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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1907.07349 | 2960672606 | Edge computing in the Internet of Things brings applications and content closer to the users by introducing an additional computational layer at the network infrastructure, between cloud and the resource-constrained data producing devices and user equipment. This way, the opportunistic nature of the operational environ... | A related problem, after the edge server placement has been completed, is how the runtime online workload is handled. This virtual machine allocation problem was considered in a number of studies, e.g. @cite_10 @cite_7 @cite_23 @cite_46 @cite_26 , but this online problem is outside of the focus on this paper. | {
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"Fog computing, an extension of cloud computing services to the edge of the network to decrease ... |
1907.07349 | 2960672606 | Edge computing in the Internet of Things brings applications and content closer to the users by introducing an additional computational layer at the network infrastructure, between cloud and the resource-constrained data producing devices and user equipment. This way, the opportunistic nature of the operational environ... | We approximate the network topology using the geospatial distances, where proximity is measured using squared Euclidean distances. This produces k-means type clustering with spherical-like clusters with centralized cluster heads @cite_19 . This results in a star-like topology with spatially centralized edge servers in ... | {
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"The capacitated centred clustering problem (CCCP) consists of defining a set of clusters with limited capacity and maximum proper similarity per cluster. Each cluster is composed of individuals from whom we can compute a centre va... |
1907.07349 | 2960672606 | Edge computing in the Internet of Things brings applications and content closer to the users by introducing an additional computational layer at the network infrastructure, between cloud and the resource-constrained data producing devices and user equipment. This way, the opportunistic nature of the operational environ... | With limited server capacity, the access point workload resulting from clustering may exceed their capacity, as demonstrated in related work @cite_30 . Therefore, assigning an access point to exactly one server may reduce the QoS. Thus, a sharing of workload between servers should be enabled. So forth, we refer to the ... | {
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"Mobile edge computing (MEC) is an emerging technology that aims at pushing applications and content close to the users (e.g., at base stations, access points, and aggregation networks) to reduce latency, improve quality of experie... |
1907.07274 | 2958781163 | Multi-label classification plays a momentous role in perceiving intricate contents of an aerial image and triggers several related studies over the last years. However, most of them deploy few efforts in exploiting label relations, while such dependencies are crucial for making accurate predictions. Although an LSTM la... | Zegeye and Demir @cite_38 propose a multi-label active learning framework using a multi-label support vector machine (SVM), relying on both the multi-label uncertainty and diversity. @cite_15 introduce a spatial and structure SVM for multi-label classification by considering spatial relations between a given patch and ... | {
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"This paper presents a novel multi-label active learning (MLAL) technique in the framework of multi-label remote sensing (RS) image scene classification problems. T... |
1907.07274 | 2958781163 | Multi-label classification plays a momentous role in perceiving intricate contents of an aerial image and triggers several related studies over the last years. However, most of them deploy few efforts in exploiting label relations, while such dependencies are crucial for making accurate predictions. Although an LSTM la... | With the development of computational resources and deep learning, very recent approaches mainly resort to deep networks for multi-label classification. @cite_25 , the authors make use of a standard CNN architecture to extract feature representations and then feed them into a multi-label classification layer, which is ... | {
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"Abstract Aerial image classification is of great significance in the remote sensing community, and many researches have been condu... |
1907.07381 | 2960520355 | Most network data are collected from only partially observable networks with both missing nodes and edges, for example due to limited resources and privacy settings specified by users on social media. Thus, it stands to the reason that inferring the missing parts of the networks by performing completion should precede ... | Network completion. Observing a partial sample of a network and inferring the remainder of the network is referred to as network completion . As the most influential study, KronEM, an approach based on Kronecker graphs to solving the network completion problem by applying the expectation-maximization (EM) algorithm, wa... | {
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1907.07381 | 2960520355 | Most network data are collected from only partially observable networks with both missing nodes and edges, for example due to limited resources and privacy settings specified by users on social media. Thus, it stands to the reason that inferring the missing parts of the networks by performing completion should precede ... | Discussions. Despite these contributions, there has been no prior work in the literature that exploits the power of deep generative models in the context of network completion. We find that generative graph models themselves such as GraphRNN can be used as a network completion method with a nontrivial extra task. More ... | {
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"A linear programming (LP) approach is proposed for the weighted graph matching problem. A linear program is obtained by formulating the graph matching problem in L sub 1 norm and then transforming the resulting quadratic optimizat... |
1907.07157 | 2958440724 | Privacy has raised considerable concerns recently, especially with the advent of information explosion and numerous data mining techniques to explore the information inside large volumes of data. In this context, a new distributed learning paradigm termed federated learning becomes prominent recently to tackle the priv... | The transfer of data will bring the problem of data leakage @cite_0 . Consequently, decentralized methods (i.e., data is only stored locally) are used to process the data and then the risk of data leakage is reduced @cite_16 . Some works use encryption-based federated learning frameworks, like homomorphic encryption @c... | {
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1907.07157 | 2958440724 | Privacy has raised considerable concerns recently, especially with the advent of information explosion and numerous data mining techniques to explore the information inside large volumes of data. In this context, a new distributed learning paradigm termed federated learning becomes prominent recently to tackle the priv... | Federated learning is a new distributed learning paradigm proposed recently to utilize the user-end computing resources and preserve user's privacy by transmitting only model parameters, instead of raw data, to the server @cite_12 @cite_2 . In federated learning, a general model will be firstly trained, and then the mo... | {
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1907.07157 | 2958440724 | Privacy has raised considerable concerns recently, especially with the advent of information explosion and numerous data mining techniques to explore the information inside large volumes of data. In this context, a new distributed learning paradigm termed federated learning becomes prominent recently to tackle the priv... | Anomaly detection @cite_9 is the identification of events or observations that do not match the expected pattern or other items in the dataset (i.e., outliers) during data mining. Outliers can be divided into point exceptions, context exceptions, and collective exceptions @cite_4 . Anomaly detection methods include SMO... | {
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1907.07263 | 2957764552 | Optimizing caching locations of popular content has received significant research attention over the last few years. This paper targets the optimization of the caching locations by proposing a novel transformation of the optimization problem to a grey-scale image that is applied to a deep convolutional neural network (... | The proliferation of popular content on the Internet created a massive amount of aggregate data that need to be transported across congested links. Bringing popular content closer to the end users via caching, in order to ease congestion episodes and increase user experience, has received significant research attention... | {
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"Abstract Today’s networks are filled with a massive and ever-growing variety of network functions that coupled with proprietary devices, which leads to network ossi... |
