aid stringlengths 9 15 | mid stringlengths 7 10 | abstract stringlengths 78 2.56k | related_work stringlengths 92 1.77k | ref_abstract dict |
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1907.05274 | 2960314297 | Autonomous vehicles (AV) have progressed rapidly with the advancements in computer vision algorithms. The deep convolutional neural network as the main contributor to this advancement has boosted the classification accuracy dramatically. However, the discovery of adversarial examples reveals the generalization gap betw... | Several studies address invariance property of deep CNN for affine transformations. In @cite_17 , anti-distortion classification result is achieved by inserting Spatial Transformer layer into the given network. However the affine parameters are not presented in a disentangled manner, which makes it less interpretable. ... | {
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"Convolutional Neural Networks (ConvNets) have shown excellent results on many visual classificat... |
1907.05321 | 2957988017 | Time is an important feature in many applications involving events that occur synchronously and or asynchronously. To effectively consume time information, recent studies have focused on designing new architectures. In this paper, we take an orthogonal but complementary approach by providing a model-agnostic vector rep... | There is a long history of algorithms for predictive modeling in time series analysis. They include auto-regressive techniques @cite_40 that predict future measurements in a sequence based on a window of past measurements. Since it is not always clear how long the window of past measurements should be, hidden Markov mo... | {
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1907.05321 | 2957988017 | Time is an important feature in many applications involving events that occur synchronously and or asynchronously. To effectively consume time information, recent studies have focused on designing new architectures. In this paper, we take an orthogonal but complementary approach by providing a model-agnostic vector rep... | While there is a body of literature on designing neural networks with activations @cite_0 @cite_14 @cite_8 @cite_45 @cite_23 , our work uses only for transforming time; the rest of the network uses other activations. There is also a set of techniques that consider time as yet another feature and concatenate time (or so... | {
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1907.05380 | 2960069057 | Super-resolution (SR) is a technique that allows increasing the resolution of a given image. Having applications in many areas, from medical imaging to consumer electronics, several SR methods have been proposed. Currently, the best performing methods are based on convolutional neural networks (CNNs) and require extens... | Statistical models The work in @cite_33 models the gradient profile of an image as a Gauss-Markov random field and performs SR via maximum likelihood estimation with the constraint that downsampled solution coincides with the given LR image. @cite_25 , images are super-resolved by ensuring that the gradient of the HR i... | {
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"In this paper, we propose an image super-resolution approach using a novel generic image prior - gradient profile prior, which is a parametric prior describing the shape and the sharpness of the i... |
1907.05380 | 2960069057 | Super-resolution (SR) is a technique that allows increasing the resolution of a given image. Having applications in many areas, from medical imaging to consumer electronics, several SR methods have been proposed. Currently, the best performing methods are based on convolutional neural networks (CNNs) and require extens... | Total variation methods A different line of work directly encodes prior knowledge into the optimization problem. For instance, @cite_6 introduced the concept of TV to capture the number of edges in an image and observed that natural images have small TV. Since then, several algorithms have been proposed to super-resolv... | {
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1907.05380 | 2960069057 | Super-resolution (SR) is a technique that allows increasing the resolution of a given image. Having applications in many areas, from medical imaging to consumer electronics, several SR methods have been proposed. Currently, the best performing methods are based on convolutional neural networks (CNNs) and require extens... | Alternative regularizers Natural images also have simple representations in other domains, and other regularizers have been used for SR. For example, @cite_18 uses the fact that images have sparse wavelet representations. The patches of natural images also tend to lie on a low-dimensional manifold @cite_13 , which has ... | {
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1907.05380 | 2960069057 | Super-resolution (SR) is a technique that allows increasing the resolution of a given image. Having applications in many areas, from medical imaging to consumer electronics, several SR methods have been proposed. Currently, the best performing methods are based on convolutional neural networks (CNNs) and require extens... | Regression methods Regression methods compute the LR-HR map using a set of basis functions whose coefficients are computed via regression. The key idea is to perform clustering on the training images, and use algorithms like kernel ridge regression (KRR) @cite_7 , Gaussian process regression @cite_10 and random forests... | {
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"The aim of single image super-resolution is to reconstruct a high-resolution image from a single low-resolution input. Although the task is ill-posed it can be see... |
1907.05380 | 2960069057 | Super-resolution (SR) is a technique that allows increasing the resolution of a given image. Having applications in many areas, from medical imaging to consumer electronics, several SR methods have been proposed. Currently, the best performing methods are based on convolutional neural networks (CNNs) and require extens... | CNN-based Currently, the best performing SR methods are based on CNNs. Taking inspiration in the dictionary learning algorithm in @cite_39 , SRCNN @cite_1 was one of the first CNNs performing image SR. It consists of a patch extraction layer, a representation layer of the non-linear mappings, and a final layer that out... | {
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1907.05336 | 2959233168 | Translation-based embedding models have gained significant attention in link prediction tasks for knowledge graphs. TransE is the primary model among translation-based embeddings and is well-known for its low complexity and high efficiency. Therefore, most of the earlier works have modified the score function of the Tr... | In order to fulfill the gap of MRL in assigning high scores to positive samples, a limited-based scoring function has been proposed @cite_17 . This method limits the score of positive samples by adding an upper-bound ( @math ). It is represented as limited-based scoring loss illustrated in fig:sota . In this way, the s... | {
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"Knowledge graphs (KGs), i.e. representation of information as a semantic graph, provide a significant test bed for many tasks including question answering, recommendation, a... |
1907.05336 | 2959233168 | Translation-based embedding models have gained significant attention in link prediction tasks for knowledge graphs. TransE is the primary model among translation-based embeddings and is well-known for its low complexity and high efficiency. Therefore, most of the earlier works have modified the score function of the Tr... | A modified version of the two previous loss functions is introduced in our previous work @cite_10 . This approach fixes the upper-bound of positive samples ( @math ) and uses a sliding mechanism to move false negative samples towards positive samples, shown as Soft Margin in fig:sota . @math refers to embedding paramet... | {
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"Knowledge graphs (KGs), i.e. representation of information as a semantic graph, provide a significant test bed for many tasks including question answering, recommendation, and link prediction. Various amount of scholarly metadata ... |
1907.05375 | 2958043174 | Road boundaries, or curbs, provide autonomous vehicles with essential information when interpreting road scenes and generating behaviour plans. Although curbs convey important information, they are difficult to detect in complex urban environments (in particular in comparison to other elements of the road such as traff... | LIDAR-based methods often rely on more traditional information engineering techniques. In @cite_5 , a ring compression analysis on dense 3D LIDAR data followed by false-positive filters was used to detect curb points. Curb models are estimated using Least Trimmed Squares (LTS) and describe the road shape on occluded cu... | {
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"Localization is an important component of autonomous vehicles, as it enables the accomplishment of tasks, such as path planning and navigation. Although vehicle pos... |
