing psychological attributions with a deep neural
network | 2,015 | http://arxiv.org/pdf/1512.01289v2 | Title Predicting visualizing psychological attribution deep neural network Summary Judgments personality based facial appearance strong effector social decision making known impact area presidential election jury decision Recent work shown possible predict perception memorability trustworthiness intelligence attribute ... | [0.02326471358537674, 0.09605322778224945, -0.04167664423584938, -0.01901482790708542, -0.0010711831273511052, 0.027963444590568542, 0.04361509531736374, -0.015402032062411308, -0.013899405486881733, 0.03444215655326843, -0.011700818315148354, -0.002669362584128976, 0.017736410722136497, 0.07271398603916168, 0.07164623... |
1,222 | 1,222 | ['Haibing Wu', 'Xiaodong Gu'] | 1512.01400v1 | Recently, dropout has seen increasing use in deep learning. For deep
convolutional neural networks, dropout is known to work well in fully-connected
layers. However, its effect in pooling layers is still not clear. This paper
demonstrates that max-pooling dropout is equivalent to randomly picking
activation based on a ... | Max-Pooling Dropout for Regularization of Convolutional Neural Networks | 2,015 | http://arxiv.org/pdf/1512.01400v1 | Title MaxPooling Dropout Regularization Convolutional Neural Networks Summary Recently dropout seen increasing use deep learning deep convolutional neural network dropout known work well fullyconnected layer However effect pooling layer still clear paper demonstrates maxpooling dropout equivalent randomly picking activ... | [0.016897985711693764, -7.620026008225977e-05, 0.0044533731415867805, 0.031333647668361664, 0.0012570390244945884, -0.023930512368679047, 0.04078090190887451, -0.009809983894228935, -0.015115304850041866, 0.009812970645725727, 0.006984876934438944, 0.01681486889719963, -0.008236587047576904, 0.03719208762049675, 0.0213... |
1,223 | 1,223 | ['Volodymyr Turchenko', 'Artur Luczak'] | 1512.01596v3 | The development of a deep (stacked) convolutional auto-encoder in the Caffe
deep learning framework is presented in this paper. We describe simple
principles which we used to create this model in Caffe. The proposed model of
convolutional auto-encoder does not have pooling/unpooling layers yet. The
results of our exper... | Creation of a Deep Convolutional Auto-Encoder in Caffe | 2,015 | http://arxiv.org/pdf/1512.01596v3 | Title Creation Deep Convolutional AutoEncoder Caffe Summary development deep stacked convolutional autoencoder Caffe deep learning framework presented paper describe simple principle used create model Caffe proposed model convolutional autoencoder poolingunpooling layer yet result experimental research show comparable ... | [-0.01935165748000145, 0.02691812813282013, -0.02918732911348343, 0.06044015288352966, 0.013700141571462154, -0.003019306343048811, 0.07921355962753296, -0.019282866269350052, -0.0870761051774025, 0.006803006865084171, -0.0018187519162893295, 0.020771779119968414, -0.00492615532130003, 0.060892872512340546, -0.00297425... |
1,224 | 1,224 | ['Michael Maire', 'Takuya Narihira', 'Stella X. Yu'] | 1512.02767v2 | Spectral embedding provides a framework for solving perceptual organization
problems, including image segmentation and figure/ground organization. From an
affinity matrix describing pairwise relationships between pixels, it clusters
pixels into regions, and, using a complex-valued extension, orders pixels
according to ... | Affinity CNN: Learning Pixel-Centric Pairwise Relations for
Figure/Ground Embedding | 2,015 | http://arxiv.org/pdf/1512.02767v2 | Title Affinity CNN Learning PixelCentric Pairwise Relations FigureGround Embedding Summary Spectral embedding provides framework solving perceptual organization problem including image segmentation figureground organization affinity matrix describing pairwise relationship pixel cluster pixel region using complexvalued ... | [-0.026018617674708366, 0.008031721226871014, -0.001296941307373345, 0.07457586377859116, -0.01237773708999157, -0.019648507237434387, -0.004271939396858215, -0.012492301873862743, -0.022054489701986313, 0.01679740473628044, -0.011167469434440136, 0.03816167265176773, -0.0008061739499680698, -0.00455609243363142, 0.028... |
1,225 | 1,225 | ['K. Eswaran', 'K. Damodhar Rao'] | 1512.04509v2 | Recently an algorithm, was discovered, which separates points in n-dimension
by planes in such a manner that no two points are left un-separated by at least
one plane{[}1-3{]}. By using this new algorithm we show that there are two ways
of classification by a neural network, for a large dimension feature space,
both of... | On non-iterative training of a neural classifier | 2,015 | http://arxiv.org/pdf/1512.04509v2 | Title noniterative training neural classifier Summary Recently algorithm discovered separate point ndimension plane manner two point left unseparated least one plane13 using new algorithm show two way classification neural network large dimension feature space noniterative deterministic demonstrate power method apply e... | [-0.04375074803829193, 0.015573645941913128, -0.025961928069591522, 0.08972742408514023, -0.02186267450451851, 0.014111526310443878, 0.05056721717119217, -0.013636123389005661, -0.033608149737119675, -0.02154446765780449, 0.006811062339693308, 0.005885583348572254, 0.035941798239946365, 0.017500808462500572, 0.03784235... |
1,226 | 1,226 | ['Jiaji Huang', 'Qiang Qiu', 'Robert Calderbank', 'Guillermo Sapiro'] | 1512.06757v2 | Deep neural networks have proved very successful in domains where large
training sets are available, but when the number of training samples is small,
their performance suffers from overfitting. Prior methods of reducing
overfitting such as weight decay, Dropout and DropConnect are data-independent.
This paper proposes... | GraphConnect: A Regularization Framework for Neural Networks | 2,015 | http://arxiv.org/pdf/1512.06757v2 | Title GraphConnect Regularization Framework Neural Networks Summary Deep neural network proved successful domain large training set available number training sample small performance suffers overfitting Prior method reducing overfitting weight decay Dropout DropConnect dataindependent paper proposes new method GraphCon... | [-0.019845163449645042, 0.032285433262586594, -0.006354076322168112, 0.03249770775437355, -0.001878057257272303, -0.07909730076789856, 0.015089893713593483, 0.011589949950575829, 0.04447934031486511, 0.04493793472647667, 0.01331805158406496, 0.027606826275587082, 0.0083230622112751, 0.06991183757781982, 0.0275301113724... |
1,227 | 1,227 | ['Jiuxiang Gu', 'Zhenhua Wang', 'Jason Kuen', 'Lianyang Ma', 'Amir Shahroudy', 'Bing Shuai', 'Ting Liu', 'Xingxing Wang', 'Li Wang', 'Gang Wang', 'Jianfei Cai', 'Tsuhan Chen'] | 1512.07108v6 | In the last few years, deep learning has led to very good performance on a
variety of problems, such as visual recognition, speech recognition and natural
language processing. Among different types of deep neural networks,
convolutional neural networks have been most extensively studied. Leveraging on
the rapid growth ... | Recent Advances in Convolutional Neural Networks | 2,015 | http://arxiv.org/pdf/1512.07108v6 | Title Recent Advances Convolutional Neural Networks Summary last year deep learning led good performance variety problem visual recognition speech recognition natural language processing Among different type deep neural network convolutional neural network extensively studied Leveraging rapid growth amount annotated da... | [0.05359747260808945, 0.024194877594709396, -0.0008037922088988125, 0.09627276659011841, -0.00257887109182775, -0.036091335117816925, 0.03494212031364441, 0.030311036854982376, -0.0066754515282809734, -0.038316793739795685, -0.07044162601232529, 0.0075162118300795555, 0.020437592640519142, 0.05179395526647568, 0.035933... |
1,228 | 1,228 | ['Zhangyang Wang', 'Shiyu Chang', 'Florin Dolcos', 'Diane Beck', 'Ding Liu', 'Thomas S. Huang'] | 1601.04155v2 | Image aesthetics assessment has been challenging due to its subjective
nature. Inspired by the scientific advances in the human visual perception and
neuroaesthetics, we design Brain-Inspired Deep Networks (BDN) for this task.
BDN first learns attributes through the parallel supervised pathways, on a
variety of selecte... | Brain-Inspired Deep Networks for Image Aesthetics Assessment | 2,016 | http://arxiv.org/pdf/1601.04155v2 | Title BrainInspired Deep Networks Image Aesthetics Assessment Summary Image aesthetic assessment challenging due subjective nature Inspired scientific advance human visual perception neuroaesthetics design BrainInspired Deep Networks BDN task BDN first learns attribute parallel supervised pathway variety selected featu... | [0.024955784901976585, 0.08123048394918442, -0.014758794568479061, 0.010384960100054741, -0.0019827804062515497, 0.018979554995894432, 0.024620572105050087, -0.014311722479760647, -0.05005025491118431, 0.01838209107518196, -0.06827984005212784, 0.008559571579098701, 0.03436597064137459, 0.03679542616009712, 0.013159986... |
1,229 | 1,229 | ['Weiguang Ding', 'Graham Taylor'] | 1602.07383v1 | Monitoring the number of insect pests is a crucial component in
pheromone-based pest management systems. In this paper, we propose an automatic
detection pipeline based on deep learning for identifying and counting pests in
images taken inside field traps. Applied to a commercial codling moth dataset,
our method shows ... | Automatic Moth Detection from Trap Images for Pest Management | 2,016 | http://arxiv.org/pdf/1602.07383v1 | Title Automatic Moth Detection Trap Images Pest Management Summary Monitoring number insect pest crucial component pheromonebased pest management system paper propose automatic detection pipeline based deep learning identifying counting pest image taken inside field trap Applied commercial codling moth dataset method s... | [-0.03675941005349159, 0.02926117181777954, 0.025709254667162895, 0.022765884175896645, -0.05537954345345497, -0.059473756700754166, 0.012641625478863716, -0.05560461804270744, 0.00659280875697732, -0.0009184268419630826, 0.028968779370188713, 0.048867370933294296, -0.019907567650079727, 0.061927732080221176, 0.0139992... |
1,230 | 1,230 | ['Shumeet Baluja'] | 1603.04000v1 | Typography is a ubiquitous art form that affects our understanding,
perception, and trust in what we read. Thousands of different font-faces have
been created with enormous variations in the characters. In this paper, we
learn the style of a font by analyzing a small subset of only four letters.