1901.05807 | 2963179857 | In many robotic applications, especially for the autonomous driving, understanding the semantic information and the geometric structure of surroundings are both essential. Semantic 3D maps, as a carrier of the environmental knowledge, are then intensively studied for their abilities and applications. However, it is sti... | Depth prediction for scene understanding used to heavily rely on stereo vision @cite_8 @cite_10 @cite_12 . Recent studies have been made progress in scene geometric understanding from the monocular camera. An encoder-decoder architecture @cite_14 based on ResNet is proposed by , which performs residual learning to pred... | {
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1901.05807 | 2963179857 | In many robotic applications, especially for the autonomous driving, understanding the semantic information and the geometric structure of surroundings are both essential. Semantic 3D maps, as a carrier of the environmental knowledge, are then intensively studied for their abilities and applications. However, it is sti... | Multi-task learning techniques are designed to use the transfer feature between different tasks by jointly predict labels from a single model. Multi-task networks are adopted in the face attribute estimation, the contour detection, the semantic segmentation @cite_4 , etc. propose a network @cite_0 which combines scene ... | {
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"Most approaches for instance-aware semantic labeling traditionally focus on accuracy. Other aspects like runtime and memory footprint are arguably as important for real-time ... |
1901.05807 | 2963179857 | In many robotic applications, especially for the autonomous driving, understanding the semantic information and the geometric structure of surroundings are both essential. Semantic 3D maps, as a carrier of the environmental knowledge, are then intensively studied for their abilities and applications. However, it is sti... | Semantic reconstruction can be basically divided into two categories. The first kind of methods are inheritors of 2D semantic segmentation results @cite_13 @cite_16 . For monocular-based reconstructions, propose an approach @cite_13 to jointly infer geometric structure and 3D semantic labeling with a CRF model. The exp... | {
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1901.05807 | 2963179857 | In many robotic applications, especially for the autonomous driving, understanding the semantic information and the geometric structure of surroundings are both essential. Semantic 3D maps, as a carrier of the environmental knowledge, are then intensively studied for their abilities and applications. However, it is sti... | Superpixel segmentation @cite_15 @cite_6 has been applied to promote stereo matching results. The matching algorithm of @cite_10 called the SPS-st, whose formulation is based on the slanted-plane model with plain-fitting technique. To reduce the memory of large-scale reconstruction while maintaining pixel-level details... | {
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"In this paper we propose a slanted plane model for jointly recovering an image segmentation, a dense depth estimate as well as boundary labels (such as occlusion bound... |
1901.05574 | 2963924969 | Deep Recurrent Neural Network (RNN) has gained popularity in many sequence classification tasks. Beyond predicting a correct class for each data instance, data scientists also want to understand what differentiating factors in the data have contributed to the classification during the learning process. We present a vis... | Many visualization techniques have been developed to facilitate the DNN model building process, covering domains such as image understanding @cite_18 @cite_5 and natural language processing @cite_7 . Techniques such as hierarchical correlation matrices @cite_2 , edge-bundled DAG @cite_9 , parallel coordinates @cite_14 ... | {
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1901.05574 | 2963924969 | Deep Recurrent Neural Network (RNN) has gained popularity in many sequence classification tasks. Beyond predicting a correct class for each data instance, data scientists also want to understand what differentiating factors in the data have contributed to the classification during the learning process. We present a vis... | Multivariate data visualization have been developed in numerous fields of analysis @cite_9 . We summarize the related work based on the visualization layout. arrange variables in matrices to benefit the pairwise comparison of attribute relationships. GPLOM @cite_23 extends Scatterplot Matrix @cite_26 to generalized plo... | {
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1901.05574 | 2963924969 | Deep Recurrent Neural Network (RNN) has gained popularity in many sequence classification tasks. Beyond predicting a correct class for each data instance, data scientists also want to understand what differentiating factors in the data have contributed to the classification during the learning process. We present a vis... | We summarize temporal sequence visualizations into the following categories: features in the visualization of events transfer. Alluvial diagrams @cite_10 reveal how network structures change over time. Outflow @cite_25 visualizes temporal event sequences in pathways that are similar to parallel coordinates. emphasizes ... | {
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1901.05856 | 2909668595 | This paper proposes a new reinforcement learning (RL) algorithm that enhances exploration by amplifying the imitation effect (AIE). This algorithm consists of self-imitation learning and random network distillation algorithms. We argue that these two algorithms complement each other and that combining these two algorit... | Experience replay @cite_17 is a technique for exploiting past experiences, and Deep Q-Network (DQN) has exhibited human-level performance in Atari games using this technique @cite_8 @cite_6 . Prioritized experience replay @cite_4 is a method for sampling prior experience based on temporal difference. ACER @cite_2 and R... | {
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1901.05856 | 2909668595 | This paper proposes a new reinforcement learning (RL) algorithm that enhances exploration by amplifying the imitation effect (AIE). This algorithm consists of self-imitation learning and random network distillation algorithms. We argue that these two algorithms complement each other and that combining these two algorit... | Exploration has been the main challenging issue for RL, and many studies have proposed methods to enhance exploration. Count-based exploration bonus @cite_30 is an intuitive and effective exploration method in which an agent receives a bonus if the agent visits a novel state, and the bonus decreases if the agent visits... | {
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1901.05856 | 2909668595 | This paper proposes a new reinforcement learning (RL) algorithm that enhances exploration by amplifying the imitation effect (AIE). This algorithm consists of self-imitation learning and random network distillation algorithms. We argue that these two algorithms complement each other and that combining these two algorit... | However, the prediction error has a stochastic characteristic because the target function is stochastic. In addition, the architecture of the predictor network is too limited to generalize the state of the environment. To solve these problems, RND @cite_29 proposed that the target network be deterministic by fixing the... | {
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1901.05856 | 2909668595 | This paper proposes a new reinforcement learning (RL) algorithm that enhances exploration by amplifying the imitation effect (AIE). This algorithm consists of self-imitation learning and random network distillation algorithms. We argue that these two algorithms complement each other and that combining these two algorit... | where @math and @math and @math are the policy (i.e., actor) and the value function parameterized by @math . @math is a hyperparameter for the value loss. Intuitively, for the same state, if the past return value is greater than the current value ( @math ), then it can be observed that the behavior in the past is a goo... | {
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1901.05743 | 2910630904 | Abstract This paper presents a robust and comprehensive graph-based rank aggregation approach, used to combine results of isolated ranker models in retrieval tasks. The method follows an unsupervised scheme, which is independent of how the isolated ranks are formulated. Our approach is able to combine arbitrary models,... | Rank aggregation can be seen as the task of finding a good permutation of retrieved objects obtained from different input ranks. The Kemeny rank aggregation problem consists of finding the optimal permutation. It is an NP-hard problem for more than three input ranks @cite_12 . In practice, rank aggregation methods comp... | {
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"We consider the problem of combining ranking results from various sources. In the context of the Web, the main applications include building meta-search engines, combining ranking functions, selecting documents based on multiple c... |
1901.05743 | 2910630904 | Abstract This paper presents a robust and comprehensive graph-based rank aggregation approach, used to combine results of isolated ranker models in retrieval tasks. The method follows an unsupervised scheme, which is independent of how the isolated ranks are formulated. Our approach is able to combine arbitrary models,... | Related to the rank aggregation task, refers to a prior family of methods that also intend to promote better results, but do not explore the inter-relationships between the ranks from the response objects. Re-ranking approaches are feature-based @cite_40 or the rank-based @cite_15 . In this sense, the exploitation of i... | {