1901.02404 | 2910894887 | Creating an image reflecting the content of a long text is a complex process that requires a sense of creativity. For example, creating a book cover or a movie poster based on their summary or a food image based on its recipe. In this paper we present the new task of generating images from long text that does not descr... | Generating high-resolution images conditioned on text descriptions is a fundamental problem in computer vision. This problem is being studied extensively and various approaches were suggested to tackle it. Deep generative models, such as @cite_0 @cite_12 @cite_13 @cite_14 , achieved tremendous progress in this domain. ... | {
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1901.02273 | 2908623876 | Spatial transformer network has been used in a layered form in conjunction with a convolutional network to enable the model to transform data spatially. In this paper, we propose a combined spatial transformer network (STN) and a Long Short-Term Memory network (LSTM) to classify digits in sequences formed by MINST elem... | 2016 proposed an recurrent attentional networks for saliency detection to detect the objects with a top-down mechanism @cite_8 .Chen-Hsuan 2016 introduced the IC-STNs attention mechanism based on LK algorithm and Inverse Compositional Variant Method to eliminate distortion with less model capacity @cite_7 . 2014 combin... | {
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1901.02453 | 2909735499 | Inverse rendering aims to estimate physical attributes of a scene, e.g., reflectance, geometry, and lighting, from image(s). Inverse rendering has been studied primarily for single objects or with methods that solve for only one of the scene attributes. We propose the first learning-based approach that jointly estimate... | For inverse rendering from a few images, most traditional optimization-based approaches make strong assumptions about statistical priors on illumination and or reflectance. A variety of sub-problems have been studied, such as intrinsic image decomposition @cite_8 , shape from shading @cite_37 @cite_30 , and BRDF estima... | {
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1901.02453 | 2909735499 | Inverse rendering aims to estimate physical attributes of a scene, e.g., reflectance, geometry, and lighting, from image(s). Inverse rendering has been studied primarily for single objects or with methods that solve for only one of the scene attributes. We propose the first learning-based approach that jointly estimate... | With recent advances in deep learning, researchers have proposed to learn data-driven priors to solve some of these inverse problems with CNNs, many of which have achieved promising results. For example, it is shown that depth and normals may be estimated from a single image @cite_48 @cite_23 @cite_0 or multiple images... | {
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1901.02453 | 2909735499 | Inverse rendering aims to estimate physical attributes of a scene, e.g., reflectance, geometry, and lighting, from image(s). Inverse rendering has been studied primarily for single objects or with methods that solve for only one of the scene attributes. We propose the first learning-based approach that jointly estimate... | A few recent work from the graphics community proposed differentiable Monte Carlo renderers @cite_34 @cite_26 for optimizing rendering parameters ( , camera poses, scattering parameters) for synthetic 3D scenes. Neural mesh renderer @cite_38 addressed the problem of differentiable visibility and rasterization. Our prop... | {
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1901.02453 | 2909735499 | Inverse rendering aims to estimate physical attributes of a scene, e.g., reflectance, geometry, and lighting, from image(s). Inverse rendering has been studied primarily for single objects or with methods that solve for only one of the scene attributes. We propose the first learning-based approach that jointly estimate... | High-quality synthetic data is essential for learning-based inverse rendering. SUNCG @cite_46 created a large-scale 3D indoor scene dataset. The images of the SUNCG dataset are not photo-realistic as they are rendered with OpenGL using diffuse materials and point source lighting. An extension of this dataset, PBRS @cit... | {
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1901.02453 | 2909735499 | Inverse rendering aims to estimate physical attributes of a scene, e.g., reflectance, geometry, and lighting, from image(s). Inverse rendering has been studied primarily for single objects or with methods that solve for only one of the scene attributes. We propose the first learning-based approach that jointly estimate... | Intrinsic image decomposition is a sub-problem of inverse rendering, where a single image is decomposed into albedo and shading. Recent methods learn intrinsic image decomposition from labeled synthetic data @cite_9 @cite_40 @cite_25 and from unlabeled @cite_11 or partially labeled real data @cite_6 @cite_35 @cite_2 @c... | {
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1901.02350 | 2909237195 | In recent years, face detection has experienced significant performance improvement with the boost of deep convolutional neural networks. In this report, we reimplement the state-of-the-art detector SRN and apply some tricks proposed in the recent literatures to obtain an extremely strong face detector, named VIM-FD. I... | Face detection has been a challenging research field since its emergence in the 1990s. Viola and Jones pioneer to use Haar features and AdaBoost to train a face detector with promising accuracy and efficiency @cite_39 , which inspires several different approaches afterwards @cite_16 @cite_35 . Apart from those, another... | {
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1901.02350 | 2909237195 | In recent years, face detection has experienced significant performance improvement with the boost of deep convolutional neural networks. In this report, we reimplement the state-of-the-art detector SRN and apply some tricks proposed in the recent literatures to obtain an extremely strong face detector, named VIM-FD. I... | Recently, face detection has been dominated by the CNN-based methods. CascadeCNN @cite_23 improves detection accuracy by training a serious of interleaved CNN models and following work @cite_21 proposes to jointly train the cascaded CNNs to realize end-to-end optimization. EMO @cite_12 proposes an Expected Max Overlapp... | {
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1901.02350 | 2909237195 | In recent years, face detection has experienced significant performance improvement with the boost of deep convolutional neural networks. In this report, we reimplement the state-of-the-art detector SRN and apply some tricks proposed in the recent literatures to obtain an extremely strong face detector, named VIM-FD. I... | Additionally, face detection has inherited some achievements from generic object detectors, such as Faster R-CNN @cite_10 , SSD @cite_42 , FPN @cite_18 , RefineDet @cite_24 and RetinaNet @cite_34 . Face R-CNN @cite_30 combines Faster R-CNN with hard negative mining and achieves promising results. FaceBoxes @cite_13 int... | {
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1901.02219 | 2911173632 | We consider the problem of detecting out-of-distribution (OOD) samples in deep reinforcement learning. In a value based reinforcement learning setting, we propose to use uncertainty estimation techniques directly on the agent's value estimating neural network to detect OOD samples. The focus of our work lies in analyzi... | A systematic way to deal with uncertainty is via Bayesian inference. Its combination with neural networks in the form of Bayesian neural networks is realised by placing a probability distribution over the weight-values of the network @cite_12 . As calculating the exact Bayesian posterior quickly becomes computationally... | {
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1901.02219 | 2911173632 | We consider the problem of detecting out-of-distribution (OOD) samples in deep reinforcement learning. In a value based reinforcement learning setting, we propose to use uncertainty estimation techniques directly on the agent's value estimating neural network to detect OOD samples. The focus of our work lies in analyzi... | For the case of low-dimensional feature spaces, OOD detection (also called novelty detection) is a well-researched problem. For a survey on the topic, see e.g. , who distinguish between probabilistic, distance-based, reconstruction-based, domain-based and information theoretic methods. During the last years, several ne... | {
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1901.02446 | 2954469458 | The recently introduced panoptic segmentation task has renewed our community's interest in unifying the tasks of instance segmentation (for thing classes) and semantic segmentation (for stuff classes). However, current state-of-the-art methods for this joint task use separate and dissimilar networks for instance and se... | The joint task of thing and stuff segmentation has a rich history, including early work on scene parsing @cite_37 , image parsing @cite_33 , and holistic scene understanding @cite_1 . With the recent introduction of the joint task @cite_5 , which includes a simple task specification and carefully designed task metrics,... | {