From these four letters... | Learning Typographic Style | 2,016 | http://arxiv.org/pdf/1603.04000v1 | Title Learning Typographic Style Summary Typography ubiquitous art form affect understanding perception trust read Thousands different fontfaces created enormous variation character paper learn style font analyzing small subset four letter four letter learn two task first discrimination task given four letter new candi... | [0.025311928242444992, 0.04977130889892578, -0.030461637303233147, 0.009247307665646076, -0.003765208413824439, 0.012525367550551891, 0.04215028136968613, 0.0364910364151001, -0.015570483170449734, 0.006841660477221012, 0.008558154106140137, -0.03144262358546257, 0.0500129871070385, 0.05901293829083443, 0.0281207393854... |
1,231 | 1,231 | ['Adedotun Akintayo', 'Kin Gwn Lore', 'Soumalya Sarkar', 'Soumik Sarkar'] | 1603.07839v1 | This paper proposes an end-to-end convolutional selective autoencoder
approach for early detection of combustion instabilities using rapidly arriving
flame image frames. The instabilities arising in combustion processes cause
significant deterioration and safety issues in various human-engineered systems
such as land a... | Early Detection of Combustion Instabilities using Deep Convolutional
Selective Autoencoders on Hi-speed Flame Video | 2,016 | http://arxiv.org/pdf/1603.07839v1 | Title Early Detection Combustion Instabilities using Deep Convolutional Selective Autoencoders Hispeed Flame Video Summary paper proposes endtoend convolutional selective autoencoder approach early detection combustion instability using rapidly arriving flame image frame instability arising combustion process cause sig... | [-0.06691711395978928, 0.05354198068380356, -0.009279065765440464, 0.06643844395875931, 0.024544352665543556, 0.02912520430982113, 0.00240775546990335, 0.05960454046726227, -0.05433112382888794, -0.039916910231113434, 0.04682253673672676, -0.009627824649214745, -0.0168723426759243, 0.06770304590463638, 0.01484145317226... |
1,232 | 1,232 | ['Markus Thom', 'Günther Palm'] | 1603.08367v1 | Sparseness is a useful regularizer for learning in a wide range of
applications, in particular in neural networks. This paper proposes a model
targeted at classification tasks, where sparse activity and sparse connectivity
are used to enhance classification capabilities. The tool for achieving this is
a sparseness-enfo... | Sparse Activity and Sparse Connectivity in Supervised Learning | 2,016 | http://arxiv.org/pdf/1603.08367v1 | Title Sparse Activity Sparse Connectivity Supervised Learning Summary Sparseness useful regularizer learning wide range application particular neural network paper proposes model targeted classification task sparse activity sparse connectivity used enhance classification capability tool achieving sparsenessenforcing pr... | [-0.03592899069190025, 0.022208554670214653, -0.015088890679180622, 0.039058420807123184, 0.04725993424654007, -0.03798478841781616, 0.03636178746819496, 0.021143300458788872, -0.05912933126091957, -0.01127125695347786, -0.010733835399150848, 0.01797039993107319, 0.03514299914240837, 0.02981664054095745, 0.041079726070... |
1,233 | 1,233 | ['Gao Huang', 'Yu Sun', 'Zhuang Liu', 'Daniel Sedra', 'Kilian Weinberger'] | 1603.09382v3 | Very deep convolutional networks with hundreds of layers have led to
significant reductions in error on competitive benchmarks. Although the
unmatched expressiveness of the many layers can be highly desirable at test
time, training very deep networks comes with its own set of challenges. The
gradients can vanish, the f... | Deep Networks with Stochastic Depth | 2,016 | http://arxiv.org/pdf/1603.09382v3 | Title Deep Networks Stochastic Depth Summary deep convolutional network hundred layer led significant reduction error competitive benchmark Although unmatched expressiveness many layer highly desirable test time training deep network come set challenge gradient vanish forward flow often diminishes training time painful... | [-0.010279844515025616, 0.04579173028469086, -0.004896120633929968, 0.05674741044640541, -0.0073264408856630325, -0.0472818948328495, 0.042658377438783646, -0.016029395163059235, -0.04169977083802223, 0.014062102884054184, 0.026848286390304565, 0.008819473907351494, 0.0012567774392664433, 0.07068894803524017, -0.000841... |
1,234 | 1,234 | ['Shuai Zheng', 'Abhinav Vishnu', 'Chris Ding'] | 1605.01369v2 | Deep Learning is a very powerful machine learning model. Deep Learning trains
a large number of parameters for multiple layers and is very slow when data is
in large scale and the architecture size is large. Inspired from the shrinking
technique used in accelerating computation of Support Vector Machines (SVM)
algorith... | Accelerating Deep Learning with Shrinkage and Recall | 2,016 | http://arxiv.org/pdf/1605.01369v2 | Title Accelerating Deep Learning Shrinkage Recall Summary Deep Learning powerful machine learning model Deep Learning train large number parameter multiple layer slow data large scale architecture size large Inspired shrinking technique used accelerating computation Support Vector Machines SVM algorithm screening techn... | [-0.018599096685647964, 0.028540238738059998, -0.02879328466951847, 0.039566945284605026, -0.007782716769725084, -0.028863022103905678, 0.01361249852925539, 0.012104012072086334, 0.01318956259638071, 0.0012450627982616425, -0.019846074283123016, -0.0011515641817823052, 0.010489356704056263, 0.03564151003956795, -0.0091... |
1,235 | 1,235 | ['Philipp Gysel'] | 1605.06402v1 | Convolutional neural networks (CNN) have achieved major breakthroughs in
recent years. Their performance in computer vision have matched and in some
areas even surpassed human capabilities. Deep neural networks can capture
complex non-linear features; however this ability comes at the cost of high
computational and mem... | Ristretto: Hardware-Oriented Approximation of Convolutional Neural
Networks | 2,016 | http://arxiv.org/pdf/1605.06402v1 | Title Ristretto HardwareOriented Approximation Convolutional Neural Networks Summary Convolutional neural network CNN achieved major breakthrough recent year performance computer vision matched area even surpassed human capability Deep neural network capture complex nonlinear feature however ability come cost high comp... | [-0.02497262880206108, 0.06187088042497635, -0.027119766920804977, 0.06563989073038101, -0.00484851049259305, -0.024940812960267067, 0.07125523686408997, -0.009898168966174126, -0.04892946407198906, 0.005995097570121288, 0.00806562602519989, 0.0040030754171311855, 0.011462477967143059, 0.03946921229362488, 0.0260490756... |
1,236 | 1,236 | ['Milad Mohammadi', 'Subhasis Das'] | 1605.08512v1 | It has been proven that transfer learning provides an easy way to achieve
state-of-the-art accuracies on several vision tasks by training a simple
classifier on top of features obtained from pre-trained neural networks. The
goal of this work is to generate better features for transfer learning from
multiple publicly av... | SNN: Stacked Neural Networks | 2,016 | http://arxiv.org/pdf/1605.08512v1 | Title SNN Stacked Neural Networks Summary proven transfer learning provides easy way achieve stateoftheart accuracy several vision task training simple classifier top feature obtained pretrained neural network goal work generate better feature transfer learning multiple publicly available pretrained neural network end ... | [-0.04533090814948082, 0.03276192024350166, -0.00010578485671430826, 0.04264337942004204, 0.014198139309883118, -0.007219626568257809, 0.052011869847774506, -0.02789374627172947, -0.04731709882616997, -0.00361702055670321, -0.0900450050830841, 0.046305689960718155, 0.04552968218922615, 0.035725757479667664, 0.019604390... |
1,237 | 1,237 | ['Tim Salimans', 'Ian Goodfellow', 'Wojciech Zaremba', 'Vicki Cheung', 'Alec Radford', 'Xi Chen'] | 1606.03498v1 | We present a variety of new architectural features and training procedures
that we apply to the generative adversarial networks (GANs) framework. We focus
on two applications of GANs: semi-supervised learning, and the generation of
images that humans find visually realistic. Unlike most work on generative
models, our p... | Improved Techniques for Training GANs | 2,016 | http://arxiv.org/pdf/1606.03498v1 | Title Improved Techniques Training GANs Summary present variety new architectural feature training procedure apply generative adversarial network GANs framework focus two application GANs semisupervised learning generation image human find visually realistic Unlike work generative model primary goal train model assigns... | [0.00790507160127163, 0.07754165679216385, -0.006464189384132624, 0.045290105044841766, -0.00022948422702029347, -0.004433211870491505, 0.028524931520223618, -0.0028470002580434084, -0.006867441348731518, 0.04633037745952606, -0.04948092997074127, 0.025544341653585434, -0.05395067483186722, 0.027009515091776848, 0.0715... |
1,238 | 1,238 | ['Kun He', 'Yan Wang', 'John Hopcroft'] | 1606.04801v2 | To what extent is the success of deep visualization due to the training?
Could we do deep visualization using untrained, random weight networks? To
address this issue, we explore new and powerful generative models for three
popular deep visualization tasks using untrained, random weight convolutional
neural networks. F... | A Powerful Generative Model Using Random Weights for the Deep Image
Representation | 2,016 | http://arxiv.org/pdf/1606.04801v2 | Title Powerful Generative Model Using Random Weights Deep Image Representation Summary extent success deep visualization due training Could deep visualization using untrained random weight network address issue explore new powerful generative model three popular deep visualization task using untrained random weight con... | [-0.002128409221768379, 0.06832403689622879, -0.02799643576145172, 0.03999074921011925, -0.003075722139328718, 0.008872193284332752, 0.022194141522049904, -0.02055860497057438, -0.029334917664527893, 0.04254429414868355, -0.021199896931648254, 0.02316487953066826, -0.02739294059574604, 0.02755904011428356, 0.0155478017... |
1,239 | 1,239 | ['Hengyuan Hu', 'Rui Peng', 'Yu-Wing Tai', 'Chi-Keung Tang'] | 1607.03250v1 | State-of-the-art neural networks are getting deeper and wider. While their
performance increases with the increasing number of layers and neurons, it is
crucial to design an efficient deep architecture in order to reduce
computational and memory costs. Designing an efficient neural network, however,
is labor intensive ... | Network Trimming: A Data-Driven Neuron Pruning Approach towards
Efficient Deep Architectures | 2,016 | http://arxiv.org/pdf/1607.03250v1 | Title Network Trimming DataDriven Neuron Pruning Approach towards Efficient Deep Architectures Summary Stateoftheart neural network getting deeper wider performance increase increasing number layer neuron crucial design efficient deep architecture order reduce computational memory cost Designing efficient neural networ... | [-0.01882012188434601, 0.04524293541908264, -0.00882603507488966, 0.06347029656171799, 0.0020454837940633297, -0.04534580186009407, 0.025939280167222023, 0.021990377455949783, -0.06560501456260681, 0.04826058819890022, -0.02478449046611786, 0.024757875129580498, -0.010864495299756527, 0.05403487756848335, 0.03348536789... |
1,240 | 1,240 | ['Christian Bailer', 'Kiran Varanasi', 'Didier Stricker'] | 1607.08064v3 | Learning based approaches have not yet achieved their full potential in
optical flow estimation, where their performance still trails heuristic
approaches. In this paper, we present a CNN based patch matching approach for
optical flow estimation. An important contribution of our approach is a novel