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"Abstract As network analysis methods prevail, more metrics are applied to co-word networks to reveal hot topics in a field. However, few studies have examined the... |
1901.05743 | 2910630904 | Abstract This paper presents a robust and comprehensive graph-based rank aggregation approach, used to combine results of isolated ranker models in retrieval tasks. The method follows an unsupervised scheme, which is independent of how the isolated ranks are formulated. Our approach is able to combine arbitrary models,... | Median Rank Aggregation (MRA) @cite_50 is an order-based method. It traverses the ranks counting the number of occurrences of the retrieved objects. The first object that occurs in more than half of the ranks is taken as the first object of the final rank. Then, the second object that occurs in more than half of the ra... | {
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1901.05743 | 2910630904 | Abstract This paper presents a robust and comprehensive graph-based rank aggregation approach, used to combine results of isolated ranker models in retrieval tasks. The method follows an unsupervised scheme, which is independent of how the isolated ranks are formulated. Our approach is able to combine arbitrary models,... | Six score-based methods were proposed by : CombSUM, CombMAX, CombMIN, CombMED, CombMNZ, and CombANZ, based on distinct priors. For these methods, each rank must be previously normalized with respect to its scores. Related to these methods, RLSim @cite_21 is a score-based technique, inspired by Naive Bayes classifier, t... | {
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1901.05743 | 2910630904 | Abstract This paper presents a robust and comprehensive graph-based rank aggregation approach, used to combine results of isolated ranker models in retrieval tasks. The method follows an unsupervised scheme, which is independent of how the isolated ranks are formulated. Our approach is able to combine arbitrary models,... | Some graph-based approaches for rank fusion were proposed based on Markov Chains, where retrieved objects are represented in the various ranks as vertices in a graph, with transition probabilities from vertex to vertex defined by the relative rankings of the items in the various ranks @cite_54 @cite_12 . | {
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1901.05657 | 2972975999 | One of the successful approaches in semi-supervised learning is based on the consistency loss between different predictions under random perturbations. Typically, a student model is trained to be consistent with teachers prediction for the inputs under different perturbation. However, to be successful,the teachers pseu... | @cite_4 apply the concept of temperature in model distillation, which aims to distill the knowledge from a large pre-trained network to a much smaller network without lossing much of the generalization ability. The temperature, a hyperparameter inside softmax function, is used to soften the probability distributions of... | {
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1901.05602 | 2909240857 | Face anti-spoofing (a.k.a presentation attack detection) has drawn growing attention due to the high-security demand in face authentication systems. Existing CNN-based approaches usually well recognize the spoofing faces when training and testing spoofing samples display similar patterns, but their performance would dr... | Most previous approaches for face anti-spoofing exploit texture differences between live and spoofing faces with pre-defined features such as LBP @cite_25 , HoG @cite_13 , and SURF @cite_19 , which are subsequently fed to a supervised classifier (., SVM, LDA) for binary classification. However, such handcrafted feature... | {
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1901.05602 | 2909240857 | Face anti-spoofing (a.k.a presentation attack detection) has drawn growing attention due to the high-security demand in face authentication systems. Existing CNN-based approaches usually well recognize the spoofing faces when training and testing spoofing samples display similar patterns, but their performance would dr... | Recently, deep learning based methods @cite_6 @cite_1 have been proposed to address face anti-spoofing. They use CNNs to learn highly discriminative representations by taking face anti-spoofing as a binary classification problem. However, most of them easily suffer overfitting. Current publicly available face anti-spoo... | {
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"In this paper, we propose a novel framework leveraging the advantages of the representational ability of deep learning and domain generalization for face spoofing de... |
1901.05635 | 2910234583 | Spatio-temporal information is very important to capture the discriminative cues between genuine and fake faces from video sequences. To explore such a temporal feature, the fine-grained motions (e.g., eye blinking, mouth movements and head swing) across video frames are very critical. In this paper, we propose a joint... | Recently, a large number of approaches have been proposed in the literature to detect spoofing attacks based on photographs, replayed videos and forged masks @cite_27 @cite_14 @cite_8 @cite_19 @cite_3 @cite_12 . Depending on the cues be used, existing face anti-spoofing methods could be roughly categorized into two gro... | {
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1901.05599 | 2963795705 | Robots hold promise in many scenarios involving outdoor use, such as search-and-rescue, wildlife management, and collecting data to improve environment, climate, and weather forecasting. However, autonomous navigation of outdoor trails remains a challenging problem. Recent work has sought to address this issue using de... | This dataset was used to train a convolutional neural network that learned to discriminate on salient features that best predict the most likely classification of the image. This method achieved classification accuracies of 85.2 While demonstrated promising results, their approach relies on real-world data collection a... | {
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1901.05599 | 2963795705 | Robots hold promise in many scenarios involving outdoor use, such as search-and-rescue, wildlife management, and collecting data to improve environment, climate, and weather forecasting. However, autonomous navigation of outdoor trails remains a challenging problem. Recent work has sought to address this issue using de... | Our approach is inspired by transfer learning; however, instead of transferring from one real-world domain to another, we are interested in the notion of transferring knowledge learned in virtual environments to the real world. For example, prior work has developed a mapless motion planner for real environments by trai... | {
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1901.05744 | 2910903466 | We present a novel technique based on deep learning and set theory which yields exceptional classification and prediction results. Having access to a sufficiently large amount of labelled training data, our methodology is capable of predicting the labels of the test data almost always even if the training data is entir... | We will recall a couple of relevant articles that highlight the current efficiency of deep neural networks and then cite a number of our articles for no apparent reason. Neural networks were initially introduced in the 1940s by McCulloch and Pitts @cite_30 in an attempt to mathematically model the human brain. Later, t... | {
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1907.06745 | 2958670508 | Humanitarian disasters have been on the rise in recent years due to the effects of climate change and socio-political situations such as the refugee crisis. Technology can be used to best mobilize resources such as food and water in the event of a natural disaster, by semi-automatically flagging tweets and short messag... | Other lines of work relevant to this paper involve minimally supervised machine learning, representation learning and transfer learning. Concerning minimally supervised machine learning (ML), in general, ML techniques where there are few, and in the case of zero-shot learning @cite_0 @cite_22 , no observed instances fo... | {
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1907.06989 | 2958962552 | In this technical report we investigate speed estimation of the ego-vehicle on the KITTI benchmark using state-of-the-art deep neural network based optical flow and single-view depth prediction methods. Using a straightforward intuitive approach and approximating a single scale factor, we evaluate several application s... | @cite_6 the authors used sparse optical flow to track feature points on images from a downward-looking camera mounted on the rear axle of the car and achieved a mean error relative to GPS measurement of 0.121 m s. However, the method works only in restricted conditions and was evaluated on self-collected data at low sp... | {