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1901.02446 | 2954469458 | The recently introduced panoptic segmentation task has renewed our community's interest in unifying the tasks of instance segmentation (for thing classes) and semantic segmentation (for stuff classes). However, current state-of-the-art methods for this joint task use separate and dissimilar networks for instance and se... | This year's COCO and Mapillary Recognition Challenge @cite_6 @cite_19 featured panoptic segmentation tracks that proved popular. However, every competitive entry in the panoptic challenges used , with no shared computation. For details of not yet published winning entries in the 2018 COCO and Mapillary Recognition Chal... | {
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1901.02446 | 2954469458 | The recently introduced panoptic segmentation task has renewed our community's interest in unifying the tasks of instance segmentation (for thing classes) and semantic segmentation (for stuff classes). However, current state-of-the-art methods for this joint task use separate and dissimilar networks for instance and se... | Region-based approaches to object detection, including the Slow Fast Faster Mask R-CNN family @cite_10 @cite_27 @cite_9 @cite_7 , which apply deep networks on candidate object regions, have proven highly successful. All recent winners of the COCO detection challenges have built on Mask R-CNN @cite_0 with FPN @cite_35 ,... | {
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1901.02446 | 2954469458 | The recently introduced panoptic segmentation task has renewed our community's interest in unifying the tasks of instance segmentation (for thing classes) and semantic segmentation (for stuff classes). However, current state-of-the-art methods for this joint task use separate and dissimilar networks for instance and se... | In our work we adopt an encoder-decoder framework, namely FPN @cite_35 . In contrast to symmetric' decoders @cite_60 , FPN uses a lightweight decoder (see Fig. ). FPN was designed for instance segmentation, and it serves as the default backbone for Mask R-CNN. We show that . | {
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1901.02257 | 2949229857 | Commonsense Reading Comprehension (CRC) is a significantly challenging task, aiming at choosing the right answer for the question referring to a narrative passage, which may require commonsense knowledge inference. Most of the existing approaches only fuse the interaction information of choice, passage, and question in... | Many architectures on MRC follow the process of representation, attention, fusion, and aggregation @cite_11 @cite_20 @cite_7 @cite_19 @cite_16 @cite_3 . BiDAF @cite_11 fuses the passage-aware question, the question-aware passage, and the original passage in context layer by concatenation, and then uses a BiLSTM for agg... | {
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1901.02257 | 2949229857 | Commonsense Reading Comprehension (CRC) is a significantly challenging task, aiming at choosing the right answer for the question referring to a narrative passage, which may require commonsense knowledge inference. Most of the existing approaches only fuse the interaction information of choice, passage, and question in... | @cite_3 present a DFN model to fuse the passage, question, and choice by dynamically determine the attention strategy. | {
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1901.02222 | 2886416400 | Natural Language Inference (NLI) is a fundamental and challenging task in Natural Language Processing (NLP). Most existing methods only apply one-pass inference process on a mixed matching feature, which is a concatenation of different matching features between a premise and a hypothesis. In this paper, we propose a ne... | Early work on the NLI task mainly uses conventional statistical methods on small-scale datasets @cite_29 @cite_18 . Recently, the neural models on NLI are based on large-scale datasets and can be categorized into two central frameworks: (i) Siamense-based framework which focuses on building sentence embeddings separate... | {
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1901.02291 | 2911208170 | Recently, a number of works have studied clustering strategies that combine classical clustering algorithms and deep learning methods. These approaches follow either a sequential way, where a deep representation is learned using a deep autoencoder before obtaining clusters with k-means, or a simultaneous way, where dee... | Within this framework, classical dimensionality reduction approaches, e.g., Principal Component Analysis (PCA), have been widely considered for the embedding task. However, the linear nature of such techniques makes it challenging to infer faithful representations of real-world data, which typically lie on highly non-l... | {
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1901.02291 | 2911208170 | Recently, a number of works have studied clustering strategies that combine classical clustering algorithms and deep learning methods. These approaches follow either a sequential way, where a deep representation is learned using a deep autoencoder before obtaining clusters with k-means, or a simultaneous way, where dee... | The deep autoencoders (DAE) have proven to be useful for dimensionality reduction @cite_16 and image denoising. In particular, the autoencoders (AE) can non-linearly transform data into a latent space. When this latent space has lower dimension than the original one @cite_16 , this can be viewed as a form of non-linear... | {
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1901.02291 | 2911208170 | Recently, a number of works have studied clustering strategies that combine classical clustering algorithms and deep learning methods. These approaches follow either a sequential way, where a deep representation is learned using a deep autoencoder before obtaining clusters with k-means, or a simultaneous way, where dee... | Since the embedding process is not guaranteed to infer representations that are suitable for the clustering task, several authors recommend to perform both tasks jointly so as to let clustering govern feature extraction and vice-versa. In @cite_1 , the authors propose a general framework, so-called DeepCluster , to int... | {
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1901.02425 | 2910681974 | Recent Salient Object Detection (SOD) systems are mostly based on Convolutional Neural Networks (CNNs). Specifically, Deeply Supervised Saliency (DSS) system has shown it is very useful to add short connections to the network and supervising on the side output. In this work, we propose a new SOD system which aims at de... | Early SOD methods are mainly based on hand-crafted features to capture saliency information, e.g. global contrast @cite_38 , multiple color spaces @cite_25 , clustering on reconstruction error @cite_34 and objectness cues @cite_33 . But traditional methods can hardly outperform CNN-based because low-level features are ... | {
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1901.02425 | 2910681974 | Recent Salient Object Detection (SOD) systems are mostly based on Convolutional Neural Networks (CNNs). Specifically, Deeply Supervised Saliency (DSS) system has shown it is very useful to add short connections to the network and supervising on the side output. In this work, we propose a new SOD system which aims at de... | One recently proposed method @cite_10 is closely related to ours because both methods exploit multiple side outputs and short connections. As shown in Figure , HED ) was proposed to detect contour, but no short connections are applied. The DSS network ) connects each layer to all the previous layers. But the large-size... | {
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1901.02425 | 2910681974 | Recent Salient Object Detection (SOD) systems are mostly based on Convolutional Neural Networks (CNNs). Specifically, Deeply Supervised Saliency (DSS) system has shown it is very useful to add short connections to the network and supervising on the side output. In this work, we propose a new SOD system which aims at de... | Another related work is @cite_33 because we both utilize the objectness cue to predict salient regions without using category information. Their method @cite_33 used various hand-crafted features to propose box candidates. Pixel-level scores are computed by applying the Gaussian process. Our method is CNN-based and we ... | {
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1901.02352 | 2910295028 | With the development of multimedia era, multi-view data is generated in various fields. Contrast with those single-view data, multi-view data brings more useful information and should be carefully excavated. Therefore, it is essential to fully exploit the complementary information embedded in multiple views to enhance ... | MSE is a good performance for multi-view dimension reduction. It can encode different features from multiple views to achieve a physically meaningful embedding. Xia el al. @cite_18 extends Laplacian Eigenmaps (LE) @cite_5 into multi-view mode and develops an architecture to learn weights for different views according t... | {