thresholded loss for... | CNN-based Patch Matching for Optical Flow with Thresholded Hinge
Embedding Loss | 2,016 | http://arxiv.org/pdf/1607.08064v3 | Title CNNbased Patch Matching Optical Flow Thresholded Hinge Embedding Loss Summary Learning based approach yet achieved full potential optical flow estimation performance still trail heuristic approach paper present CNN based patch matching approach optical flow estimation important contribution approach novel thresho... | [-0.048210423439741135, 0.038108449429273605, 0.009967567399144173, 0.07074859738349915, -0.05375124514102936, -0.05146218091249466, 0.0552767850458622, 0.03851082921028137, -0.07135859876871109, -0.005664671305567026, 0.03291254863142967, 0.040145501494407654, 0.03029008023440838, 0.08703414350748062, 0.00930542778223... |
1,241 | 1,241 | ['Priyadarshini Panda', 'Kaushik Roy'] | 1608.00611v1 | One of the key challenges in machine learning is to design a computationally
efficient multi-class classifier while maintaining the output accuracy and
performance. In this paper, we present a tree-based classifier: Attention Tree
(ATree) for large-scale image classification that uses recursive Adaboost
training to con... | Attention Tree: Learning Hierarchies of Visual Features for Large-Scale
Image Recognition | 2,016 | http://arxiv.org/pdf/1608.00611v1 | Title Attention Tree Learning Hierarchies Visual Features LargeScale Image Recognition Summary One key challenge machine learning design computationally efficient multiclass classifier maintaining output accuracy performance paper present treebased classifier Attention Tree ATree largescale image classification us recu... | [0.0018988262163475156, 0.0030219191685318947, -0.02189488150179386, 0.05939989537000656, 0.030536331236362457, 0.019668323919177055, 0.013437477871775627, 0.014852151274681091, 0.0002752818400040269, -0.05663195252418518, -0.03965822979807854, -0.003996850922703743, 0.025973834097385406, 0.020865997299551964, 0.006839... |
1,242 | 1,242 | ['Martin Simonovsky', 'Nikos Komodakis'] | 1608.02728v1 | The focus of our work is speeding up evaluation of deep neural networks in
retrieval scenarios, where conventional architectures may spend too much time
on negative examples. We propose to replace a monolithic network with our novel
cascade of feature-sharing deep classifiers, called OnionNet, where subsequent
stages m... | OnionNet: Sharing Features in Cascaded Deep Classifiers | 2,016 | http://arxiv.org/pdf/1608.02728v1 | Title OnionNet Sharing Features Cascaded Deep Classifiers Summary focus work speeding evaluation deep neural network retrieval scenario conventional architecture may spend much time negative example propose replace monolithic network novel cascade featuresharing deep classifier called OnionNet subsequent stage may add ... | [-0.025480329990386963, 0.08324232697486877, -0.001398551627062261, 0.07479862868785858, -0.045973315834999084, -0.012426000088453293, 0.07170754671096802, 0.017373129725456238, 0.013058639131486416, -0.009118038229644299, -0.02865036576986313, 0.038936637341976166, -0.05493044853210449, 0.06826525181531906, -0.0517177... |
1,243 | 1,243 | ['Yiwen Guo', 'Anbang Yao', 'Yurong Chen'] | 1608.04493v2 | Deep learning has become a ubiquitous technology to improve machine
intelligence. However, most of the existing deep models are structurally very
complex, making them difficult to be deployed on the mobile platforms with
limited computational power. In this paper, we propose a novel network
compression method called dy... | Dynamic Network Surgery for Efficient DNNs | 2,016 | http://arxiv.org/pdf/1608.04493v2 | Title Dynamic Network Surgery Efficient DNNs Summary Deep learning become ubiquitous technology improve machine intelligence However existing deep model structurally complex making difficult deployed mobile platform limited computational power paper propose novel network compression method called dynamic network surger... | [-0.006695954594761133, 0.04581962525844574, -0.0478852316737175, -0.010604342445731163, -0.014581057243049145, -0.030758507549762726, 0.0069971345365047455, -0.007448140066117048, -0.08795372396707535, 0.019251277670264244, 0.02410922572016716, 0.013347893953323364, 0.05058887600898743, 0.07207558304071426, 0.00647721... |
1,244 | 1,244 | ['Martin Simonovsky', 'Benjamín Gutiérrez-Becker', 'Diana Mateus', 'Nassir Navab', 'Nikos Komodakis'] | 1609.05396v1 | Multimodal registration is a challenging problem in medical imaging due the
high variability of tissue appearance under different imaging modalities. The
crucial component here is the choice of the right similarity measure. We make a
step towards a general learning-based solution that can be adapted to specific
situati... | A Deep Metric for Multimodal Registration | 2,016 | http://arxiv.org/pdf/1609.05396v1 | Title Deep Metric Multimodal Registration Summary Multimodal registration challenging problem medical imaging due high variability tissue appearance different imaging modality crucial component choice right similarity measure make step towards general learningbased solution adapted specific situation present metric bas... | [-0.0062970793806016445, 0.026432298123836517, -0.005144237540662289, -0.009815048426389694, -0.020571034401655197, 0.012395408004522324, 0.035873934626579285, 0.025956571102142334, -0.028962887823581696, 0.01889321208000183, -0.024078000336885452, -0.10039685666561127, 0.026016797870397568, 0.04106814041733742, 0.0533... |
1,245 | 1,245 | ['Rasool Fakoor', 'Abdel-rahman Mohamed', 'Margaret Mitchell', 'Sing Bing Kang', 'Pushmeet Kohli'] | 1611.02261v4 | We present a method to improve video description generation by modeling
higher-order interactions between video frames and described concepts. By
storing past visual attention in the video associated to previously generated
words, the system is able to decide what to look at and describe in light of
what it has already... | Memory-augmented Attention Modelling for Videos | 2,016 | http://arxiv.org/pdf/1611.02261v4 | Title Memoryaugmented Attention Modelling Videos Summary present method improve video description generation modeling higherorder interaction video frame described concept storing past visual attention video associated previously generated word system able decide look describe light already looked described enables eff... | [0.0396299734711647, -0.007141186390072107, -0.00021379107784014195, 0.04924452304840088, 0.017194844782352448, -0.0006667841225862503, 0.01020939089357853, -0.012556235305964947, -0.03456626832485199, -0.0841597467660904, 0.0023134704679250717, -0.08068519830703735, 0.023881971836090088, 0.06138698756694794, 0.0318479... |
1,246 | 1,246 | ['Jason Kuen', 'Xiangfei Kong', 'Gang Wang', 'Yap-Peng Tan'] | 1611.05552v5 | Deluge Networks (DelugeNets) are deep neural networks which efficiently
facilitate massive cross-layer information inflows from preceding layers to
succeeding layers. The connections between layers in DelugeNets are established
through cross-layer depthwise convolutional layers with learnable filters,
acting as a flexi... | DelugeNets: Deep Networks with Efficient and Flexible Cross-layer
Information Inflows | 2,016 | http://arxiv.org/pdf/1611.05552v5 | Title DelugeNets Deep Networks Efficient Flexible Crosslayer Information Inflows Summary Deluge Networks DelugeNets deep neural network efficiently facilitate massive crosslayer information inflow preceding layer succeeding layer connection layer DelugeNets established crosslayer depthwise convolutional layer learnable... | [-0.048334017395973206, 0.02082235738635063, -0.023733941838145256, 0.015828952193260193, -0.018368760123848915, -0.017619770020246506, 0.07667647302150726, -0.018047496676445007, -0.050517670810222626, 0.020544596016407013, -0.020787661895155907, 0.017004692927002907, -0.023921053856611252, 0.06855510175228119, 0.0013... |
1,247 | 1,247 | ['Jose M Alvarez', 'Mathieu Salzmann'] | 1611.06321v2 | Nowadays, the number of layers and of neurons in each layer of a deep network
are typically set manually. While very deep and wide networks have proven
effective in general, they come at a high memory and computation cost, thus
making them impractical for constrained platforms. These networks, however, are
known to hav... | Learning the Number of Neurons in Deep Networks | 2,016 | http://arxiv.org/pdf/1611.06321v2 | Title Learning Number Neurons Deep Networks Summary Nowadays number layer neuron layer deep network typically set manually deep wide network proven effective general come high memory computation cost thus making impractical constrained platform network however known many redundant parameter could thus principle replace... | [-0.0319480262696743, 0.03335307538509369, -0.017721883952617645, 0.05101701244711876, 0.03260786086320877, -0.03915951028466225, 0.03651270642876625, -0.04963458329439163, -0.02142641879618168, 0.030403725802898407, 0.0038869809359312057, 0.015116853639483452, -3.600350100896321e-05, 0.007221557199954987, 0.0531693510... |
1,248 | 1,248 | ['Tsung-Wei Ke', 'Michael Maire', 'Stella X. Yu'] | 1611.07661v2 | We propose a multigrid extension of convolutional neural networks (CNNs).
Rather than manipulating representations living on a single spatial grid, our
network layers operate across scale space, on a pyramid of grids. They consume
multigrid inputs and produce multigrid outputs; convolutional filters
themselves have bot... | Multigrid Neural Architectures | 2,016 | http://arxiv.org/pdf/1611.07661v2 | Title Multigrid Neural Architectures Summary propose multigrid extension convolutional neural network CNNs Rather manipulating representation living single spatial grid network layer operate across scale space pyramid grid consume multigrid input produce multigrid output convolutional filter withinscale crossscale exte... | [0.02347070351243019, 0.040291331708431244, -0.02252259850502014, 0.06388084590435028, -0.0015876631950959563, 0.018364202231168747, 0.1017490029335022, -0.04520164057612419, 0.004124898929148912, 0.034628696739673615, -0.03358730301260948, 0.0078040133230388165, -0.015620511956512928, 0.052562206983566284, 0.068220809... |
1,249 | 1,249 | ['Yanghao Li', 'Naiyan Wang', 'Jiaying Liu', 'Xiaodi Hou'] | 1701.01036v2 | Neural Style Transfer has recently demonstrated very exciting results which
catches eyes in both academia and industry. Despite the amazing results, the
principle of neural style transfer, especially why the Gram matrices could
represent style remains unclear. In this paper, we propose a novel
interpretation of neural ... | Demystifying Neural Style Transfer | 2,017 | http://arxiv.org/pdf/1701.01036v2 | Title Demystifying Neural Style Transfer Summary Neural Style Transfer recently demonstrated exciting result catch eye academia industry Despite amazing result principle neural style transfer especially Gram matrix could represent style remains unclear paper propose novel interpretation neural style transfer treating d... | [0.0018681659130379558, 0.041154734790325165, -0.019428160041570663, 0.03537003695964813, -0.016766520217061043, 0.005738838575780392, -0.0003907916834577918, 0.0451369434595108, -0.08303647488355637, -0.0006824033916927874, -0.05712631344795227, -0.017913484945893288, 0.05085799843072891, 0.02553568407893181, 0.055940... |
1,250 | 1,250 | ['Volodymyr Turchenko', 'Eric Chalmers', 'Artur Luczak'] | 1701.04949v1 | This paper presents the development of several models of a deep convolutional
auto-encoder in the Caffe deep learning framework and their experimental
evaluation on the example of MNIST dataset. We have created five models of a
convolutional auto-encoder which differ architecturally by the presence or
absence of poolin... | A Deep Convolutional Auto-Encoder with Pooling - Unpooling Layers in