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1907.07011 | 2956444090 | Introducing explicit constraints on the structural predictions has been an effective way to improve the performance of semantic segmentation models. Existing methods are mainly based on insufficient hand-crafted rules that only partially capture the image structure, and some methods can also suffer from the efficiency ... | Fully convolutional network @cite_23 is one of the pioneers that introduce deep learning into semantic segmentation and achieve impressive performance on benchmark datasets. Two important techniques are proposed and have been explored extensively afterward. First, they adapt networks that are originally designed for im... | {
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1907.07011 | 2956444090 | Introducing explicit constraints on the structural predictions has been an effective way to improve the performance of semantic segmentation models. Existing methods are mainly based on insufficient hand-crafted rules that only partially capture the image structure, and some methods can also suffer from the efficiency ... | Pair-wise pixel affinity is a fundamental computer vision concept and has been widely used under deep learning scenarios. Maire al @cite_26 utilize affinity relation in the spectral embedding field while Liu al @cite_5 constructs a linear propagation module to learn pair-wise similarity matrix. Recently, pixel affinity... | {
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1907.06968 | 2959103553 | We present a deep learning-based multitask framework for joint 3D human pose estimation and action recognition from RGB video sequences. Our approach proceeds along two stages. In the first, we run a real-time 2D pose detector to determine the precise pixel location of important keypoints of the body. A two-stream neur... | To the best of our knowledge, several studies @cite_26 @cite_63 @cite_66 stated that regressing the 3D pose from 2D joint locations is difficult and not too accurate. However, motivated by Martinez al @cite_1 , we believe that a simple neural network can learn effectively a . Therefore, this paper aims at proposing a s... | {
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1907.06968 | 2959103553 | We present a deep learning-based multitask framework for joint 3D human pose estimation and action recognition from RGB video sequences. Our approach proceeds along two stages. In the first, we run a real-time 2D pose detector to determine the precise pixel location of important keypoints of the body. A two-stream neur... | Human action recognition from skelet al data or 3D poses is a challenging task. Previous works on this topic can be divided into two main groups of method. The first group @cite_33 @cite_51 @cite_29 extracts hand-crafted features and uses probabilistic graphical models, Hidden Markov Model (HMM) @cite_33 or Conditional... | {
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1907.06968 | 2959103553 | We present a deep learning-based multitask framework for joint 3D human pose estimation and action recognition from RGB video sequences. Our approach proceeds along two stages. In the first, we run a real-time 2D pose detector to determine the precise pixel location of important keypoints of the body. A two-stream neur... | In the literature, 3D human pose estimation and action recognition are closely related. However, both problems are generally considered as two distinct tasks @cite_44 . Although some approaches have been proposed for tackling the problem of jointly predicting 3D poses and recognizing actions in RGB images or video sequ... | {
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1907.06870 | 2960799269 | Model compression has become necessary when applying neural networks (NN) into many real application tasks that can accept slightly-reduced model accuracy with strict tolerance to model complexity. Recently, Knowledge Distillation, which distills the knowledge from well-trained and highly complex teacher model into a c... | Besides, using distillation for size reduction is mentioned by , which gives a new direction for training compact student models. The weighted average of soft distributed representation from the teacher's output and ground truth is much useful when training a model, so that some practices have been put for training com... | {
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1907.06870 | 2960799269 | Model compression has become necessary when applying neural networks (NN) into many real application tasks that can accept slightly-reduced model accuracy with strict tolerance to model complexity. Recently, Knowledge Distillation, which distills the knowledge from well-trained and highly complex teacher model into a c... | A piecewise linear function is composed of multiple linear segments. Some piecewise functions are continuous when the boundary value calculated by two adjacent intervals function is the same, whereas some may not be continuous. Benefit from its simplicity and the fitting ability to any function with enough segments, it... | {
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1907.07023 | 2962145851 | Training convolutional networks for semantic segmentation with strong (per-pixel) and weak (per-bounding-box) supervision requires a large amount of weakly labeled data. We propose two methods for selecting the most relevant data with weak supervision. The first method is designed for finding visually similar images wi... | Our trained convolutional networks, the GMM models, and the two selection methods algorithms will be made available to the research community @cite_28 . | {
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1907.06778 | 2960282122 | Location-based queries enable fundamental services for mobile road network travelers. While the benefits of location-based services (LBS) are numerous, exposure of mobile travelers' location information to untrusted LBS providers may lead to privacy breaches. In this paper, we propose StarCloak, a utility-aware and att... | falls under the location obfuscation category. Under this category, Mouratidis and Yiu @cite_10 provide @math -anonymity for road network travelers under the reciprocity requirement. @cite_6 support personalized privacy specifications such that a cloaked region satisfies @math -anonymity and includes a total minimum se... | {
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1907.06890 | 2956805085 | End-to-end learning has recently emerged as a promising technique to tackle the problem of autonomous driving. Existing works show that learning a navigation policy from raw sensor data may reduce the system's reliance on external sensing systems, (e.g. GPS), and or outperform traditional methods based on state estimat... | To account for model uncertainty in deep learning, a distribution is placed over neural network (NN) weights @math , defining a Bayesian neural network @cite_0 @cite_8 @cite_13 . The work of @cite_17 provides a mathematically grounded framework to capture model uncertainty leveraging dropout at test-time @cite_5 . Spec... | {
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1907.06890 | 2956805085 | End-to-end learning has recently emerged as a promising technique to tackle the problem of autonomous driving. Existing works show that learning a navigation policy from raw sensor data may reduce the system's reliance on external sensing systems, (e.g. GPS), and or outperform traditional methods based on state estimat... | A further step towards total uncertainty estimation was made by @cite_9 , that proposed a framework to jointly estimate both data and model uncertainty under the assumption of having input points with different noise levels than others. The data uncertainty is learned by training the NN under the : where the input nois... | {
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1907.06890 | 2956805085 | End-to-end learning has recently emerged as a promising technique to tackle the problem of autonomous driving. Existing works show that learning a navigation policy from raw sensor data may reduce the system's reliance on external sensing systems, (e.g. GPS), and or outperform traditional methods based on state estimat... | Sampling approaches are often too slow for practical scenarios. @cite_6 introduced a lightweight approach to recover uncertainty while maintaining the same network architecture, with minor changes to propagate both mean and variance of the input distribution. They propose to replace every intermediate network activatio... | {
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1907.06823 | 2960410483 | In this paper, an stereo-based traversability analysis approach for all terrains in off-road mobile robotics, e.g. Unmanned Ground Vehicles (UGVs) is proposed. This approach reformulates the problem of terrain traversability analysis into two main problems: (1) 3D terrain reconstruction and (2) terrain all surfaces det... | Our recent work @cite_1 in terrain traversability estimation mainly proposed geometry based features such as pixel-based surface normals using a stereo camera for environment perception. Mainly, it was explained how these pixel-based surface normals can perform the generated terrain point cloud segmentation and terrain... | {
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1907.06823 | 2960410483 | In this paper, an stereo-based traversability analysis approach for all terrains in off-road mobile robotics, e.g. Unmanned Ground Vehicles (UGVs) is proposed. This approach reformulates the problem of terrain traversability analysis into two main problems: (1) 3D terrain reconstruction and (2) terrain all surfaces det... | In @cite_11 , a similar approach has been proposed for terrain traversability analysis using Kinect on mobile robots. This approach is mainly based on geometry-based pixel-based surface normals and considering the kinematic capability of the vehicle such as max height, max slope and max step. This approach was applied ... | {