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1901.02184 | 2902503307 | In this paper, we propose to disentangle and interpret contextual effects that are encoded in a pre-trained deep neural network. We use our method to explain the gaming strategy of the alphaGo Zero model. Unlike previous studies that visualized image appearances corresponding to the network output or a neural activatio... | Visualization of filters in intermediate layers is the most direct method to analyze the knowledge hidden inside a neural network. @cite_8 @cite_24 @cite_16 @cite_28 @cite_7 @cite_35 @cite_0 showed the appearance that maximized the score of a given unit. @cite_28 used up-convolutional nets to invert CNN feature maps to... | {
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1901.02184 | 2902503307 | In this paper, we propose to disentangle and interpret contextual effects that are encoded in a pre-trained deep neural network. We use our method to explain the gaming strategy of the alphaGo Zero model. Unlike previous studies that visualized image appearances corresponding to the network output or a neural activatio... | Some studies retrieved certain units from intermediate layers of CNNs that were related to certain semantics, although the relationship between a certain semantics and each neural unit was usually convincing enough. People usually parallel the retrieved units similar to conventional mid-level features @cite_5 of images... | {
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1901.02184 | 2902503307 | In this paper, we propose to disentangle and interpret contextual effects that are encoded in a pre-trained deep neural network. We use our method to explain the gaming strategy of the alphaGo Zero model. Unlike previous studies that visualized image appearances corresponding to the network output or a neural activatio... | Model-diagnosis methods, such as the LIME @cite_10 , the SHAP @cite_14 , influence functions @cite_27 , gradient-based visualization methods @cite_25 @cite_18 , and @cite_20 extracted image regions that were responsible for network outputs. @cite_22 @cite_36 distilled knowledge from a pre-trained neural network into ex... | {
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1901.02184 | 2902503307 | In this paper, we propose to disentangle and interpret contextual effects that are encoded in a pre-trained deep neural network. We use our method to explain the gaming strategy of the alphaGo Zero model. Unlike previous studies that visualized image appearances corresponding to the network output or a neural activatio... | A new trend is to learn networks with meaningful feature representations in intermediate layers @cite_30 @cite_4 @cite_21 in a weakly-supervised or unsupervised manner. For example, capsule nets @cite_32 and interpretable RCNN @cite_11 learned interpretable middle-layer features. InfoGAN @cite_2 and @math -VAE @cite_17... | {
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1901.01774 | 2907001860 | Accurate house prediction is of great significance to various real estate stakeholders such as house owners, buyers, investors, and agents. We propose a location-centered prediction framework that differs from existing work in terms of data profiling and prediction model. Regarding data profiling, we define and capture... | House is usually treated as a heterogeneous goods, defined by a bundle of utility bearing features @cite_33 @cite_21 . Therefore, the house price can be considered as a quantitative representation of a set of these features. Over the past decades, a large amount of studies have examined the relationship between house p... | {
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1901.01774 | 2907001860 | Accurate house prediction is of great significance to various real estate stakeholders such as house owners, buyers, investors, and agents. We propose a location-centered prediction framework that differs from existing work in terms of data profiling and prediction model. Regarding data profiling, we define and capture... | Recent studies have focused on house price prediction from local views and have gradually become a serious alternative and extension of conventional house price modeling approaches. Among these studies, @cite_17 compared alternative methods for taking spatial dependence into account in house price prediction, and concl... | {
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1901.01994 | 2909857857 | Central Pattern Generators (CPGs) are biological neural circuits capable of producing coordinated rhythmic outputs in the absence of rhythmic input. As a result, they are responsible for most rhythmic motion in living organisms. This rhythmic control is broadly applicable to fields such as locomotive robotics and medic... | @cite_19 provides evidence that locomotion specific structures than general purpose MLPs can provide motion intuitions to RL environments. Brielfy, the SCN architecture is to separately learn local and global control. These are specific to locomotion tasks: the agent needs to learn global interactions and general patte... | {
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1901.01994 | 2909857857 | Central Pattern Generators (CPGs) are biological neural circuits capable of producing coordinated rhythmic outputs in the absence of rhythmic input. As a result, they are responsible for most rhythmic motion in living organisms. This rhythmic control is broadly applicable to fields such as locomotive robotics and medic... | RNNs model time-sequences well by maintaining a hidden state as a function of priors @cite_14 . Previously, RNNs have been intensively used in natural language processing because sequence prediction is best modeled by including previous context. Therefore, to leverage the added context, RNNs has also been explored loos... | {
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1901.01978 | 2909236140 | While the classic Vickrey-Clarke-Groves mechanism ensures incentive compatibility for a static one-shot game, it does not appear to be feasible to construct a dominant truth-telling mechanism for agents that are stochastic dynamic systems. The contribution of this paper is to show that for a set of LQG agents a mechani... | There are also some related works aiming at achieving budget balance for VCG mechanism. @cite_18 discusses the trade-off between budget balance and efficiency of the mechanism. Cavallo @cite_16 uses domain information regarding agent valuation spaces to achieve redistribution of much of the required transfer payments b... | {
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1901.01978 | 2909236140 | While the classic Vickrey-Clarke-Groves mechanism ensures incentive compatibility for a static one-shot game, it does not appear to be feasible to construct a dominant truth-telling mechanism for agents that are stochastic dynamic systems. The contribution of this paper is to show that for a set of LQG agents a mechani... | The problem of how to conduct bidding to achieve social welfare optimality in stochastic dynamic systems is examined in @cite_0 . It assumes that all agents are price-takers. | {
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1901.01978 | 2909236140 | While the classic Vickrey-Clarke-Groves mechanism ensures incentive compatibility for a static one-shot game, it does not appear to be feasible to construct a dominant truth-telling mechanism for agents that are stochastic dynamic systems. The contribution of this paper is to show that for a set of LQG agents a mechani... | A preliminary announcement of some of these results was presented in the conference paper @cite_19 . The layered payment structure for LQG systems is mentioned there, and incentive compatibility results are presented without proofs. The present paper contains the complete proof of incentive compatibility, and further i... | {
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1901.01805 | 2908176280 | In this paper we address the problem of multi-cue affect recognition in challenging environments such as child-robot interaction. Towards this goal we propose a method for automatic recognition of affect that leverages body expressions alongside facial expressions, as opposed to traditional methods that usually focus o... | The overwhelming majority of previous works in emotion recognition from visual cues have focused on using only faces as cues @cite_44 . Latest surveys @cite_25 @cite_37 @cite_9 highlight the need for taking into account bodily expression as additional stimuli for automatic emotion recognition systems, as well as the la... | {
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1901.01805 | 2908176280 | In this paper we address the problem of multi-cue affect recognition in challenging environments such as child-robot interaction. Towards this goal we propose a method for automatic recognition of affect that leverages body expressions alongside facial expressions, as opposed to traditional methods that usually focus o... | Gunes and Piccardi @cite_32 @cite_27 focused on combining handcrafted facial and body features for recognizing 12 different affective states in a subset of the FABO dataset @cite_4 which contains upper body affective recordings of 23 subjects. @cite_39 used Sobel filters combined with convolutional layers in the same d... | {
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1901.01805 | 2908176280 | In this paper we address the problem of multi-cue affect recognition in challenging environments such as child-robot interaction. Towards this goal we propose a method for automatic recognition of affect that leverages body expressions alongside facial expressions, as opposed to traditional methods that usually focus o... | B " a @cite_42 introduced the GEMEP (GEneva Multimodal Emotion Portrayal) corpus, the CoreSet of which includes 10 actors performing 12 emotional expression. In @cite_5 , introduce a body action and posture coding system (BAP) similar to the Facial Action Coding System (FACS) which is used for coding the human facial e... | {