Caffe | 2,017 | http://arxiv.org/pdf/1701.04949v1 | Title Deep Convolutional AutoEncoder Pooling Unpooling Layers Caffe Summary paper present development several model deep convolutional autoencoder Caffe deep learning framework experimental evaluation example MNIST dataset created five model convolutional autoencoder differ architecturally presence absence pooling unpo... | [-0.026916606351733208, 0.016761701554059982, -0.02285112999379635, 0.0665423721075058, 0.026855766773223877, -0.0014077763771638274, 0.06978847831487656, -0.018267475068569183, -0.07678340375423431, 0.010368173941969872, -0.018519587814807892, 0.04988749697804451, -0.004826694261282682, 0.04490241780877113, -0.0072960... |
1,251 | 1,251 | ['Mete Ozay', 'Takayuki Okatani'] | 1701.06123v2 | Recent advances in optimization methods used for training convolutional
neural networks (CNNs) with kernels, which are normalized according to
particular constraints, have shown remarkable success. This work introduces an
approach for training CNNs using ensembles of joint spaces of kernels
constructed using different ... | Optimization on Product Submanifolds of Convolution Kernels | 2,017 | http://arxiv.org/pdf/1701.06123v2 | Title Optimization Product Submanifolds Convolution Kernels Summary Recent advance optimization method used training convolutional neural network CNNs kernel normalized according particular constraint shown remarkable success work introduces approach training CNNs using ensemble joint space kernel constructed using dif... | [0.0029807635582983494, -0.002941632643342018, 0.0029629445634782314, 0.09010506421327591, 0.011009071953594685, 0.010367858223617077, 0.023977885022759438, -0.019275924190878868, -0.0011275589931756258, 0.03760818764567375, -0.02370557188987732, -0.019632741808891296, -0.010397171601653099, 0.021992480382323265, 0.047... |
1,252 | 1,252 | ['Di Xie', 'Jiang Xiong', 'Shiliang Pu'] | 1703.01827v3 | Deep neural network is difficult to train and this predicament becomes worse
as the depth increases. The essence of this problem exists in the magnitude of
backpropagated errors that will result in gradient vanishing or exploding
phenomenon. We show that a variant of regularizer which utilizes orthonormality
among diff... | All You Need is Beyond a Good Init: Exploring Better Solution for
Training Extremely Deep Convolutional Neural Networks with Orthonormality and
Modulation | 2,017 | http://arxiv.org/pdf/1703.01827v3 | Title Need Beyond Good Init Exploring Better Solution Training Extremely Deep Convolutional Neural Networks Orthonormality Modulation Summary Deep neural network difficult train predicament becomes worse depth increase essence problem exists magnitude backpropagated error result gradient vanishing exploding phenomenon ... | [-0.0031870584934949875, 0.08209974318742752, -0.0010925524402409792, 0.06085912510752678, 0.05100148171186447, -0.011209608986973763, 0.012526903301477432, 0.0075141736306250095, -0.06407284736633301, 0.03482397645711899, -0.06920332461595535, 0.0718940794467926, -0.011172013357281685, -0.006591996643692255, 0.0229009... |
1,253 | 1,253 | ['Tharindu Fernando', 'Simon Denman', 'Aaron McFadyen', 'Sridha Sridharan', 'Clinton Fookes'] | 1703.04706v1 | In the domain of sequence modelling, Recurrent Neural Networks (RNN) have
been capable of achieving impressive results in a variety of application areas
including visual question answering, part-of-speech tagging and machine
translation. However this success in modelling short term dependencies has not
successfully tra... | Tree Memory Networks for Modelling Long-term Temporal Dependencies | 2,017 | http://arxiv.org/pdf/1703.04706v1 | Title Tree Memory Networks Modelling Longterm Temporal Dependencies Summary domain sequence modelling Recurrent Neural Networks RNN capable achieving impressive result variety application area including visual question answering partofspeech tagging machine translation However success modelling short term dependency su... | [0.0025645678397268057, 0.041818492114543915, -0.014846110716462135, 0.032753944396972656, -0.03767929598689079, -0.0027936280239373446, -0.008567067794501781, -0.032484278082847595, -0.0760747417807579, -0.03437585011124611, 0.034454211592674255, -0.06219428405165672, 0.029815437272191048, 0.06776227056980133, -0.0109... |
1,254 | 1,254 | ['Timo Hackel', 'Nikolay Savinov', 'Lubor Ladicky', 'Jan D. Wegner', 'Konrad Schindler', 'Marc Pollefeys'] | 1704.03847v1 | This paper presents a new 3D point cloud classification benchmark data set
with over four billion manually labelled points, meant as input for data-hungry
(deep) learning methods. We also discuss first submissions to the benchmark
that use deep convolutional neural networks (CNNs) as a work horse, which
already show re... | Semantic3D.net: A new Large-scale Point Cloud Classification Benchmark | 2,017 | http://arxiv.org/pdf/1704.03847v1 | Title Semantic3Dnet new Largescale Point Cloud Classification Benchmark Summary paper present new 3D point cloud classification benchmark data set four billion manually labelled point meant input datahungry deep learning method also discus first submission benchmark use deep convolutional neural network CNNs work horse... | [-0.02490987256169319, 0.012011982500553131, 0.036000609397888184, 0.07820869982242584, 0.02205965854227543, 0.025714172050356865, 0.03594175726175308, -0.05654573440551758, -0.06215629354119301, 0.011109132319688797, -0.02479170821607113, 0.07157041132450104, 0.009195130318403244, 0.04298366233706474, 0.02099038474261... |
1,255 | 1,255 | ['Justin Lazarow', 'Long Jin', 'Zhuowen Tu'] | 1704.07820v1 | We study unsupervised learning by developing introspective generative
modeling (IGM) that attains a generator using progressively learned deep
convolutional neural networks. The generator is itself a discriminator, capable
of introspection: being able to self-evaluate the difference between its
generated samples and th... | Introspective Generative Modeling: Decide Discriminatively | 2,017 | http://arxiv.org/pdf/1704.07820v1 | Title Introspective Generative Modeling Decide Discriminatively Summary study unsupervised learning developing introspective generative modeling IGM attains generator using progressively learned deep convolutional neural network generator discriminator capable introspection able selfevaluate difference generated sample... | [0.019453367218375206, 0.07172452658414841, -0.02202647365629673, 0.07391197979450226, -0.01537074614316225, 0.008400761522352695, 0.05157385393977165, -0.02893260307610035, -0.01746879145503044, 0.027252964675426483, -0.0040261270478367805, 0.011639992706477642, -0.04287824407219887, 0.013733461499214172, 0.0148847941... |
1,256 | 1,256 | ['Fabrizio Pedersoli', 'George Tzanetakis', 'Andrea Tagliasacchi'] | 1705.07175v2 | There are many applications scenarios for which the computational performance
and memory footprint of the prediction phase of Deep Neural Networks (DNNs)
needs to be optimized. Binary Neural Networks (BDNNs) have been shown to be an
effective way of achieving this objective. In this paper, we show how
Convolutional Neu... | Espresso: Efficient Forward Propagation for BCNNs | 2,017 | http://arxiv.org/pdf/1705.07175v2 | Title Espresso Efficient Forward Propagation BCNNs Summary many application scenario computational performance memory footprint prediction phase Deep Neural Networks DNNs need optimized Binary Neural Networks BDNNs shown effective way achieving objective paper show Convolutional Neural Networks CNNs implemented using b... | [-0.016200853511691093, 0.028504690155386925, -0.018677322193980217, 0.04408161714673042, -0.027021383866667747, -0.002883989829570055, 0.07190146297216415, 0.003577472409233451, -0.028449367731809616, -0.0029134268406778574, 0.03664013743400574, 0.003072368912398815, -0.004900366999208927, 0.08810308575630188, 0.00768... |
1,257 | 1,257 | ['Haojin Yang', 'Martin Fritzsche', 'Christian Bartz', 'Christoph Meinel'] | 1705.09864v1 | Binary Neural Networks (BNNs) can drastically reduce memory size and accesses
by applying bit-wise operations instead of standard arithmetic operations.
Therefore it could significantly improve the efficiency and lower the energy
consumption at runtime, which enables the application of state-of-the-art deep
learning mo... | BMXNet: An Open-Source Binary Neural Network Implementation Based on
MXNet | 2,017 | http://arxiv.org/pdf/1705.09864v1 | Title BMXNet OpenSource Binary Neural Network Implementation Based MXNet Summary Binary Neural Networks BNNs drastically reduce memory size access applying bitwise operation instead standard arithmetic operation Therefore could significantly improve efficiency lower energy consumption runtime enables application stateo... | [-0.05149267986416817, 0.04199634864926338, -0.01881209760904312, 0.05497327446937561, -0.01935211569070816, -0.028668515384197235, 0.07538139820098877, 0.010077118873596191, -0.05978301167488098, 0.00014186944463290274, -0.007982340641319752, 0.005640218034386635, -0.016821784898638725, 0.07750771939754486, 0.01979602... |
1,258 | 1,258 | ['Jean-Charles Vialatte', 'Vincent Gripon', 'Gilles Coppin'] | 1706.02684v3 | We propose a simple and generic layer formulation that extends the properties
of convolutional layers to any domain that can be described by a graph. Namely,
we use the support of its adjacency matrix to design learnable weight sharing
filters able to exploit the underlying structure of signals in the same fashion
as f... | Learning Local Receptive Fields and their Weight Sharing Scheme on
Graphs | 2,017 | http://arxiv.org/pdf/1706.02684v3 | Title Learning Local Receptive Fields Weight Sharing Scheme Graphs Summary propose simple generic layer formulation extends property convolutional layer domain described graph Namely use support adjacency matrix design learnable weight sharing filter able exploit underlying structure signal fashion image proposed formu... | [0.030468454584479332, 0.0005635989364236593, 0.011782009154558182, 0.06126868352293968, -0.01075859647244215, -0.052926141768693924, 0.05323818698525429, 0.034196887165308, 0.06526892632246017, 0.048263028264045715, -0.019077030941843987, 0.0367056280374527, -0.02867376059293747, 0.06673018634319305, 0.010753354988992... |
1,259 | 1,259 | ['Yuntao Chen', 'Naiyan Wang', 'Zhaoxiang Zhang'] | 1707.01220v2 | We have witnessed rapid evolution of deep neural network architecture design
in the past years. These latest progresses greatly facilitate the developments
in various areas such as computer vision and natural language processing.
However, along with the extraordinary performance, these state-of-the-art
models also brin... | DarkRank: Accelerating Deep Metric Learning via Cross Sample
Similarities Transfer | 2,017 | http://arxiv.org/pdf/1707.01220v2 | Title DarkRank Accelerating Deep Metric Learning via Cross Sample Similarities Transfer Summary witnessed rapid evolution deep neural network architecture design past year latest progress greatly facilitate development various area computer vision natural language processing However along extraordinary performance stat... | [0.002645194763317704, 0.021841926500201225, -0.015017774887382984, 0.06543312966823578, -0.0023301439359784126, 0.025294283404946327, 0.007461649365723133, 0.029021771624684334, 0.002284357091411948, -0.024278344586491585, -0.034518204629421234, -0.011612228117883205, -0.0004274074744898826, -0.013436558656394482, 0.0... |
1,260 | 1,260 | ['Surat Teerapittayanon', 'Bradley McDanel', 'H. T. Kung'] | 1709.01686v1 | Deep neural networks are state of the art methods for many learning tasks due
to their ability to extract increasingly better features at each network layer.