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1907.06823 | 2960410483 | In this paper, an stereo-based traversability analysis approach for all terrains in off-road mobile robotics, e.g. Unmanned Ground Vehicles (UGVs) is proposed. This approach reformulates the problem of terrain traversability analysis into two main problems: (1) 3D terrain reconstruction and (2) terrain all surfaces det... | In @cite_6 and @cite_4 , an geometry-based feature is proposed for roughness estimation so-called as Unevenness Point Descriptor (UPD). This feature is basically describing the unevenness and roughness on one point by measuring pixel-based normals and averaging the normals in k-neighborhood. This proposed feature is de... | {
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1907.06777 | 2961980567 | Accurately estimating the orientation of pedestrians is an important and challenging task for autonomous driving because this information is essential for tracking and predicting pedestrian behavior. This paper presents a flexible Virtual Multi-View Synthesis module that can be adopted into 3D object detection methods ... | Previous works have recognized that accurate orientation estimation requires a feature extraction process that captures fine-grained semantic information of objects. @cite_4 identify that standard Faster R-CNN feature maps are too low resolution for pedestrians and instead use atrous convolutions @cite_10 and pool feat... | {
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1907.06777 | 2961980567 | Accurately estimating the orientation of pedestrians is an important and challenging task for autonomous driving because this information is essential for tracking and predicting pedestrian behavior. This paper presents a flexible Virtual Multi-View Synthesis module that can be adopted into 3D object detection methods ... | Using keypoint detections @cite_31 @cite_40 @cite_45 @cite_36 and CAD models @cite_44 @cite_29 have been shown to be effective in gaining semantic understanding of objects of interest. The use of 2D keypoint detections to estimate pose has been well studied as the Perspective-n-Point (PnP) problem with many proposed so... | {
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1907.06777 | 2961980567 | Accurately estimating the orientation of pedestrians is an important and challenging task for autonomous driving because this information is essential for tracking and predicting pedestrian behavior. This paper presents a flexible Virtual Multi-View Synthesis module that can be adopted into 3D object detection methods ... | Most similar to our work are 3D pose estimation methods designed for autonomous driving scenarios. These methods have mainly focused on the representation of orientation and designing new loss functions. Pose-RCNN @cite_13 uses a Biternion representation for orientation as recommended by @cite_11 . The monocular 3D obj... | {
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1907.06796 | 2957060066 | Augmented Reality (AR) brings immersive experiences to users. With recent advances in computer vision and mobile computing, AR has scaled across platforms, and has increased adoption in major products. One of the key challenges in enabling AR features is proper anchoring of the virtual content to the real world, a proc... | Accurate initialization improves the resilience of SLAM algorithms and makes optimization converge faster. Researchers have relied on Structure-from-Motion (SfM) techniques, rotation averaging @cite_15 @cite_3 , or closed-form solutions @cite_9 to initialize the camera trajectories and the world map. However, these tec... | {
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1907.06796 | 2957060066 | Augmented Reality (AR) brings immersive experiences to users. With recent advances in computer vision and mobile computing, AR has scaled across platforms, and has increased adoption in major products. One of the key challenges in enabling AR features is proper anchoring of the virtual content to the real world, a proc... | Planar trackers are widely used in SfM applications and panoramic image registration @cite_20 . @cite_21 studied planar tracking for augmented reality applications. Direct region tracking algorithms typically use a homography to warp an image patch from the template to the source and minimize the difference @cite_4 . @... | {
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1907.06796 | 2957060066 | Augmented Reality (AR) brings immersive experiences to users. With recent advances in computer vision and mobile computing, AR has scaled across platforms, and has increased adoption in major products. One of the key challenges in enabling AR features is proper anchoring of the virtual content to the real world, a proc... | In @cite_12 , the authors propose a homography-based planar detection and tracking algorithm to estimate 6DoF camera poses. Recently, @cite_6 used detected surfaces from an image retrieval pipeline to initialize depth from the surface map. @cite_19 adopted gradient orientation for direct surface tracking. Correlation f... | {
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1907.06881 | 2956902387 | Recent researches attempt to improve the detection performance by adopting the idea of cascade for single-stage detectors. In this paper, we analyze and discover that inconsistency is the major factor limiting the performance. The refined anchors are associated with the feature extracted from the previous location and ... | In advance of the wide development of deep convolutional networks, the sliding-window paradigm dominates the field of object detection for years. Most progress is related to handcrafted image descriptors such as HOG @cite_6 and SIFT @cite_13 . Based on these powerful features, DPMs @cite_3 help to extend dense detector... | {
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1907.06881 | 2956902387 | Recent researches attempt to improve the detection performance by adopting the idea of cascade for single-stage detectors. In this paper, we analyze and discover that inconsistency is the major factor limiting the performance. The refined anchors are associated with the feature extracted from the previous location and ... | Compared with two-stage methods, one-stage approaches aim at achieving real-time speed while maintaining great performance. OverFeat @cite_9 is one of the first modern single-stage object detectors based on deep networks. YOLO @cite_23 @cite_26 and SSD @cite_11 have renewed interest in one-stage approaches by skipping ... | {
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1907.06881 | 2956902387 | Recent researches attempt to improve the detection performance by adopting the idea of cascade for single-stage detectors. In this paper, we analyze and discover that inconsistency is the major factor limiting the performance. The refined anchors are associated with the feature extracted from the previous location and ... | Non-maximum suppression (NMS) has been an essential component for removing duplicated bounding boxes in most object detectors since @cite_6 . It works in an iterative manner. At each iteration, the bounding box with the maximum classification confidence is selected and its neighboring boxes are suppressed using a prede... | {
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1901.05282 | 2909775062 | Using generative adversarial networks (GANs), we investigate the possibility of creating large amounts of analysis-specific simulated LHC events at limited computing cost. This kind of generative model is analysis specific in the sense that it directly generates the high-level features used in the last stage of a given... | Generative adversarial networks @cite_17 have been investigated for LHC applications to simulate the energy deposits of individual particles @cite_18 @cite_0 @cite_22 and jets @cite_27 @cite_1 , as well as to accelerate Matrix-Element methods @cite_12 . Recently, a GAN-based generator was developed to simulate data col... | {
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1901.05282 | 2909775062 | Using generative adversarial networks (GANs), we investigate the possibility of creating large amounts of analysis-specific simulated LHC events at limited computing cost. This kind of generative model is analysis specific in the sense that it directly generates the high-level features used in the last stage of a given... | The adversarial training (AT) technique is used in HEP for tasks other than event generation: reference @cite_35 discusses how to account for uncertainties associated to a given nuisance parameter using AT. Reference @cite_34 uses AT to preserve the independence of a given network score (a jet tagger) from a specific p... | {
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1901.05389 | 2910117392 | The socioeconomic status of people depends on a combination of individual characteristics and environmental variables, thus its inference from online behavioral data is a difficult task. Attributes like user semantics in communication, habitat, occupation, or social network are all known to be determinant predictors of... | There is a growing effort in the field to combine online behavioral data with census records, and expert annotated information to infer social attributes of users of online services. The predicted attributes range from easily assessable individual characteristics such as age @cite_40 , or occupation @cite_38 @cite_18 @... | {