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1901.01805 | 2908176280 | In this paper we address the problem of multi-cue affect recognition in challenging environments such as child-robot interaction. Towards this goal we propose a method for automatic recognition of affect that leverages body expressions alongside facial expressions, as opposed to traditional methods that usually focus o... | In @cite_17 recorded a database of 10 participants performing 8 emotions, using the same framework as the GEMEP dataset. Afterwards, they fused audio, facial and body movement features using different Bayesian classifiers for automatically recognizing the depicted emotions. In @cite_38 a two branch face-body late-fusio... | {
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1901.01805 | 2908176280 | In this paper we address the problem of multi-cue affect recognition in challenging environments such as child-robot interaction. Towards this goal we propose a method for automatic recognition of affect that leverages body expressions alongside facial expressions, as opposed to traditional methods that usually focus o... | Regarding the application of affect recognition in CRI, the necessity of empathy as a primary capability of social robots for the establishment of positive long-term human-robot interaction has been the research item of several studies @cite_16 @cite_43 . In @cite_11 presented a system that learned to perceive affectiv... | {
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1901.01760 | 2907137919 | We explore the importance of spatial contextual information in human pose estimation. Most state-of-the-art pose networks are trained in a multi-stage manner and produce several auxiliary predictions for deep supervision. With this principle, we present two conceptually simple and yet computational efficient modules, n... | The key of human pose estimation lies in joint detection and spatial relation configuration. Previous human pose estimation methods can be divided into two groups. The first is to learn feature representation using powerful CNN. These methods detect body joint location directly or predict the score maps for body joints... | {
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1901.01760 | 2907137919 | We explore the importance of spatial contextual information in human pose estimation. Most state-of-the-art pose networks are trained in a multi-stage manner and produce several auxiliary predictions for deep supervision. With this principle, we present two conceptually simple and yet computational efficient modules, n... | The other group focuses on modeling spatial relationship between body joints. The pictorial structures @cite_9 modeled spatial deformation by designing pairwise terms between different joints. To deal with human poses with large variation, a mixture model is learned for each joint. Yang al @cite_52 used a part mixture ... | {
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1901.01760 | 2907137919 | We explore the importance of spatial contextual information in human pose estimation. Most state-of-the-art pose networks are trained in a multi-stage manner and produce several auxiliary predictions for deep supervision. With this principle, we present two conceptually simple and yet computational efficient modules, n... | Later methods @cite_36 @cite_27 modeled structures via CNN. Tompson al @cite_27 utilized the Markov Random Field (MRF) to model distribution of body parts. Convolutional priors were used in defining the pairwise terms of joints. The method of @cite_53 utilized geometrical transform kernels to capture relation of joints... | {
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1901.01760 | 2907137919 | We explore the importance of spatial contextual information in human pose estimation. Most state-of-the-art pose networks are trained in a multi-stage manner and produce several auxiliary predictions for deep supervision. With this principle, we present two conceptually simple and yet computational efficient modules, n... | Previous work on feature learning for graph-structure can be divided into two categories. One direction is to apply CNN to graphs. Based on graph Laplacian, methods of @cite_50 @cite_39 @cite_26 applied CNN to spectral domain. In order to operate CNN directly on graph, the method of @cite_21 used a special hash functio... | {
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1901.02035 | 2908990317 | We propose a generalized decision-theoretic system for a heterogeneous team of autonomous agents who are tasked with online identification of phenotypically expressed stress in crop fields.. This system employs four distinct types of agents, specific to four available sensor modalities: satellites (Layer 3), uninhabite... | The most recent advance in precision agriculture is the FarmBeats initiative driven by Microsoft AI @cite_15 , in which a variety of network-accessible sensor modalities, including soil water potential sensors and AUAVs, are arranged to provide automated and targeted water intervention. This methodology is powerful for... | {
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1901.02035 | 2908990317 | We propose a generalized decision-theoretic system for a heterogeneous team of autonomous agents who are tasked with online identification of phenotypically expressed stress in crop fields.. This system employs four distinct types of agents, specific to four available sensor modalities: satellites (Layer 3), uninhabite... | Concerning the goal of disease identification and intervention, the wide array of contemporary efforts leverage phenotypic expressions of stress largely via thermal detection @cite_12 and are often specific to the expression from a specific disease @cite_4 . What remains is a generalized model that encompasses the vari... | {
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1901.01977 | 2953386523 | We propose a hybrid approach aimed at improving the sample efficiency in goal-directed reinforcement learning. We do this via a two-step mechanism where firstly, we approximate a model from Model-Free reinforcement learning. Then, we leverage this approximate model along with a notion of reachability using Mean First P... | Earlier works have demonstrated various approaches to solve goal-directed reinforcement learning problem. presented a solution to solve GDRLP for an indoor unknown environment. Firstly, using temporal difference learning method, they find an initial solution to reach the goal and then improve upon the initial solution ... | {
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1901.01977 | 2953386523 | We propose a hybrid approach aimed at improving the sample efficiency in goal-directed reinforcement learning. We do this via a two-step mechanism where firstly, we approximate a model from Model-Free reinforcement learning. Then, we leverage this approximate model along with a notion of reachability using Mean First P... | Another interesting research direction focused on reducing the size of problem space in MB approaches. proposed structured reachability analysis of MDPs in order to remove variables from problem description, thereby reducing the size of MDP and eventually making it easier to solve @cite_14 . It is therefore very intuit... | {
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1901.01977 | 2953386523 | We propose a hybrid approach aimed at improving the sample efficiency in goal-directed reinforcement learning. We do this via a two-step mechanism where firstly, we approximate a model from Model-Free reinforcement learning. Then, we leverage this approximate model along with a notion of reachability using Mean First P... | Along similar direction, several prior works focused on devising a model as an initialization for MF component @cite_18 @cite_23 . One of the challenges that this leads to is the inaccuracies in the model which cause the issue of model bias. A suggested solution to overcome model bias is to directly train the dynamics ... | {
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1901.01769 | 2908459497 | The first six months of 2018 have seen cryptocurrency thefts of $761 million, and the technology is also the latest and greatest tool for money laundering. This increase in crime has caused both researchers and law enforcement to look for ways to trace criminal proceeds. Although tracing algorithms have improved recent... | A more mature system was BitConeView, presented by Battista and Donato in 2015 @cite_4 . This was among the first to provide a sensible GUI to inspect how a particular UTXO propagated through the network. In order to explain what it means for money to move, the authors came up with purity' -- basically a version of hai... | {
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1901.01769 | 2908459497 | The first six months of 2018 have seen cryptocurrency thefts of $761 million, and the technology is also the latest and greatest tool for money laundering. This increase in crime has caused both researchers and law enforcement to look for ways to trace criminal proceeds. Although tracing algorithms have improved recent... | devised a graph visualization of blockchain that allowed them to detect laundering activity and several denial-of-service attacks @cite_6 . Unlike previous approaches, they made use of top-down system-wide visualization to understand transaction patterns. The follow-up paper from proposed an extension to a global view,... | {