However, the improved performance of additional layers in a deep network comes
at the cost of added latency and energy usage in feedforward inference. As
networks... | BranchyNet: Fast Inference via Early Exiting from Deep Neural Networks | 2,017 | http://arxiv.org/pdf/1709.01686v1 | Title BranchyNet Fast Inference via Early Exiting Deep Neural Networks Summary Deep neural network state art method many learning task due ability extract increasingly better feature network layer However improved performance additional layer deep network come cost added latency energy usage feedforward inference netwo... | [-0.014560464769601822, -0.004123300313949585, -0.021990522742271423, 0.01897454634308815, 0.010444418527185917, -0.006409340538084507, 0.03740128129720688, -0.004397416952997446, -0.015668360516428947, 0.033885929733514786, -0.002063640160486102, 0.04671047255396843, 0.012329970486462116, 0.040317635983228683, -0.0013... |
1,261 | 1,261 | ['Lars Hertel', 'Erhardt Barth', 'Thomas Käster', 'Thomas Martinetz'] | 1710.02286v1 | Recognizing objects in natural images is an intricate problem involving
multiple conflicting objectives. Deep convolutional neural networks, trained on
large datasets, achieve convincing results and are currently the
state-of-the-art approach for this task. However, the long time needed to train
such deep networks is a... | Deep Convolutional Neural Networks as Generic Feature Extractors | 2,017 | http://arxiv.org/pdf/1710.02286v1 | Title Deep Convolutional Neural Networks Generic Feature Extractors Summary Recognizing object natural image intricate problem involving multiple conflicting objective Deep convolutional neural network trained large datasets achieve convincing result currently stateoftheart approach task However long time needed train ... | [0.055276669561862946, 0.08531878143548965, 0.02434695139527321, 0.08176027983427048, -0.006701956037431955, -0.0053156595677137375, 0.03818340599536896, 0.00768188014626503, -0.03675878793001175, -0.020023290067911148, -0.05037746950984001, 0.020229918882250786, -0.0051667108200490475, 0.05279255658388138, 0.004422547... |
1,262 | 1,262 | ['Amir R. Zamir', 'Tilman Wekel', 'Pulkit Argrawal', 'Colin Weil', 'Jitendra Malik', 'Silvio Savarese'] | 1710.08247v1 | Though a large body of computer vision research has investigated developing
generic semantic representations, efforts towards developing a similar
representation for 3D has been limited. In this paper, we learn a generic 3D
representation through solving a set of foundational proxy 3D tasks:
object-centric camera pose ... | Generic 3D Representation via Pose Estimation and Matching | 2,017 | http://arxiv.org/pdf/1710.08247v1 | Title Generic 3D Representation via Pose Estimation Matching Summary Though large body computer vision research investigated developing generic semantic representation effort towards developing similar representation 3D limited paper learn generic 3D representation solving set foundational proxy 3D task objectcentric c... | [0.0035579355899244547, 0.008825414814054966, 0.04246417060494423, 0.07153753936290741, 0.018051359802484512, 0.026438191533088684, -0.0189497172832489, 0.0006842162692919374, -0.02422534115612507, -0.06660788506269455, -0.032758746296167374, 0.016573769971728325, 0.020808732137084007, 0.02202565036714077, 0.0511442534... |
1,263 | 1,263 | ['Wentao Zhu', 'Xiang Xiang', 'Trac D. Tran', 'Gregory D. Hager', 'Xiaohui Xie'] | 1710.09288v2 | Mass segmentation provides effective morphological features which are
important for mass diagnosis. In this work, we propose a novel end-to-end
network for mammographic mass segmentation which employs a fully convolutional
network (FCN) to model a potential function, followed by a CRF to perform
structured learning. Be... | Adversarial Deep Structured Nets for Mass Segmentation from Mammograms | 2,017 | http://arxiv.org/pdf/1710.09288v2 | Title Adversarial Deep Structured Nets Mass Segmentation Mammograms Summary Mass segmentation provides effective morphological feature important mass diagnosis work propose novel endtoend network mammographic mass segmentation employ fully convolutional network FCN model potential function followed CRF perform structur... | [0.005084969103336334, 0.0478687658905983, -0.023579174652695656, 0.0025526713579893112, 0.021077895537018776, -0.006411607842892408, 0.039174992591142654, -0.0019371509552001953, -0.02620006911456585, 0.08844754844903946, 0.017531489953398705, -0.013856551609933376, -0.042832471430301666, 0.0659298375248909, -0.006094... |
1,264 | 1,264 | ['Abram L. Friesen', 'Pedro Domingos'] | 1710.11573v2 | As neural networks grow deeper and wider, learning networks with
hard-threshold activations is becoming increasingly important, both for network
quantization, which can drastically reduce time and energy requirements, and
for creating large integrated systems of deep networks, which may have
non-differentiable componen... | Deep Learning as a Mixed Convex-Combinatorial Optimization Problem | 2,017 | http://arxiv.org/pdf/1710.11573v2 | Title Deep Learning Mixed ConvexCombinatorial Optimization Problem Summary neural network grow deeper wider learning network hardthreshold activation becoming increasingly important network quantization drastically reduce time energy requirement creating large integrated system deep network may nondifferentiable compon... | [-0.024745939299464226, 0.048497576266527176, -0.010489068925380707, 0.04372246935963631, 0.00037308523315005004, -0.03732449933886528, 0.02477993257343769, 0.010313551872968674, -0.03590583801269531, -0.007668009493499994, -0.01979215256869793, -0.025559019297361374, -0.00286197941750288, 0.035007186233997345, 0.01519... |
1,265 | 1,265 | ['Clemens Rosenbaum', 'Tim Klinger', 'Matthew Riemer'] | 1711.01239v2 | Multi-task learning (MTL) with neural networks leverages commonalities in
tasks to improve performance, but often suffers from task interference which
reduces the benefits of transfer. To address this issue we introduce the
routing network paradigm, a novel neural network and training algorithm. A
routing network is a ... | Routing Networks: Adaptive Selection of Non-linear Functions for
Multi-Task Learning | 2,017 | http://arxiv.org/pdf/1711.01239v2 | Title Routing Networks Adaptive Selection Nonlinear Functions MultiTask Learning Summary Multitask learning MTL neural network leverage commonality task improve performance often suffers task interference reduces benefit transfer address issue introduce routing network paradigm novel neural network training algorithm r... | [-0.03320518508553505, 0.018011756241321564, -0.039724837988615036, 0.0017999497940763831, -0.016830507665872574, -0.04531371220946312, 0.06524404138326645, -0.04493222385644913, -0.03968140855431557, -0.03363380953669548, -0.048899583518505096, 0.024483533576130867, -0.013164801523089409, 0.04224236309528351, 0.025904... |
1,266 | 1,266 | ['Asit Mishra', 'Debbie Marr'] | 1711.05852v1 | Deep learning networks have achieved state-of-the-art accuracies on computer
vision workloads like image classification and object detection. The performant
systems, however, typically involve big models with numerous parameters. Once
trained, a challenging aspect for such top performing models is deployment on
resourc... | Apprentice: Using Knowledge Distillation Techniques To Improve
Low-Precision Network Accuracy | 2,017 | http://arxiv.org/pdf/1711.05852v1 | Title Apprentice Using Knowledge Distillation Techniques Improve LowPrecision Network Accuracy Summary Deep learning network achieved stateoftheart accuracy computer vision workload like image classification object detection performant system however typically involve big model numerous parameter trained challenging as... | [-0.012383731082081795, 0.03359812870621681, -0.004361429251730442, 0.07009915262460709, 0.008439279161393642, -0.025960611179471016, 0.012676875106990337, 0.038575172424316406, -0.05150830000638962, 0.015350639820098877, -0.00023165768652688712, 0.022468218579888344, 0.016864120960235596, 0.035549335181713104, -0.0147... |
1,267 | 1,267 | ['Assaf Shocher', 'Nadav Cohen', 'Michal Irani'] | 1712.06087v1 | Deep Learning has led to a dramatic leap in Super-Resolution (SR) performance
in the past few years. However, being supervised, these SR methods are
restricted to specific training data, where the acquisition of the
low-resolution (LR) images from their high-resolution (HR) counterparts is
predetermined (e.g., bicubic ... | "Zero-Shot" Super-Resolution using Deep Internal Learning | 2,017 | http://arxiv.org/pdf/1712.06087v1 | Title ZeroShot SuperResolution using Deep Internal Learning Summary Deep Learning led dramatic leap SuperResolution SR performance past year However supervised SR method restricted specific training data acquisition lowresolution LR image highresolution HR counterpart predetermined eg bicubic downscaling without distra... | [-0.005256193690001965, 0.014045723713934422, 0.018430551514029503, 0.028767792508006096, 0.0009444840252399445, -0.048796772956848145, 0.02405684068799019, 0.03092619962990284, -0.05667911469936371, 0.018793640658259392, 0.025771131739020348, 0.04578088968992233, -0.002007536357268691, -0.017837952822446823, 0.0489461... |
1,268 | 1,268 | ['Brian Kenji Iwana', 'Seiichi Uchida'] | 1712.06530v2 | In this paper, we propose a method of improving Convolutional Neural Networks
(CNN) by determining the optimal alignment of weights and inputs using dynamic
programming. Conventional CNNs convolve learnable shared weights, or filters,
across the input data. The filters use a linear matching of weights to inputs
using a... | Dynamic Weight Alignment for Convolutional Neural Networks | 2,017 | http://arxiv.org/pdf/1712.06530v2 | Title Dynamic Weight Alignment Convolutional Neural Networks Summary paper propose method improving Convolutional Neural Networks CNN determining optimal alignment weight input using dynamic programming Conventional CNNs convolve learnable shared weight filter across input data filter use linear matching weight input u... | [-0.017075086012482643, 0.05346205458045006, -0.00879612471908331, 0.025621332228183746, 0.008343745023012161, 0.014775816351175308, 0.049178797751665115, 0.017555607482790947, -0.01668686419725418, 0.05007995665073395, 0.00695795426145196, -0.0374453030526638, 0.04209280386567116, 0.037956222891807556, 0.0169405825436... |
1,269 | 1,269 | ['Timothy Rozario', 'Troy Long', 'Mingli Chen', 'Weiguo Lu', 'Steve Jiang'] | 1801.00096v1 | Data cleaning consumes about 80% of the time spent on data analysis for
clinical research projects. This is a much bigger problem in the era of big
data and machine learning in the field of medicine where large volumes of data
are being generated. We report an initial effort towards automated patient data
cleaning usin... | Towards automated patient data cleaning using deep learning: A
feasibility study on the standardization of organ labeling | 2,017 | http://arxiv.org/pdf/1801.00096v1 | Title Towards automated patient data cleaning using deep learning feasibility study standardization organ labeling Summary Data cleaning consumes 80 time spent data analysis clinical research project much bigger problem era big data machine learning field medicine large volume data generated report initial effort towar... | [0.03695407509803772, 0.0910504013299942, -0.014825277961790562, -0.022756880149245262, 0.013462974689900875, 0.021876422688364983, 0.04678391292691231, -0.0018635698361322284, -0.023861782625317574, 0.0483638234436512, -0.0020824761595577, -0.05180240795016289, 0.013292522169649601, 0.07247072458267212, -0.03056285902... |
1,270 | 1,270 | ['Mohammad Javad Shafiee', 'Brendan Chwyl', 'Francis Li', 'Rongyan Chen', 'Michelle Karg', 'Christian Scharfenberger', 'Alexander Wong'] | 1801.05387v1 | The computational complexity of leveraging deep neural networks for
extracting deep feature representations is a significant barrier to its
widespread adoption, particularly for use in embedded devices. One particularly