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1901.05350 | 2908701480 | TensorFlow.js is a library for building and executing machine learning algorithms in JavaScript. TensorFlow.js models run in a web browser and in the Node.js environment. The library is part of the TensorFlow ecosystem, providing a set of APIs that are compatible with those in Python, allowing models to be ported betwe... | WebDNN @cite_59 is another deep learning library in JS that can execute pretrained models developed in TensorFlow, Keras, PyTorch, Chainer and Caffe. To accelerate computation, WebDNN uses WebGPU @cite_9 , a technology initially proposed by Apple. WebGPU is in an early exploratory stage and currently only supported in ... | {
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1901.05362 | 2909928957 | Current benchmarks for optical flow algorithms evaluate the estimation quality by comparing their predicted flow field with the ground truth, and additionally may compare interpolated frames, based on these predictions, with the correct frames from the actual image sequences. For the latter comparisons, objective measu... | So far, there is only one benchmark that is used for evaluating the performance of frame interpolation, namely the Middlebury benchmark. It was originally designed for the evaluation of optical flow algorithms. Since it provides the ground-truth in-between images to evaluate the interpolation performance of optical flo... | {
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1901.05362 | 2909928957 | Current benchmarks for optical flow algorithms evaluate the estimation quality by comparing their predicted flow field with the ground truth, and additionally may compare interpolated frames, based on these predictions, with the correct frames from the actual image sequences. For the latter comparisons, objective measu... | Some interpolation algorithms like @cite_6 , @cite_27 used the UCF 101 dataset @cite_23 for training and testing. Others like @cite_20 , @cite_22 , @cite_18 used the videos from @cite_8 , @cite_16 . For evaluation, generally they chose to compute one of MSE, PSNR, and SSIM between their interpolated images and the grou... | {
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1901.05415 | 2910458567 | The majority of conversations a dialogue agent sees over its lifetime occur after it has already been trained and deployed, leaving a vast store of potential training signal untapped. In this work, we propose the self-feeding chatbot, a dialogue agent with the ability to extract new training examples from the conversat... | The general concepts of lifelong learning and never-ending (language) learning @cite_8 are related to the topics discussed in this work, as is active learning @cite_7 and predictive modeling @cite_3 . | {
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1901.05510 | 2911027342 | This article presents a novel framework for performing visual inspection around 3D infrastructures, by establishing a team of fully autonomous Micro Aerial Vehicles (MAVs) with robust localization, planning and perception capabilities. The proposed aerial inspection system reaches high level of autonomy on a large scal... | Nowadays, Micro Aerial Vehicles (MAVs) are gaining more and more attention from the scientific community, constituting a fast-paced emerging technology that constantly pushes their limits for accomplishing complex tasks @cite_5 . These platforms are characterized by their mechanical simplicity, agility, stability and o... | {
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1901.05510 | 2911027342 | This article presents a novel framework for performing visual inspection around 3D infrastructures, by establishing a team of fully autonomous Micro Aerial Vehicles (MAVs) with robust localization, planning and perception capabilities. The proposed aerial inspection system reaches high level of autonomy on a large scal... | One of the most common application areas that MAVs are employed, is in the filming industry, but there are efforts from other industries such as Mining, Oil, and Energy Providers, to invest in the commercialization of MAVs to perform remote inspection applications. Towards this vision, MAVs are powerful tools that have... | {
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1907.06484 | 2954902138 | A recent trend in IR has been the usage of neural networks to learn retrieval models for text based adhoc search. While various approaches and architectures have yielded significantly better performance than traditional retrieval models such as BM25, it is still difficult to understand exactly why a document is relevan... | There are two main approaches to interpretability in machine learning models: model agnostic and model introspective approaches. Model agnostic approaches @cite_15 @cite_9 generate post-hoc explanations for the original model by treating it as a black box by learning an interpretable model on the output of the model or... | {
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1907.06600 | 2959857067 | Risk adjustment has become an increasingly important tool in healthcare. It has been extensively applied to payment adjustment for health plans to reflect the expected cost of providing coverage for members. Risk adjustment models are typically estimated using linear regression, which does not fully exploit the informa... | Le and Mikolov @cite_3 extended the models to groups of words, including sentences, paragraphs, and entire documents. In their Distributed Memory Model of Paragraph Vectors (PV-DM), which is analogous to the CBOW model, a paragraph (or other chosen word group) vector is added as a predictor to the context words' vector... | {
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1907.06458 | 2957482284 | Keyword extraction is used for summarizing the content of a document and supports efficient document retrieval, and is as such an indispensable part of modern text-based systems. We explore how load centrality, a graph-theoretic measure applied to graphs derived from a given text can be used to efficiently identify and... | The method that we propose in this paper, RaKUn, is a graph-based keyword extraction method. We exploit some of the ideas from the area of graph aggregation-based learning, where, for example, graph convolutional neural networks and similar approaches were shown to yield high quality vertex representations by aggregati... | {
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1907.06458 | 2957482284 | Keyword extraction is used for summarizing the content of a document and supports efficient document retrieval, and is as such an indispensable part of modern text-based systems. We explore how load centrality, a graph-theoretic measure applied to graphs derived from a given text can be used to efficiently identify and... | The main contributions of this paper are as follows. The notion of load centrality was to our knowledge not yet sufficiently exploited for keyword extraction. We show that this fast measure offers competitive performance to other widely used centralities, such as for example the PageRank centrality (used in @cite_7 ). ... | {
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1907.06490 | 2957558419 | Recently, convolutional neural networks (CNN) have been successfully applied to many remote sensing problems. However, deep learning techniques for multi-image super-resolution from multitemporal unregistered imagery have received little attention so far. This work proposes a novel CNN-based technique that exploits bot... | The literature on SR techniques is extensive, both for SISR and for MISR techniques. SISR approaches can be classified into three main classes: interpolation-based methods (e.g., Lanczos kernels), optimization-based methods and learning-based methods. Optimization-based methods explicitly model prior knowledge about na... | {
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1907.06490 | 2957558419 | Recently, convolutional neural networks (CNN) have been successfully applied to many remote sensing problems. However, deep learning techniques for multi-image super-resolution from multitemporal unregistered imagery have received little attention so far. This work proposes a novel CNN-based technique that exploits bot... | Learning-based methods can be pixel-based or example-based. The latter ones are the most popular and they model the correspondence among LR and HR patches for HR patch prediction. After the early work by @cite_15 based on searching @math -nearest neighbors LR-HR patch pairs of the input LR patch to estimate the HR patc... | {
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1907.06490 | 2957558419 | Recently, convolutional neural networks (CNN) have been successfully applied to many remote sensing problems. However, deep learning techniques for multi-image super-resolution from multitemporal unregistered imagery have received little attention so far. This work proposes a novel CNN-based technique that exploits bot... | While most of the deep learning SISR works are related to traditional natural images, lately CNNs have been exploited for remote sensing imagery. A deep learning based method has been applied by @cite_2 on remote sensing images in the frequency domain. Their CNN takes as input discrete wavelet transformed images and ad... | {