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1901.01769 | 2908459497 | The first six months of 2018 have seen cryptocurrency thefts of $761 million, and the technology is also the latest and greatest tool for money laundering. This increase in crime has caused both researchers and law enforcement to look for ways to trace criminal proceeds. Although tracing algorithms have improved recent... | Unlike BitConduit and similar systems, we are not doing any actor characterization in our visualisation tool @cite_9 . The generation of graph colours is exogenous, relying on external theft reports or of software that analyses patterns of mixes, ransomware and other undesirable activity. | {
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1901.01939 | 2907034307 | The main goal of network pruning is imposing sparsity on the neural network by increasing the number of parameters with zero value in order to reduce the architecture size and the computational speedup. In most of the previous research works, sparsity is imposed stochastically without considering any prior knowledge of... | Network compression for parameter reduction has been of great interest for a long time and a large number of research efforts are dedicated to it. In @cite_10 @cite_50 @cite_43 @cite_16 , network pruning has been performed with a significant reduction in parameters, although they suffer from computational inefficiency ... | {
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1901.01939 | 2907034307 | The main goal of network pruning is imposing sparsity on the neural network by increasing the number of parameters with zero value in order to reduce the architecture size and the computational speedup. In most of the previous research works, sparsity is imposed stochastically without considering any prior knowledge of... | In @cite_35 @cite_12 @cite_26 , pruning the unimportant parts of the structure This can be neurons in fully-connected layers or channels filters in convolutional layers. rather than simple weight pruning has been proposed and significant computational speedup has been achieved. However, for the aforementioned methods, ... | {
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1901.01939 | 2907034307 | The main goal of network pruning is imposing sparsity on the neural network by increasing the number of parameters with zero value in order to reduce the architecture size and the computational speedup. In most of the previous research works, sparsity is imposed stochastically without considering any prior knowledge of... | In this paper, the goal is to impose the sparsity in an accurate and interpretable way using the attention mechanism. So far, attention-based deep architectures has been proposed for image @cite_39 @cite_38 @cite_15 @cite_46 and speech domains @cite_41 @cite_25 @cite_49 , as well as machine translation @cite_8 @cite_47... | {
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1901.01575 | 2907202993 | Subject matching performance in iris biometrics is contingent upon fast, high-quality iris segmentation. In many cases, iris biometrics acquisition equipment takes a number of images in sequence and combines the segmentation and matching results for each image to strengthen the result. To date, segmentation has occurre... | As the first step of iris recognition, iris segmentation has been approached in a variety of ways. Early and, for many years, dominant methods relied upon the circular structure of both the pupil and the iris, as proposed by Daugman @cite_5 . In addition to circular approximations of iris boundaries, those methods dete... | {
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1901.01575 | 2907202993 | Subject matching performance in iris biometrics is contingent upon fast, high-quality iris segmentation. In many cases, iris biometrics acquisition equipment takes a number of images in sequence and combines the segmentation and matching results for each image to strengthen the result. To date, segmentation has occurre... | Subsequent approaches departed from circular approximations, and in many cases were inspired by Daugman's suggestion to use a Fourier series to provide a more complex model of the iris boundary @cite_24 . Arvacheh and Tizhoosh @cite_26 proposed using active contour models to detect the pupillary and limbic boundaries o... | {
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1901.01575 | 2907202993 | Subject matching performance in iris biometrics is contingent upon fast, high-quality iris segmentation. In many cases, iris biometrics acquisition equipment takes a number of images in sequence and combines the segmentation and matching results for each image to strengthen the result. To date, segmentation has occurre... | Other methods approached iris segmentation from a localization perspective. Luengo @cite_9 proposed using mathematical morphology to extract the outer boundaries of both the pupil and the iris. They dealt with occlusions by simply removing portions of the segmentation which likely contained parts of the eyelid, eyelash... | {
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1901.01575 | 2907202993 | Subject matching performance in iris biometrics is contingent upon fast, high-quality iris segmentation. In many cases, iris biometrics acquisition equipment takes a number of images in sequence and combines the segmentation and matching results for each image to strengthen the result. To date, segmentation has occurre... | More recently, iris segmentation has moved away from these classical approaches toward machine learning oriented methods. Li @cite_6 proposed a method which uses k-means clustering to detect the outer boundary of the iris. In a similar fashion, Sahmoud and Abuhaiba @cite_19 proposed using k-means clustering to determin... | {
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1901.01575 | 2907202993 | Subject matching performance in iris biometrics is contingent upon fast, high-quality iris segmentation. In many cases, iris biometrics acquisition equipment takes a number of images in sequence and combines the segmentation and matching results for each image to strengthen the result. To date, segmentation has occurre... | Recent advances in deep learning-based segmentation have resulted in many applications of convolutional neural network architectures to iris segmentation. Jalilian and Uhl @cite_20 proposed to use several types of convolutional encoder-decoder networks and reported better performance for deep learning-based approaches ... | {
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1901.01574 | 2949199146 | This work systematically analyzes the smoothing effect of vocabulary reduction for phrase translation models. We extensively compare various word-level vocabularies to show that the performance of smoothing is not significantly affected by the choice of vocabulary. This result provides empirical evidence that the stand... | Other types of features are also trained on word-level labels, e.g. hierarchical reordering features @cite_3 , an @math -gram-based translation model @cite_4 , and sparse word pair features @cite_5 . The first and the third are trained with a large-scale discriminative training algorithm. | {
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1901.01466 | 2949875363 | Statistical spoken dialogue systems usually rely on a single- or multi-domain dialogue model that is restricted in its capabilities of modelling complex dialogue structures, e.g., relations. In this work, we propose a novel dialogue model that is centred around entities and is able to model relations as well as multipl... | The proposed CEDM achieves this by modelling state information about conversational entities instead of domains. More precisely, it models separate states about the objects (e.g., the hotel or restaurant) and the relations. Previous work on dialogue modelling already incorporated the idea of objects or entities to be t... | {
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1901.01544 | 2906731962 | It is inevitable to train large deep learning models on a large-scale cluster equipped with accelerators system. Deep gradient compression would highly increase the bandwidth utilization and speed up the training process but hard to implement on ring structure. In this paper, we find that redundant gradient and gradien... | Researchers have proposed many approaches to accelerate distributed deep learning training process. Distributed synchronous Stochastic Gradient Descent (SSGD) is commonly adopted solution for parallelize the tasks across machines. Message exchange algorithm would also speed up training process by make full use of the c... | {
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1901.01711 | 2906895564 | Matrix completion focuses on recovering a matrix from a small subset of its observed elements, and has already gained cumulative attention in computer vision. Many previous approaches formulate this issue as a low-rank matrix approximation problem. Recently, a truncated nuclear norm has been presented as a surrogate of... | Instead of standard nuclear norm, some researches devise the variants of nuclear norm to improve its performance. Oh @cite_20 proposed the partial sum minimization of singular values (PSSV) method, to replace the traditional nuclear norm in RPCA. It implicitly expected a soft constraint of the target rank. The objectiv... | {