promising strategy to addressing the complexity issue is the notion of
evolutionary synthesis of dee... | StressedNets: Efficient Feature Representations via Stress-induced
Evolutionary Synthesis of Deep Neural Networks | 2,018 | http://arxiv.org/pdf/1801.05387v1 | Title StressedNets Efficient Feature Representations via Stressinduced Evolutionary Synthesis Deep Neural Networks Summary computational complexity leveraging deep neural network extracting deep feature representation significant barrier widespread adoption particularly use embedded device One particularly promising st... | [-0.0016200004611164331, -0.006138931959867477, -0.05016482248902321, 0.05396905168890953, 0.020888006314635277, 0.01014507282525301, 0.049316197633743286, 0.01982908509671688, -0.015409830026328564, 0.010732641443610191, 0.0002856180944945663, 0.016164004802703857, -0.01144458819180727, -0.013783845119178295, 0.017129... |
1,271 | 1,271 | ['Wentao Zhu', 'Chaochun Liu', 'Wei Fan', 'Xiaohui Xie'] | 1801.09555v1 | In this work, we present a fully automated lung computed tomography (CT)
cancer diagnosis system, DeepLung. DeepLung consists of two components, nodule
detection (identifying the locations of candidate nodules) and classification
(classifying candidate nodules into benign or malignant). Considering the 3D
nature of lun... | DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule
Detection and Classification | 2,018 | http://arxiv.org/pdf/1801.09555v1 | Title DeepLung Deep 3D Dual Path Nets Automated Pulmonary Nodule Detection Classification Summary work present fully automated lung computed tomography CT cancer diagnosis system DeepLung DeepLung consists two component nodule detection identifying location candidate nodule classification classifying candidate nodule b... | [-0.022021811455488205, 0.00792370829731226, -0.03821546584367752, 0.014682930894196033, 0.00035765624488703907, 0.004730533808469772, 0.015938475728034973, 0.010848598554730415, -0.020664917305111885, -0.0013650734908878803, 0.006906967144459486, -0.012424222193658352, -0.011567771434783936, 0.03137766569852829, 0.022... |
1,272 | 1,272 | ['Yang Hu', 'Guihua Wen', 'Huiqiang Liao', 'Changjun Wang', 'Dan Dai', 'Zhiwen Yu', 'Jun Zhang'] | 1802.02203v2 | The tongue image is an important physical information of human, it is of
great importance to the diagnosis and treatment in clinical medicine. Herbal
prescriptions are simple, noninvasive and low side effects, and are widely
applied in China. Researches on automatic construction technology of herbal
prescription based ... | Automatic construction of Chinese herbal prescription from tongue image
via CNNs and auxiliary latent therapy topics | 2,018 | http://arxiv.org/pdf/1802.02203v2 | Title Automatic construction Chinese herbal prescription tongue image via CNNs auxiliary latent therapy topic Summary tongue image important physical information human great importance diagnosis treatment clinical medicine Herbal prescription simple noninvasive low side effect widely applied China Researches automatic ... | [0.06061870977282524, 0.032625459134578705, -0.01484221126884222, 0.001112616853788495, 0.010239568538963795, -0.001036180998198688, 0.03783392161130905, 0.037268463522195816, -0.020599544048309326, 0.011865121312439442, 0.015518378466367722, -0.01854405365884304, 0.042677294462919235, 0.04356662184000015, 0.0364194624... |
1,273 | 1,273 | ['Yoojin Choi', 'Mostafa El-Khamy', 'Jungwon Lee'] | 1802.02271v1 | Compression of deep neural networks (DNNs) for memory- and
computation-efficient compact feature representations becomes a critical
problem particularly for deployment of DNNs on resource-limited platforms. In
this paper, we investigate lossy compression of DNNs by weight quantization and
lossless source coding for mem... | Universal Deep Neural Network Compression | 2,018 | http://arxiv.org/pdf/1802.02271v1 | Title Universal Deep Neural Network Compression Summary Compression deep neural network DNNs memory computationefficient compact feature representation becomes critical problem particularly deployment DNNs resourcelimited platform paper investigate lossy compression DNNs weight quantization lossless source coding memor... | [-0.05824408307671547, 0.049109093844890594, -0.005034924950450659, 0.03527325764298439, -0.008865266107022762, -0.005506171379238367, 0.039409030228853226, 0.0197879858314991, -0.08290362358093262, 0.03960421308875084, 0.010879616253077984, 0.011784383095800877, 0.016462275758385658, -0.005858412478119135, 0.011204366... |
1,274 | 1,274 | ['Emanuele Sansone', 'Quoc-Tin Phan', 'Francesco G. B. De Natale'] | 1802.03505v1 | Estimating the true density in high-dimensional feature spaces is a
well-known problem in machine learning. This work shows that it is possible to
formulate the optimization problem as a minimization and use the
representational power of neural networks to learn very complex densities. A
theoretical bound on the estima... | Plummer Autoencoders | 2,018 | http://arxiv.org/pdf/1802.03505v1 | Title Plummer Autoencoders Summary Estimating true density highdimensional feature space wellknown problem machine learning work show possible formulate optimization problem minimization use representational power neural network learn complex density theoretical bound estimation error given dealing finite number sample... | [-0.01979243941605091, 0.07196786999702454, -0.03691814839839935, 0.013316767290234566, 0.0035808829125016928, -0.026110144332051277, 0.04829821735620499, 0.0013687587343156338, -0.05493529513478279, 0.038091544061899185, 0.020084815099835396, -0.008838819339871407, 0.0027800099924206734, 0.07562094926834106, 0.0615407... |
1,275 | 1,275 | ['Jeff Zhang', 'Tianyu Gu', 'Kanad Basu', 'Siddharth Garg'] | 1802.04657v2 | Due to their growing popularity and computational cost, deep neural networks
(DNNs) are being targeted for hardware acceleration. A popular architecture for
DNN acceleration, adopted by the Google Tensor Processing Unit (TPU), utilizes
a systolic array based matrix multiplication unit at its core. This paper deals
with... | Analyzing and Mitigating the Impact of Permanent Faults on a Systolic
Array Based Neural Network Accelerator | 2,018 | http://arxiv.org/pdf/1802.04657v2 | Title Analyzing Mitigating Impact Permanent Faults Systolic Array Based Neural Network Accelerator Summary Due growing popularity computational cost deep neural network DNNs targeted hardware acceleration popular architecture DNN acceleration adopted Google Tensor Processing Unit TPU utilizes systolic array based matri... | [0.018174592405557632, -0.010011626407504082, -0.04175625368952751, 0.04996875301003456, 0.0658169835805893, 0.020216185599565506, 0.07176627963781357, -0.00827297754585743, 0.010033844970166683, -0.02260962687432766, 0.02354070171713829, 0.03112345188856125, -0.0018009681953117251, 0.04219643399119377, 0.0370676666498... |
1,276 | 1,276 | ['Xingyu Liu', 'Jeff Pool', 'Song Han', 'William J. Dally'] | 1802.06367v1 | Convolutional Neural Networks (CNNs) are computationally intensive, which
limits their application on mobile devices. Their energy is dominated by the
number of multiplies needed to perform the convolutions. Winograd's minimal
filtering algorithm (Lavin, 2015) and network pruning (Han et al., 2015) can
reduce the opera... | Efficient Sparse-Winograd Convolutional Neural Networks | 2,018 | http://arxiv.org/pdf/1802.06367v1 | Title Efficient SparseWinograd Convolutional Neural Networks Summary Convolutional Neural Networks CNNs computationally intensive limit application mobile device energy dominated number multiplies needed perform convolution Winograds minimal filtering algorithm Lavin 2015 network pruning Han et al 2015 reduce operation... | [-0.006850548554211855, 0.07805029302835464, 0.0282461978495121, 0.0728176161646843, 0.01069050282239914, -0.039613474160432816, 0.07376556843519211, 0.006438352167606354, -0.044668786227703094, 0.024298977106809616, -0.01772988773882389, -0.04599148407578468, -0.008649812079966068, 0.010818030685186386, 0.047760773450... |
1,277 | 1,277 | ['Vinay Uday Prabhu', 'Nishant Desai', 'John Whaley'] | 1802.06927v1 | In this paper, we would like to disseminate a serendipitous discovery
involving Lyapunov exponents of a 1-D time series and their use in serving as a
filtering defense tool against a specific kind of deep adversarial
perturbation. To this end, we use the state-of-the-art CleverHans library to
generate adversarial pertu... | On Lyapunov exponents and adversarial perturbation | 2,018 | http://arxiv.org/pdf/1802.06927v1 | Title Lyapunov exponent adversarial perturbation Summary paper would like disseminate serendipitous discovery involving Lyapunov exponent 1D time series use serving filtering defense tool specific kind deep adversarial perturbation end use stateoftheart CleverHans library generate adversarial perturbation standard Conv... | [-0.022910982370376587, 0.018659869208931923, -0.02031935565173626, 0.015665875747799873, -0.019329702481627464, -0.016183625906705856, 0.00815452728420496, 0.018171528354287148, -0.031016558408737183, 0.008201711811125278, 0.04307468235492706, -0.08097264915704727, -0.04538678005337715, 0.06527532637119293, 0.06715340... |
1,278 | 1,278 | ['Tal Ben-Nun', 'Torsten Hoefler'] | 1802.09941v1 | Deep Neural Networks (DNNs) are becoming an important tool in modern
computing applications. Accelerating their training is a major challenge and
techniques range from distributed algorithms to low-level circuit design. In
this survey, we describe the problem from a theoretical perspective, followed
by approaches for i... | Demystifying Parallel and Distributed Deep Learning: An In-Depth
Concurrency Analysis | 2,018 | http://arxiv.org/pdf/1802.09941v1 | Title Demystifying Parallel Distributed Deep Learning InDepth Concurrency Analysis Summary Deep Neural Networks DNNs becoming important tool modern computing application Accelerating training major challenge technique range distributed algorithm lowlevel circuit design survey describe problem theoretical perspective fo... | [-0.014139722101390362, 0.04417242109775543, -0.016048695892095566, 0.07408139109611511, -0.03066244162619114, -0.01148309838026762, 0.08359061926603317, -0.05428282544016838, -0.01868341863155365, -0.012694623321294785, -0.045549653470516205, -0.024997875094413757, 0.016991129145026207, 0.022708186879754066, -0.042600... |
1,279 | 1,279 | ['Kagan Tumer', 'Joydeep Ghosh'] | cs/9905013v1 | Integrating the outputs of multiple classifiers via combiners or
meta-learners has led to substantial improvements in several difficult pattern
recognition problems. In the typical setting investigated till now, each
classifier is trained on data taken or resampled from a common data set, or
(almost) randomly selected ... | Robust Combining of Disparate Classifiers through Order Statistics | 1,999 | http://arxiv.org/pdf/cs/9905013v1 | Title Robust Combining Disparate Classifiers Order Statistics Summary Integrating output multiple classifier via combiners metalearners led substantial improvement several difficult pattern recognition problem typical setting investigated till classifier trained data taken resampled common data set almost randomly sele... | [0.0033243349753320217, 0.03700825572013855, -0.01981152780354023, -0.018016992136836052, -0.0012344983406364918, 0.033205606043338776, 0.06795665621757507, -0.00480225682258606, -0.01868198625743389, -0.045117784291505814, 0.021949773654341698, -0.014264748431742191, 0.09334788471460342, -0.012700811959803104, -0.0323... |
1,280 | 1,280 | ['Frédéric Bastien', 'Yoshua Bengio', 'Arnaud Bergeron', 'Nicolas Boulanger-Lewandowski', 'Thomas Breuel', 'Youssouf Chherawala', 'Moustapha Cisse', 'Myriam Côté', 'Dumitru Erhan', 'Jeremy Eustache', 'Xavier Glorot', 'Xavier Muller', 'Sylvain Pannetier Lebeuf', 'Razvan Pascanu', 'Salah Rifai', 'Francois Savard', 'Guill... | 1009.3589v1 | Recent theoretical and empirical work in statistical machine learning has