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1907.06490 | 2957558419 | Recently, convolutional neural networks (CNN) have been successfully applied to many remote sensing problems. However, deep learning techniques for multi-image super-resolution from multitemporal unregistered imagery have received little attention so far. This work proposes a novel CNN-based technique that exploits bot... | Concerning MISR, the first work was proposed by Tsai and Huang @cite_26 , who used a frequency-domain technique to combine multiple under-sampled images with sub-pixel displacements to improve the spatial resolution of Landsat TM acquisitions. Due to the drawbacks of the frequency-domain algorithms, like the difficulty... | {
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1907.06490 | 2957558419 | Recently, convolutional neural networks (CNN) have been successfully applied to many remote sensing problems. However, deep learning techniques for multi-image super-resolution from multitemporal unregistered imagery have received little attention so far. This work proposes a novel CNN-based technique that exploits bot... | The iterative back projection (IBP) approach was introduced by Irani and Peleg @cite_1 . IBP aims to improve an initial guess of the super-resolved image by back projecting the difference between simulated LR images and actual LR images to the SR image. The updates are iteratively performed attempting to invert the for... | {
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1907.06632 | 2956348853 | In this paper, we present the Metamorphic Testing of an in-use deep learning based forecasting application. The application looks at the past data of system characteristics (e.g. memory allocation') to predict outages in the future. We focus on two statistical machine learning based components - a) detection of co-rela... | However, there are various deficiencies in this standard process of training' and validation'. It is sometimes seen that subtle implementation mistakes do not produce obvious signals during training and validation and may go undetected @cite_14 @cite_4 @cite_24 . It is also often the case that validation data does not ... | {
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1907.06632 | 2956348853 | In this paper, we present the Metamorphic Testing of an in-use deep learning based forecasting application. The application looks at the past data of system characteristics (e.g. memory allocation') to predict outages in the future. We focus on two statistical machine learning based components - a) detection of co-rela... | Thus, there has been a growing interest in the effective testing of ML based applications. Some of the recent work includes, measuring the invariance of image classifiers to rotations and translations @cite_17 , changes in image characteristics such as contrast @cite_11 @cite_0 @cite_1 and introducing spurious objects ... | {
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1907.06632 | 2956348853 | In this paper, we present the Metamorphic Testing of an in-use deep learning based forecasting application. The application looks at the past data of system characteristics (e.g. memory allocation') to predict outages in the future. We focus on two statistical machine learning based components - a) detection of co-rela... | In this paper, we continue @cite_14 to explore the testing of an ML application with a focus on identifying implementation bugs. We approach the problem through the application of Metamorphic Testing. Some of the existing work in Metamorphic Testing of ML & statistical applications include the testing of Naive-Bayes cl... | {
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1901.05195 | 2910360141 | Current state of the art solutions in the control of an autonomous vehicle mainly use supervised end-to-end learning, or decoupled perception, planning and action pipelines. Another possible solution is deep reinforcement learning, but such a method requires that the agent interacts with its surroundings in a simulated... | T he ability of an autonomous car to navigate without human input has become a mainstream research topic in the quest for autonomous driving. In this paper we propose a simulated environment engine for learning autonomous driving behaviors, entitled GridSim (see Fig. ). The simulator uses an Occupancy Grid (OG) sensor ... | {
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1901.05195 | 2910360141 | Current state of the art solutions in the control of an autonomous vehicle mainly use supervised end-to-end learning, or decoupled perception, planning and action pipelines. Another possible solution is deep reinforcement learning, but such a method requires that the agent interacts with its surroundings in a simulated... | An AV must be able to sense its own surroundings and form an environment model consisting of moving and stationary objects @cite_3 , and to further use this information in order to learn long term driving strategies. These driving policies govern the vehicle's motion @cite_2 and automatically output control signals for... | {
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1901.04980 | 2909141925 | Dobrushin (1972) showed that the interface of a 3D Ising model with minus boundary conditions above the @math -plane and plus below is rigid (has @math -fluctuations) at every sufficiently low temperature. Since then, basic features of this interface -- such as the asymptotics of its maximum -- were only identified in ... | Subsequently, cluster expansion was instrumental in analyzing the analogous interface in two dimensions. This line of work culminated in the seminal monograph @cite_32 , showing that the shape of a macroscopic minus droplet in the plus phase takes after the , the convex body minimizing the surface energy to volume rati... | {
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1901.04980 | 2909141925 | Dobrushin (1972) showed that the interface of a 3D Ising model with minus boundary conditions above the @math -plane and plus below is rigid (has @math -fluctuations) at every sufficiently low temperature. Since then, basic features of this interface -- such as the asymptotics of its maximum -- were only identified in ... | While cluster expansion only converges at sufficiently large @math , it is natural to ask if the rigidity of the interface, and our new results, hold for all @math . This is not believed to be the case, as the Ising model is widely believed to undergo a for @math (and no other dimension): Much like the SOS and DG appro... | {
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1901.04980 | 2909141925 | Dobrushin (1972) showed that the interface of a 3D Ising model with minus boundary conditions above the @math -plane and plus below is rigid (has @math -fluctuations) at every sufficiently low temperature. Since then, basic features of this interface -- such as the asymptotics of its maximum -- were only identified in ... | Much progress has been made in recent years on understanding the distribution of the maximum of the 2D discrete Gaussian free field and its local geometry. It is known for instance ( @cite_14 @cite_20 @cite_18 ; see also, e.g., @cite_23 ) that this maximum is tight around an expected maximum that is asymptotically @mat... | {
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1901.04980 | 2909141925 | Dobrushin (1972) showed that the interface of a 3D Ising model with minus boundary conditions above the @math -plane and plus below is rigid (has @math -fluctuations) at every sufficiently low temperature. Since then, basic features of this interface -- such as the asymptotics of its maximum -- were only identified in ... | We end this section with other perspectives on the 3D Ising model at low temperatures, which were also the focus of much attention. While the interface-based approach of Dobrushin @cite_2 to understanding the low-temperature 3D Ising model proved to be extremely fruitful in 2D (where the results hold for interfaces in ... | {
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1901.04980 | 2909141925 | Dobrushin (1972) showed that the interface of a 3D Ising model with minus boundary conditions above the @math -plane and plus below is rigid (has @math -fluctuations) at every sufficiently low temperature. Since then, basic features of this interface -- such as the asymptotics of its maximum -- were only identified in ... | In lieu of this approach, a coarse-graining technique of Pisztora @cite_24 enabled the establishment of the surface tension and Wulff shape scaling limit for the 3D Ising model at low-temperature: Cerf and Pisztora @cite_22 considered an Ising model on an @math box with all-plus boundary conditions, and showed that con... | {
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1901.05127 | 2910836674 | Neural style transfer has drawn considerable attention from both academic and industrial field. Although visual effect and efficiency have been significantly improved, existing methods are unable to coordinate spatial distribution of visual attention between the content image and stylized image, or render diverse level... | Attention Models. One of the most promising trends in research is the emergence of incorporating attention mechanism into deep learning framework @cite_20 @cite_8 . Rather than compressing an entire image or a sequence into a static representation, attention allows the model to focus on the most relevant part of images... | {