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1901.01711 | 2906895564 | Matrix completion focuses on recovering a matrix from a small subset of its observed elements, and has already gained cumulative attention in computer vision. Many previous approaches formulate this issue as a low-rank matrix approximation problem. Recently, a truncated nuclear norm has been presented as a surrogate of... | Similarly, the joint Schatten- @math norm and @math norm @cite_23 was used to substitute the rank function and enhance the robustness to outliers. The @math norm of a vector @math is defined as @math . The definition of Schatten- @math norm ( @math ) of a matrix @math is where @math is the @math -th singular value of @... | {
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1901.01711 | 2906895564 | Matrix completion focuses on recovering a matrix from a small subset of its observed elements, and has already gained cumulative attention in computer vision. Many previous approaches formulate this issue as a low-rank matrix approximation problem. Recently, a truncated nuclear norm has been presented as a surrogate of... | The TNNR was first presented by Hu @cite_15 , which approximated to the rank function better than the nuclear norm. In contrast to treating all singular values together, the TNNR leaves out the largest @math singular values, and tries to minimize the smallest @math singular values, where @math , @math are the dimension... | {
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1901.01711 | 2906895564 | Matrix completion focuses on recovering a matrix from a small subset of its observed elements, and has already gained cumulative attention in computer vision. Many previous approaches formulate this issue as a low-rank matrix approximation problem. Recently, a truncated nuclear norm has been presented as a surrogate of... | In Step 2, @math is obtained by solving the following sub-problem: Two typical optimization approaches were developed in @cite_15 to minimize , i.e., the alternating direction method of multipliers and the accelerated proximal gradient line search method. However, both of them require numerous iterations to converge an... | {
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1901.01711 | 2906895564 | Matrix completion focuses on recovering a matrix from a small subset of its observed elements, and has already gained cumulative attention in computer vision. Many previous approaches formulate this issue as a low-rank matrix approximation problem. Recently, a truncated nuclear norm has been presented as a surrogate of... | Recently, a variety of studies were derived from the TNNR. Hong @cite_4 combined the truncated nuclear norm with the online RPCA @cite_25 , to promote low dimensional subspace estimation. Motion capture data completion @cite_21 demonstrated the validity via integrating it with the truncated nuclear norm. Large scale mu... | {
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1901.01517 | 2908001447 | The effectiveness of many optimal network control algorithms (e.g., BackPressure) relies on the premise that all of the nodes are fully controllable. However, these algorithms may yield poor performance in a partially-controllable network where a subset of nodes are uncontrollable and use some unknown policy. Such a pa... | In terms of technical tools, our work leverages techniques from reinforcement learning, since a partially-controllable network with queue-dependent uncontrollable policy can be formulated as an MDP with unknown dynamics. Over the past few decades, many reinforcement learning algorithms have been developed, such as Q-le... | {
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1901.01577 | 2951502268 | We address for the first time unsupervised training for a translation task with hundreds of thousands of vocabulary words. We scale up the expectation-maximization (EM) algorithm to learn a large translation table without any parallel text or seed lexicon. First, we solve the memory bottleneck and enforce the sparsity ... | Early work on unsupervised sequence learning was mainly for , a combinatorial problem of matching input-output symbols with 1:1 or homophonic assumption @cite_10 @cite_4 @cite_15 . relaxes this assumption to allow many-to-many mapping, while the vocabulary is usually limited to a few thousand types @cite_11 @cite_1 @ci... | {
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1901.01577 | 2951502268 | We address for the first time unsupervised training for a translation task with hundreds of thousands of vocabulary words. We scale up the expectation-maximization (EM) algorithm to learn a large translation table without any parallel text or seed lexicon. First, we solve the memory bottleneck and enforce the sparsity ... | There has been several attempts to improve the scalability of decipherment methods, which are however not applicable to 100k-vocabulary translation scenarios. For EM-based decipherment, and accelerate hypothesis expansions but do not explicitly solve the memory issue for a large lexicon table. Count-based Bayesian infe... | {
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1901.01577 | 2951502268 | We address for the first time unsupervised training for a translation task with hundreds of thousands of vocabulary words. We scale up the expectation-maximization (EM) algorithm to learn a large translation table without any parallel text or seed lexicon. First, we solve the memory bottleneck and enforce the sparsity ... | Our problem is also related to with hidden Markov model (HMM). To the best of our knowledge, there is no published work on HMM training for a 100k-size discrete space. HMM taggers are often integrated with sparse priors @cite_16 @cite_5 , which is not readily possible in a large vocabulary setting due to the memory bot... | {
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1901.01538 | 2743442682 | Nowadays, web services play a major role in the development of enterprise applications. Many such applications are now developed using a service-oriented architecture (SOA), where microservices is one of its most popular kind. A RESTful web service will provide data via an API over the network using HTTP, possibly inte... | Canfora and Di Penta provided a discussion on the trends and challenges of SOA testing @cite_36 . Afterwards, they provided a more in detail survey @cite_2 . There are different kinds of testing for SOA (unit, integration, regression, robustness, etc.), which also depend on which stakeholders are involved, e.g., servic... | {
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1901.01538 | 2743442682 | Nowadays, web services play a major role in the development of enterprise applications. Many such applications are now developed using a service-oriented architecture (SOA), where microservices is one of its most popular kind. A RESTful web service will provide data via an API over the network using HTTP, possibly inte... | Successively, @cite_1 carried out a survey as well on SOA testing, in which 177 papers were analysed. One of the interesting results of this survey is that, although the number of papers on SOA testing has been increasing throughout the years, only 11 | {
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1901.01538 | 2743442682 | Nowadays, web services play a major role in the development of enterprise applications. Many such applications are now developed using a service-oriented architecture (SOA), where microservices is one of its most popular kind. A RESTful web service will provide data via an API over the network using HTTP, possibly inte... | A lot of the work in the literature has been focusing on black-box testing of SOAP web services described with WSDL (Web Services Description Language). Different strategies have been proposed, like for example @cite_6 @cite_37 @cite_13 @cite_19 @cite_7 @cite_18 . If those services also provide a semantic model (e.g., ... | {
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1901.01538 | 2743442682 | Nowadays, web services play a major role in the development of enterprise applications. Many such applications are now developed using a service-oriented architecture (SOA), where microservices is one of its most popular kind. A RESTful web service will provide data via an API over the network using HTTP, possibly inte... | Black-box testing has its advantages, but also its limitations. Coverage measures could improve the generation of tests but, often, web services are remote and there is no access to their source code. For testing purposes, @cite_20 proposed an approach in which feedback on code coverage is provided as a service, withou... | {
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1901.01538 | 2743442682 | Nowadays, web services play a major role in the development of enterprise applications. Many such applications are now developed using a service-oriented architecture (SOA), where microservices is one of its most popular kind. A RESTful web service will provide data via an API over the network using HTTP, possibly inte... | Regarding RESTful web services, Chakrabarti and Kumar @cite_11 provided a testing framework in which automatic generation test cases corresponding to an exhaustive list of all valid combinations of query parameter values''. @cite_32 proposed a technique to generate tests for RESTful API based on an idealised, property-... | {