demonstrated the importance of learning algorithms for deep architectures,
i.e., function classes obtained by composing multiple non-linear
transformations. Self-taught learning (exploiting unlabeled examples or
examples from other distributions)... | Deep Self-Taught Learning for Handwritten Character Recognition | 2,010 | http://arxiv.org/pdf/1009.3589v1 | Title Deep SelfTaught Learning Handwritten Character Recognition Summary Recent theoretical empirical work statistical machine learning demonstrated importance learning algorithm deep architecture ie function class obtained composing multiple nonlinear transformation Selftaught learning exploiting unlabeled example exa... | [-0.01655230112373829, 0.02031043730676174, -0.016390619799494743, 0.06834777444601059, -0.00966436043381691, 0.006898415740579367, 0.10378711670637131, 0.015818534418940544, 0.0017315736040472984, 0.012750939466059208, -0.01459258608520031, 0.026064295321702957, 0.037962015718221664, 0.041975442320108414, 0.0291551947... |
1,281 | 1,281 | ['Holger R. Roth', 'Le Lu', 'Ari Seff', 'Kevin M. Cherry', 'Joanne Hoffman', 'Shijun Wang', 'Jiamin Liu', 'Evrim Turkbey', 'Ronald M. Summers'] | 1406.2639v1 | Automated Lymph Node (LN) detection is an important clinical diagnostic task
but very challenging due to the low contrast of surrounding structures in
Computed Tomography (CT) and to their varying sizes, poses, shapes and sparsely
distributed locations. State-of-the-art studies show the performance range of
52.9% sensi... | A New 2.5D Representation for Lymph Node Detection using Random Sets of
Deep Convolutional Neural Network Observations | 2,014 | http://arxiv.org/pdf/1406.2639v1 | Title New 25D Representation Lymph Node Detection using Random Sets Deep Convolutional Neural Network Observations Summary Automated Lymph Node LN detection important clinical diagnostic task challenging due low contrast surrounding structure Computed Tomography CT varying size pose shape sparsely distributed location ... | [-0.008637003600597382, -0.017441168427467346, -0.02512010559439659, -0.002739807590842247, -0.04122290387749672, -0.016685549169778824, 0.03773622214794159, 0.010982673615217209, 0.02463010884821415, 0.041838034987449646, 0.020850876346230507, -0.016511712223291397, -0.030121080577373505, 0.008731517009437084, -0.0184... |
1,282 | 1,282 | ['Sijin Li', 'Zhi-Qiang Liu', 'Antoni B. Chan'] | 1406.3474v1 | We propose an heterogeneous multi-task learning framework for human pose
estimation from monocular image with deep convolutional neural network. In
particular, we simultaneously learn a pose-joint regressor and a sliding-window
body-part detector in a deep network architecture. We show that including the
body-part dete... | Heterogeneous Multi-task Learning for Human Pose Estimation with Deep
Convolutional Neural Network | 2,014 | http://arxiv.org/pdf/1406.3474v1 | Title Heterogeneous Multitask Learning Human Pose Estimation Deep Convolutional Neural Network Summary propose heterogeneous multitask learning framework human pose estimation monocular image deep convolutional neural network particular simultaneously learn posejoint regressor slidingwindow bodypart detector deep netwo... | [-0.02100444585084915, 0.02864932082593441, -0.036122579127550125, 0.029148230329155922, 0.04041983187198639, 0.020600976422429085, 0.015538412146270275, -0.014263544231653214, 0.02987072430551052, -0.035333264619112015, -0.05488698184490204, -0.06873107701539993, 0.03127753734588623, 0.007236036472022533, 0.0603905878... |
1,283 | 1,283 | ['Alexey Dosovitskiy', 'Philipp Fischer', 'Jost Tobias Springenberg', 'Martin Riedmiller', 'Thomas Brox'] | 1406.6909v2 | Deep convolutional networks have proven to be very successful in learning
task specific features that allow for unprecedented performance on various
computer vision tasks. Training of such networks follows mostly the supervised
learning paradigm, where sufficiently many input-output pairs are required for
training. Acq... | Discriminative Unsupervised Feature Learning with Exemplar Convolutional
Neural Networks | 2,014 | http://arxiv.org/pdf/1406.6909v2 | Title Discriminative Unsupervised Feature Learning Exemplar Convolutional Neural Networks Summary Deep convolutional network proven successful learning task specific feature allow unprecedented performance various computer vision task Training network follows mostly supervised learning paradigm sufficiently many inputo... | [-0.02972966991364956, 0.014566835947334766, 0.0014093855861574411, 0.08222246915102005, 0.024936173111200333, -0.006173251196742058, 0.06945673376321793, -0.002376290038228035, 0.010993259027600288, -0.008254345506429672, -0.010138756595551968, 0.057871926575899124, -0.018555255606770515, 0.044171079993247986, 0.02166... |
1,284 | 1,284 | ['Geoffrey E. Hinton', 'Nitish Srivastava', 'Alex Krizhevsky', 'Ilya Sutskever', 'Ruslan R. Salakhutdinov'] | 1207.0580v1 | When a large feedforward neural network is trained on a small training set,
it typically performs poorly on held-out test data. This "overfitting" is
greatly reduced by randomly omitting half of the feature detectors on each
training case. This prevents complex co-adaptations in which a feature detector
is only helpful... | Improving neural networks by preventing co-adaptation of feature
detectors | 2,012 | http://arxiv.org/pdf/1207.0580v1 | Title Improving neural network preventing coadaptation feature detector Summary large feedforward neural network trained small training set typically performs poorly heldout test data overfitting greatly reduced randomly omitting half feature detector training case prevents complex coadaptations feature detector helpfu... | [-0.022551847621798515, 0.04460768401622772, 0.028782280161976814, 0.0554269477725029, 0.005728363059461117, -0.0026410461869090796, 0.03652056306600571, -0.014345422387123108, -0.026584817096590996, -0.007680885028094053, -0.058417920023202896, 0.01764410361647606, 0.030286256223917007, 0.0943911224603653, 0.003108060... |
1,285 | 1,285 | ['Lou Marvin Caraig'] | 1207.5774v3 | The Sparse Distributed Memory proposed by Pentii Kanerva (SDM in short) was
thought to be a model of human long term memory. The architecture of the SDM
permits to store binary patterns and to retrieve them using partially matching
patterns. However Kanerva's model is especially efficient only in handling
random data. ... | A New Training Algorithm for Kanerva's Sparse Distributed Memory | 2,012 | http://arxiv.org/pdf/1207.5774v3 | Title New Training Algorithm Kanervas Sparse Distributed Memory Summary Sparse Distributed Memory proposed Pentii Kanerva SDM short thought model human long term memory architecture SDM permit store binary pattern retrieve using partially matching pattern However Kanervas model especially efficient handling random data... | [0.028168728575110435, -0.004952354356646538, 0.00010467105312272906, 0.02855062298476696, -0.02128901705145836, -0.00922656524926424, 0.040516674518585205, 0.032775506377220154, -0.038389988243579865, -0.028713220730423927, 0.018169773742556572, -0.04427427798509598, -0.0016448211390525103, 0.03755858168005943, 0.0093... |
1,286 | 1,286 | ['Xiangyu Zhang', 'Jianhua Zou', 'Kaiming He', 'Jian Sun'] | 1505.06798v2 | This paper aims to accelerate the test-time computation of convolutional
neural networks (CNNs), especially very deep CNNs that have substantially
impacted the computer vision community. Unlike previous methods that are
designed for approximating linear filters or linear responses, our method takes
the nonlinear units ... | Accelerating Very Deep Convolutional Networks for Classification and
Detection | 2,015 | http://arxiv.org/pdf/1505.06798v2 | Title Accelerating Deep Convolutional Networks Classification Detection Summary paper aim accelerate testtime computation convolutional neural network CNNs especially deep CNNs substantially impacted computer vision community Unlike previous method designed approximating linear filter linear response method take nonlin... | [-0.01299706194549799, 0.049706313759088516, -0.013436630368232727, 0.13289044797420502, 0.0028946702368557453, -0.006387717556208372, 0.0325813889503479, 0.02892559953033924, 0.012740431353449821, -0.02126402221620083, 0.012752329930663109, 0.044696491211652756, -0.030155330896377563, 0.05201643705368042, 0.0209901910... |
1,287 | 1,287 | ['Lei Wang', 'Baochang Zhang'] | 1505.06800v1 | This paper proposes boosting-like deep learning (BDL) framework for
pedestrian detection. Due to overtraining on the limited training samples,
overfitting is a major problem of deep learning. We incorporate a boosting-like
technique into deep learning to weigh the training samples, and thus prevent
overtraining in the ... | Boosting-like Deep Learning For Pedestrian Detection | 2,015 | http://arxiv.org/pdf/1505.06800v1 | Title Boostinglike Deep Learning Pedestrian Detection Summary paper proposes boostinglike deep learning BDL framework pedestrian detection Due overtraining limited training sample overfitting major problem deep learning incorporate boostinglike technique deep learning weigh training sample thus prevent overtraining ite... | [-0.027217471972107887, -0.026003912091255188, 0.002112295478582382, 0.0628073662519455, 0.00659519387409091, 0.025538448244333267, -0.015554523095488548, 0.0038489762227982283, -0.006982299033552408, 0.013168951496481895, 0.014449158683419228, 0.04842018336057663, -0.03582747280597687, -0.02246512472629547, -0.0138990... |
1,288 | 1,288 | ['Jyothi Korra'] | 1604.02376v2 | In Computer Vision, problem of identifying or classifying the objects present
in an image is called Object Categorization. It is a challenging problem,
especially when the images have clutter background, occlusions or different
lighting conditions. Many vision features have been proposed which aid object
categorization... | Finding Optimal Combination of Kernels using Genetic Programming | 2,016 | http://arxiv.org/pdf/1604.02376v2 | Title Finding Optimal Combination Kernels using Genetic Programming Summary Computer Vision problem identifying classifying object present image called Object Categorization challenging problem especially image clutter background occlusion different lighting condition Many vision feature proposed aid object categorizat... | [-0.009314346127212048, 0.019023843109607697, -0.018202193081378937, 0.06400197744369507, -0.00837255921214819, -0.0026058570947498083, 0.023166514933109283, 0.05722185969352722, 0.03262889012694359, -0.026188919320702553, 0.03877796232700348, 0.05200709030032158, 0.01111442968249321, 0.011155807413160801, 0.0175313204... |
1,289 | 1,289 | ['Biswajit Paria', 'Vikas Reddy', 'Anirban Santara', 'Pabitra Mitra'] | 1604.02646v3 | The success of deep neural networks is mostly due their ability to learn
meaningful features from the data. Features learned in the hidden layers of
deep neural networks trained in computer vision tasks have been shown to be
similar to mid-level vision features. We leverage this fact in this work and
propose the visual... | Visualization Regularizers for Neural Network based Image Recognition | 2,016 | http://arxiv.org/pdf/1604.02646v3 | Title Visualization Regularizers Neural Network based Image Recognition Summary success deep neural network mostly due ability learn meaningful feature data Features learned hidden layer deep neural network trained computer vision task shown similar midlevel vision feature leverage fact work propose visualization regul... | [-0.012037497945129871, 0.04560335725545883, -0.02106517180800438, 0.04609900712966919, 0.023573758080601692, -0.015860341489315033, 0.015994740650057793, 0.02769344486296177, -0.00286907353438437, 0.05008585751056671, 0.02630486525595188, 0.09598731249570847, 0.008211609907448292, -0.004103078041225672, 0.007778150960... |
1,290 | 1,290 | ['Ashley Prater'] | 1604.03073v2 | Reservoir computing is a recently introduced machine learning paradigm that
has been shown to be well-suited for the processing of spatiotemporal data.