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1901.05138 | 2909251876 | Dynamic Programming Languages are quite popular because they increase the programmer's productivity. However, the absence of types in the source code makes the program written in these languages difficult to understand and virtual machines that execute these programs cannot produced optimized code. To overcome this cha... | Convolutional Neural Network for sequence modeling was introduced by Yoon Kim @cite_10 for sentiment classification on a text sequence. The idea of convolutions on Tree-based structure was developed by @cite_7 with the idea of sliding convolutional kernel combined with dynamic pooling @cite_2 to process the AST to extr... | {
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1901.05138 | 2909251876 | Dynamic Programming Languages are quite popular because they increase the programmer's productivity. However, the absence of types in the source code makes the program written in these languages difficult to understand and virtual machines that execute these programs cannot produced optimized code. To overcome this cha... | In natural language processing sentiment classification is a long-standing task. Initial approaches used bag-of-words, which evolved to sequence modeling using linear LSTM which assumes a right-branching sentence. @cite_8 have introduced Recursive Neural Network for classifying the sentiment of all nodes in the parse t... | {
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"abstract": [
"Semantic word spaces have been very useful but cannot express the meaning of longer phrases in a principled way. Further progress towards understanding compositionality in tasks such as sentiment detection requires richer supervise... |
1901.05138 | 2909251876 | Dynamic Programming Languages are quite popular because they increase the programmer's productivity. However, the absence of types in the source code makes the program written in these languages difficult to understand and virtual machines that execute these programs cannot produced optimized code. To overcome this cha... | The Tree-Structured LSTM introduced in @cite_5 had two main contributions: (i) a generalization of LSTMs to tree-structured network topologies, and (ii) Child-Sum Trees to handle arbitrary number of children in the tree nodes. It also replaced simple recurrent units in recursive network with LSTM cells to overcome the ... | {
"cite_N": [
"@cite_5"
],
"mid": [
"2104246439"
],
"abstract": [
"Because of their superior ability to preserve sequence information over time, Long Short-Term Memory (LSTM) networks, a type of recurrent neural network with a more complex computational unit, have obtained strong results on a vari... |
1901.05138 | 2909251876 | Dynamic Programming Languages are quite popular because they increase the programmer's productivity. However, the absence of types in the source code makes the program written in these languages difficult to understand and virtual machines that execute these programs cannot produced optimized code. To overcome this cha... | The architecture and algorithm introduced by Le and Zuidema @cite_9 allows information to flow not only bottom-up, as in traditional recursive neural network, but top-down as well. Every node in the hierarchical structure is associated with two vectors: (i) inside representation to represent content under the node, and... | {
"cite_N": [
"@cite_9"
],
"mid": [
"2251628756"
],
"abstract": [
"We propose the first implementation of an infinite-order generative dependency model. The model is based on a new recursive neural network architecture, the Inside-Outside Recursive Neural Network. This architecture allows informat... |
1901.04982 | 2909529610 | Modern Intel CPUs reduce their frequency when executing wide vector operations (AVX2 and AVX-512 instructions), as these instructions increase power consumption. The frequency is only increased again two milliseconds after the last code section containing such instructions has been executed in order to prevent excessiv... | Frequency variations depending on the executed instruction mix were first described for Haswell-EP processors which have different maximum frequencies depending on whether AVX instructions are executed @cite_7 . Whereas previous CPUs operated at a constant frequency and the power consumption varied depending on the ins... | {
"cite_N": [
"@cite_6",
"@cite_7"
],
"mid": [
"2515937114",
"1628605343"
],
"abstract": [
"The Intel Haswell-EP processor generation introduces several major advancements of power control and energy-efficiency features. For computationally intense applications using advanced vector extens... |
1901.04982 | 2909529610 | Modern Intel CPUs reduce their frequency when executing wide vector operations (AVX2 and AVX-512 instructions), as these instructions increase power consumption. The frequency is only increased again two milliseconds after the last code section containing such instructions has been executed in order to prevent excessiv... | We use core specialization as a technique to limit the effect of AVX-induced frequency reduction to select cores. Core specialization has been suggested as a mechanism to increase performance before, although different effects were utilized. As the fastest cache levels are usually private to the individual cores, core ... | {
"cite_N": [
"@cite_13",
"@cite_4"
],
"mid": [
"2143677609",
"2765448242"
],
"abstract": [
"For the past 30+ years, system calls have been the de facto interface used by applications to request services from the operating system kernel. System calls have almost universally been implemente... |
1901.04982 | 2909529610 | Modern Intel CPUs reduce their frequency when executing wide vector operations (AVX2 and AVX-512 instructions), as these instructions increase power consumption. The frequency is only increased again two milliseconds after the last code section containing such instructions has been executed in order to prevent excessiv... | The approaches described above implement core specialization in software, but operate on multiprocessor systems containing hardware-wise identical cores. However, different applications (or parts of applications) have different requirements to the underlying microarchitecture. For example, a memory-intensive applicatio... | {
"cite_N": [
"@cite_21"
],
"mid": [
"2112085716"
],
"abstract": [
"This paper proposes and evaluates single-ISA heterogeneous multi-core architectures as a mechanism to reduce processor power dissipation. Our design incorporates heterogeneous cores representing different points in the power perfo... |
1901.04982 | 2909529610 | Modern Intel CPUs reduce their frequency when executing wide vector operations (AVX2 and AVX-512 instructions), as these instructions increase power consumption. The frequency is only increased again two milliseconds after the last code section containing such instructions has been executed in order to prevent excessiv... | Similarly, a heterogeneous multi-core system can provide cores with different ISAs @cite_12 . For example, the ARM Thumb instruction set provides higher code density, but provides fewer and smaller general purpose registers, and is therefore efficient for execution of code sections with low register pressure, whereas c... | {
"cite_N": [
"@cite_12"
],
"mid": [
"2110653637"
],
"abstract": [
"Heterogeneous multicore architectures have the potential for high performance and energy efficiency. These architectures may be composed of small power-efficient cores, large high-performance cores, and or specialized cores that a... |
1901.04982 | 2909529610 | Modern Intel CPUs reduce their frequency when executing wide vector operations (AVX2 and AVX-512 instructions), as these instructions increase power consumption. The frequency is only increased again two milliseconds after the last code section containing such instructions has been executed in order to prevent excessiv... | On such a system, threads need to be migrated to a suitable core whenever they execute significant amounts of wide SIMD instructions. is an operating system mechanism to automatically move threads to a suitable core @cite_0 @cite_15 . Whenever a thread executes an instruction not supported on its current core, the core... | {
"cite_N": [
"@cite_0",
"@cite_15"
],
"mid": [
"1974809793",
"1991867972"
],
"abstract": [
"A heterogeneous processor consists of cores that are asymmetric in performance and functionality. Such a design provides a cost-effective solution for processor manufacturers to continuously improv... |
1901.04982 | 2909529610 | Modern Intel CPUs reduce their frequency when executing wide vector operations (AVX2 and AVX-512 instructions), as these instructions increase power consumption. The frequency is only increased again two milliseconds after the last code section containing such instructions has been executed in order to prevent excessiv... | Previous work assumes a heterogeneous multiprocessor where cores differ in hardware @cite_0 @cite_15 . However, the concept of fault-and-migrate is applicable to software-based heterogeneity similar to the one used in our design. Whereas our prototype currently requires the developer to manually annotate code sections ... | {
"cite_N": [
"@cite_0",
"@cite_15"
],
"mid": [
"1974809793",
"1991867972"
],
"abstract": [
"A heterogeneous processor consists of cores that are asymmetric in performance and functionality. Such a design provides a cost-effective solution for processor manufacturers to continuously improv... |
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