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1901.01538 | 2743442682 | Nowadays, web services play a major role in the development of enterprise applications. Many such applications are now developed using a service-oriented architecture (SOA), where microservices is one of its most popular kind. A RESTful web service will provide data via an API over the network using HTTP, possibly inte... | Regarding the usage of evolutionary techniques for testing web services, Di @cite_35 proposed an approach for testing Service Level Agreements (SLA). Given an API in which a contract is formally defined stating for example, that the service provider guarantees to the service consumer a response time less than 30 ms and... | {
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1901.01628 | 2908399268 | Byte-addressable persistent memories (PM) has finally made their way into production. An important and pressing problem that follows is how to deploy them in existing datacenters. One viable approach is to attach PM as self-contained devices to the network as disaggregated persistent memory, or DPM. DPM requires no cha... | NAM-DB @cite_23 @cite_17 is a -based database system that uses one-sided communication for both read and write. Infiniswap @cite_66 is an RDMA-based remote memory paging system. Remote regions @cite_28 is a system that exposes remote memory as files that other host servers can access (through a file system interface). ... | {
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1901.01628 | 2908399268 | Byte-addressable persistent memories (PM) has finally made their way into production. An important and pressing problem that follows is how to deploy them in existing datacenters. One viable approach is to attach PM as self-contained devices to the network as disaggregated persistent memory, or DPM. DPM requires no cha... | Mojim @cite_19 , Hotpot @cite_49 , and Octopus @cite_67 are three recent distributed systems. Mojim @cite_19 is the first system that targets using in distributed, datacenter environments. Mojim provides an efficient, RDMA-based, asynchronous replication mechanism for , to make it more reliable and available. Hotpot @c... | {
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"mid": [
"2090204040",
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"abstract": [
"Next-generation non-volatile memories (NVMs) promise DRAM-like performance, persistence, and high density. They can attach directly to processors to form non-vola... |
1901.01628 | 2908399268 | Byte-addressable persistent memories (PM) has finally made their way into production. An important and pressing problem that follows is how to deploy them in existing datacenters. One viable approach is to attach PM as self-contained devices to the network as disaggregated persistent memory, or DPM. DPM requires no cha... | ReFlex @cite_70 is a software-based system builds on IX @cite_15 and exposes a logical block interface for users to access remote Flash with nearly identical performance as accessing local Flash. RAMCloud @cite_74 is a remote key-value storage system that stores a full copy of all data in DRAM and backups in disks or S... | {
"cite_N": [
"@cite_70",
"@cite_15",
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],
"abstract": [
"Remote access to NVMe Flash enables flexible scaling and high utilization of Flash capacity and IOPS within a datacenter. Howeve... |
1901.01649 | 2964182391 | Predicting the future is a fantasy but practicality work. It is the key component to intelligent agents, such as self-driving vehicles, medical monitoring devices and robotics. In this work, we consider generating unseen future frames from previous observations, which is notoriously hard due to the uncertainty in frame... | Our networks makes full use of the advantages of the above model and avoids their weakness as much as possible. Our approach exploit simple loss to make the machine learn the coarse predicted frame and Difference Guide(DG) information which contain the variation of foreground and background from the previous frames to ... | {
"cite_N": [
"@cite_0",
"@cite_26"
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"abstract": [
"Generative Adversarial Nets [8] were recently introduced as a novel way to train generative models. In this work we introduce the conditional version of generative adversarial nets, which can be co... |
1901.01488 | 2907012035 | Selectivity estimation remains a critical task in query optimization even after decades of research and industrial development. Optimizers rely on accurate selectivities when generating execution plans. They maintain a large range of statistical synopses for efficiently estimating selectivities. Nonetheless, small erro... | A large variety of synopses have been proposed for selectivity estimation. Many of them are also used in approximate query processing @cite_10 . Histograms are the most frequently implemented in practice. While they provide accurate estimation for a single attribute, they cannot handle correlations between attributes a... | {
"cite_N": [
"@cite_8",
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"mid": [
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"1581547316"
],
"abstract": [
"As a result of decades of research and industrial development, modern query optimizers are complex software... |
1901.01488 | 2907012035 | Selectivity estimation remains a critical task in query optimization even after decades of research and industrial development. Optimizers rely on accurate selectivities when generating execution plans. They maintain a large range of statistical synopses for efficiently estimating selectivities. Nonetheless, small erro... | The LEO optimizer @cite_6 monitors relational operator selectivities during query execution. These exact values are continuously compared with the synopses-based estimates used during optimization. When the two have a large deviation, synopses are adjusted to automatically correct their error for future queries. Mid-qu... | {
"cite_N": [
"@cite_14",
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],
"mid": [
"1975731516",
"2131413649"
],
"abstract": [
"For a number of reasons, even the best query optimizers can very often produce sub-optimal query execution plans, leading to a significant degradation of performance. This is especially true in dat... |
1901.01535 | 2951394060 | In this paper, we consider the problem of reconstructing a dense 3D model using images captured from different views. Recent methods based on convolutional neural networks (CNN) allow learning the entire task from data. However, they do not incorporate the physics of image formation such as perspective geometry and occ... | 3D reconstruction methods can be roughly categorized into model-based and learning-based approaches, which learn the task from data. As a thorough survey on 3D reconstruction techniques is beyond the scope of this paper, we discuss only the most related approaches and refer to @cite_45 @cite_13 @cite_0 for a more thoro... | {
"cite_N": [
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"mid": [
"",
"2033819227",
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],
"abstract": [
"",
"From the Publisher: A basic problem in computer vision is to understand the structure of a real world scene given several images of it. Recent major developments in ... |
1901.01535 | 2951394060 | In this paper, we consider the problem of reconstructing a dense 3D model using images captured from different views. Recent methods based on convolutional neural networks (CNN) allow learning the entire task from data. However, they do not incorporate the physics of image formation such as perspective geometry and occ... | Learning-based 3D Reconstruction The development of large 3D shape databases @cite_41 @cite_10 has fostered the development of learning based solutions @cite_16 @cite_42 @cite_30 @cite_12 to 3D reconstruction. Choy al @cite_16 propose a unified framework for single and multi-view reconstruction by using a 3D recurrent ... | {
"cite_N": [
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... |
1901.01535 | 2951394060 | In this paper, we consider the problem of reconstructing a dense 3D model using images captured from different views. Recent methods based on convolutional neural networks (CNN) allow learning the entire task from data. However, they do not incorporate the physics of image formation such as perspective geometry and occ... | As most of the aforementioned methods solve the 3D reconstruction problem via recognizing the scene content, they are only applicable to object reconstruction and do not generalize well to novel object categories or full scenes. Towards a more general learning based model for 3D reconstruction, @cite_32 @cite_6 propose... | {
"cite_N": [
"@cite_32",
"@cite_6"
],
"mid": [
"2963966978",
"2964243776"
],
"abstract": [
"We present a learnt system for multi-view stereopsis. In contrast to recent learning based methods for 3D reconstruction, we leverage the underlying 3D geometry of the problem through feature proje... |
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