Rather than training the network node connections and weights via
backpropagation in traditional recurrent neural networks, reservoirs instead
have fixed connections an... | Reservoir computing for spatiotemporal signal classification without
trained output weights | 2,016 | http://arxiv.org/pdf/1604.03073v2 | Title Reservoir computing spatiotemporal signal classification without trained output weight Summary Reservoir computing recently introduced machine learning paradigm shown wellsuited processing spatiotemporal data Rather training network node connection weight via backpropagation traditional recurrent neural network r... | [-0.05331907421350479, 0.010840771719813347, -0.03555891290307045, -0.01076823566108942, 0.0038990764878690243, 0.017771175131201744, 0.04048176109790802, -0.034472402185201645, 0.010651816613972187, 0.0266720037907362, 0.019613485783338547, -0.0010804618941619992, 0.040080476552248, 0.022253913804888725, 0.04988390579... |
1,291 | 1,291 | ['Jason Kuen', 'Kian Ming Lim', 'Chin Poo Lee'] | 1604.04144v1 | Visual representation is crucial for a visual tracking method's performances.
Conventionally, visual representations adopted in visual tracking rely on
hand-crafted computer vision descriptors. These descriptors were developed
generically without considering tracking-specific information. In this paper,
we propose to l... | Self-taught learning of a deep invariant representation for visual
tracking via temporal slowness principle | 2,016 | http://arxiv.org/pdf/1604.04144v1 | Title Selftaught learning deep invariant representation visual tracking via temporal slowness principle Summary Visual representation crucial visual tracking method performance Conventionally visual representation adopted visual tracking rely handcrafted computer vision descriptor descriptor developed generically witho... | [-0.049022823572158813, 0.06272765249013901, -0.0016849592793732882, 0.057420335710048676, 0.02984175831079483, -0.01895485259592533, 0.03547830507159233, 0.028954308480024338, -0.031147433444857597, -0.01848485693335533, 0.05986462160944939, -0.020572742447257042, -0.024902770295739174, 0.06879204511642456, 0.01768510... |
1,292 | 1,292 | ['Jiang Wang', 'Yi Yang', 'Junhua Mao', 'Zhiheng Huang', 'Chang Huang', 'Wei Xu'] | 1604.04573v1 | While deep convolutional neural networks (CNNs) have shown a great success in
single-label image classification, it is important to note that real world
images generally contain multiple labels, which could correspond to different
objects, scenes, actions and attributes in an image. Traditional approaches to
multi-labe... | CNN-RNN: A Unified Framework for Multi-label Image Classification | 2,016 | http://arxiv.org/pdf/1604.04573v1 | Title CNNRNN Unified Framework Multilabel Image Classification Summary deep convolutional neural network CNNs shown great success singlelabel image classification important note real world image generally contain multiple label could correspond different object scene action attribute image Traditional approach multilab... | [0.04776640981435776, 0.0533481165766716, 0.014942086301743984, 0.0747450441122055, 0.004805758595466614, 0.00595345301553607, 0.04863865673542023, 0.006765434984117746, -0.016401587054133415, -0.03206595778465271, -0.03871645778417587, 0.048140451312065125, -0.03995596989989281, 0.004954193253070116, 0.006143094506114... |
1,293 | 1,293 | ['Markus Thom', 'Matthias Rapp', 'Günther Palm'] | 1604.04767v1 | Learning dictionaries suitable for sparse coding instead of using engineered
bases has proven effective in a variety of image processing tasks. This paper
studies the optimization of dictionaries on image data where the representation
is enforced to be explicitly sparse with respect to a smooth, normalized
sparseness m... | Efficient Dictionary Learning with Sparseness-Enforcing Projections | 2,016 | http://arxiv.org/pdf/1604.04767v1 | Title Efficient Dictionary Learning SparsenessEnforcing Projections Summary Learning dictionary suitable sparse coding instead using engineered base proven effective variety image processing task paper study optimization dictionary image data representation enforced explicitly sparse respect smooth normalized sparsenes... | [-0.04228078946471214, 0.02037239819765091, 0.004286458715796471, 0.04913000017404556, 0.008266383782029152, -0.014589275233447552, 0.0052305907011032104, 0.028183577582240105, -0.02306833676993847, 0.01894480548799038, 0.06478360295295715, 0.019637849181890488, 0.005339419934898615, 0.0883985087275505, -0.010233661159... |
1,294 | 1,294 | ['Mariusz Bojarski', 'Davide Del Testa', 'Daniel Dworakowski', 'Bernhard Firner', 'Beat Flepp', 'Prasoon Goyal', 'Lawrence D. Jackel', 'Mathew Monfort', 'Urs Muller', 'Jiakai Zhang', 'Xin Zhang', 'Jake Zhao', 'Karol Zieba'] | 1604.07316v1 | We trained a convolutional neural network (CNN) to map raw pixels from a
single front-facing camera directly to steering commands. This end-to-end
approach proved surprisingly powerful. With minimum training data from humans
the system learns to drive in traffic on local roads with or without lane
markings and on highw... | End to End Learning for Self-Driving Cars | 2,016 | http://arxiv.org/pdf/1604.07316v1 | Title End End Learning SelfDriving Cars Summary trained convolutional neural network CNN map raw pixel single frontfacing camera directly steering command endtoend approach proved surprisingly powerful minimum training data human system learns drive traffic local road without lane marking highway also operates area unc... | [-0.0052204192616045475, -0.0005378538044169545, 0.0010341499000787735, 0.09203584492206573, 0.004228456411510706, 0.00496628088876605, 0.03637706860899925, 0.00038848735857754946, -0.031071534380316734, -0.024753548204898834, 0.01998056285083294, 0.08151568472385406, -0.03609565272927284, 0.06427893042564392, 0.032730... |
1,295 | 1,295 | ['Wen Wang', 'Zhen Cui', 'Hong Chang', 'Shiguang Shan', 'Xilin Chen'] | 1402.2031v1 | The comparison of heterogeneous samples extensively exists in many
applications, especially in the task of image classification. In this paper, we
propose a simple but effective coupled neural network, called Deeply Coupled
Autoencoder Networks (DCAN), which seeks to build two deep neural networks,
coupled with each ot... | Deeply Coupled Auto-encoder Networks for Cross-view Classification | 2,014 | http://arxiv.org/pdf/1402.2031v1 | Title Deeply Coupled Autoencoder Networks Crossview Classification Summary comparison heterogeneous sample extensively exists many application especially task image classification paper propose simple effective coupled neural network called Deeply Coupled Autoencoder Networks DCAN seek build two deep neural network cou... | [-0.03552306443452835, -0.0017497153021395206, -0.02156813256442547, 0.06486256420612335, 0.020600484684109688, 0.014657450839877129, 0.050341397523880005, -0.01938673108816147, 0.0218329019844532, -0.021376438438892365, -0.08015791326761246, 0.01679808273911476, 0.02498188242316246, 0.020889148116111755, 0.00358513859... |
1,296 | 1,296 | ['S. K. Katiyar', 'P. V. Arun'] | 1405.6137v1 | The improvements in spectral and spatial resolution of the satellite images
have facilitated the automatic extraction and identification of the features
from satellite images and aerial photographs. An automatic object extraction
method is presented for extracting and identifying the various objects from
satellite imag... | An enhanced neural network based approach towards object extraction | 2,014 | http://arxiv.org/pdf/1405.6137v1 | Title enhanced neural network based approach towards object extraction Summary improvement spectral spatial resolution satellite image facilitated automatic extraction identification feature satellite image aerial photograph automatic object extraction method presented extracting identifying various object satellite im... | [0.026542624458670616, 0.022746378555893898, 0.01991499401628971, 0.07399605959653854, -0.023753676563501358, -0.009220050647854805, -0.015764430165290833, -0.035527877509593964, -0.014567505568265915, 0.0048294151201844215, 0.03618008643388748, 0.06346581876277924, 0.030375467613339424, 0.03418363630771637, 0.03978973... |
1,297 | 1,297 | ['Mohammad Ali Keyvanrad', 'Mohammad Mehdi Homayounpour'] | 1408.3264v7 | Nowadays, this is very popular to use the deep architectures in machine
learning. Deep Belief Networks (DBNs) are deep architectures that use stack of
Restricted Boltzmann Machines (RBM) to create a powerful generative model using
training data. DBNs have many ability like feature extraction and
classification that are... | A brief survey on deep belief networks and introducing a new object
oriented toolbox (DeeBNet) | 2,014 | http://arxiv.org/pdf/1408.3264v7 | Title brief survey deep belief network introducing new object oriented toolbox DeeBNet Summary Nowadays popular use deep architecture machine learning Deep Belief Networks DBNs deep architecture use stack Restricted Boltzmann Machines RBM create powerful generative model using training data DBNs many ability like featu... | [-0.03950696066021919, 0.04416142776608467, -0.016896501183509827, 0.054647836834192276, -0.011401049792766571, -0.0042555853724479675, 0.07369701564311981, -0.024179836735129356, 0.016666268929839134, 0.01472508069127798, -0.025297628715634346, 0.017359817400574684, 0.009478696621954441, 0.06041313335299492, 0.0070667... |
1,298 | 1,298 | ['Sébastien Ouellet'] | 1408.3750v1 | The goal of the present study is to explore the application of deep
convolutional network features to emotion recognition. Results indicate that
they perform similarly to other published models at a best recognition rate of
94.4%, and do so with a single still image rather than a video stream. An
implementation of an a... | Real-time emotion recognition for gaming using deep convolutional
network features | 2,014 | http://arxiv.org/pdf/1408.3750v1 | Title Realtime emotion recognition gaming using deep convolutional network feature Summary goal present study explore application deep convolutional network feature emotion recognition Results indicate perform similarly published model best recognition rate 944 single still image rather video stream implementation affe... | [0.0020066150464117527, 0.017236609011888504, -0.02321830578148365, 0.07818114012479782, 0.04029898717999458, 0.025645524263381958, 0.018791958689689636, -0.005568698048591614, -0.007751898840069771, -0.007275332231074572, -0.016296900808811188, -0.013765068724751472, -0.02998919039964676, 0.07420498877763748, 0.030264... |
1,299 | 1,299 | ['Yangqing Jia', 'Evan Shelhamer', 'Jeff Donahue', 'Sergey Karayev', 'Jonathan Long', 'Ross Girshick', 'Sergio Guadarrama', 'Trevor Darrell'] | 1408.5093v1 | Caffe provides multimedia scientists and practitioners with a clean and
modifiable framework for state-of-the-art deep learning algorithms and a
collection of reference models. The framework is a BSD-licensed C++ library
with Python and MATLAB bindings for training and deploying general-purpose
convolutional neural net... | Caffe: Convolutional Architecture for Fast Feature Embedding | 2,014 | http://arxiv.org/pdf/1408.5093v1 | Title Caffe Convolutional Architecture Fast Feature Embedding Summary Caffe provides multimedia scientist practitioner clean modifiable framework stateoftheart deep learning algorithm collection reference model framework BSDlicensed C library Python MATLAB binding training deploying generalpurpose convolutional neural ... | [0.0014900341629981995, 0.03233158588409424, 0.007082968018949032, 0.07052892446517944, 0.013149814680218697, -0.03736178204417229, 0.04942554980516434, -0.002487603574991226, -0.07673437148332596, -0.01635831408202648, -0.009302343241870403, 0.0036060784477740526, -0.013153237290680408, 0.08294370025396347, 0.02912564... |
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