January 30, 2020

3307 words 16 mins read

Paper Group ANR 256

Deep Plug-and-play Prior for Low-rank Tensor Completion. A Proposed Artificial intelligence Model for Real-Time Human Action Localization and Tracking. Lexical Features Are More Vulnerable, Syntactic Features Have More Predictive Power. Hybrid Code Networks using a convolutional neural network as an input layer achieves higher turn accuracy. Real-t …

Deep Plug-and-play Prior for Low-rank Tensor Completion


Title	Deep Plug-and-play Prior for Low-rank Tensor Completion
Authors	Wen-Hao Xu, Xi-Le Zhao, Tai-Xiang Jiang, Yao Wang, Michael Ng
Abstract	Multi-dimensional images, such as color images and multi-spectral images, are highly correlated and contain abundant spatial and spectral information. However, real-world multi-dimensional images are usually corrupted by missing entries. By integrating deterministic low-rankness prior with the data-driven deep prior, we suggest a novel regularized tensor completion model for multi-dimensional image completion. In the objective function, we adopt the newly emerged tensor nuclear norm (TNN) to characterize the global low-rankness prior of the multi-dimensional images. We also formulate an implicit regularizer to plug in the denoising neural network (termed as deep denoiser), which is convinced to express the deep image prior learned from a large number of natural images. The resulting model can be efficiently solved by the alternating directional method of multipliers algorithm under the plug-and-play (PnP) framework. Experimental results on color images, videos, and multi-spectral images demonstrate that the proposed method can recover both the global structure and fine details very well and achieve superior performance over competing methods in terms of quality metrics and visual effects.
Tasks	Denoising
Published	2019-05-11
URL	https://arxiv.org/abs/1905.04449v2
PDF	https://arxiv.org/pdf/1905.04449v2.pdf
PWC	https://paperswithcode.com/paper/deep-plug-and-play-prior-for-low-rank-tensor
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A Proposed Artificial intelligence Model for Real-Time Human Action Localization and Tracking


Title	A Proposed Artificial intelligence Model for Real-Time Human Action Localization and Tracking
Authors	Ahmed Ali Hammam, Mona Soliman, Aboul Ella Hassanien
Abstract	In recent years, artificial intelligence (AI) based on deep learning (DL) has sparked tremendous global interest. DL is widely used today and has expanded into various interesting areas. It is becoming more popular in cross-subject research, such as studies of smart city systems, which combine computer science with engineering applications. Human action detection is one of these areas. Human action detection is an interesting challenge due to its stringent requirements in terms of computing speed and accuracy. High-accuracy real-time object tracking is also considered a significant challenge. This paper integrates the YOLO detection network, which is considered a state-of-the-art tool for real-time object detection, with motion vectors and the Coyote Optimization Algorithm (COA) to construct a real-time human action localization and tracking system. The proposed system starts with the extraction of motion information from a compressed video stream and the extraction of appearance information from RGB frames using an object detector. Then, a fusion step between the two streams is performed, and the results are fed into the proposed action tracking model. The COA is used in object tracking due to its accuracy and fast convergence. The basic foundation of the proposed model is the utilization of motion vectors, which already exist in a compressed video bit stream and provide sufficient information to improve the localization of the target action without requiring high consumption of computational resources compared with other popular methods of extracting motion information, such as optical flows. This advantage allows the proposed approach to be implemented in challenging environments where the computational resources are limited, such as Internet of Things (IoT) systems.
Tasks	Action Detection, Action Localization, Object Detection, Object Tracking, Real-Time Object Detection
Published	2019-11-09
URL	https://arxiv.org/abs/1911.04469v1
PDF	https://arxiv.org/pdf/1911.04469v1.pdf
PWC	https://paperswithcode.com/paper/191104469
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Lexical Features Are More Vulnerable, Syntactic Features Have More Predictive Power


Title	Lexical Features Are More Vulnerable, Syntactic Features Have More Predictive Power
Authors	Jekaterina Novikova, Aparna Balagopalan, Ksenia Shkaruta, Frank Rudzicz
Abstract	Understanding the vulnerability of linguistic features extracted from noisy text is important for both developing better health text classification models and for interpreting vulnerabilities of natural language models. In this paper, we investigate how generic language characteristics, such as syntax or the lexicon, are impacted by artificial text alterations. The vulnerability of features is analysed from two perspectives: (1) the level of feature value change, and (2) the level of change of feature predictive power as a result of text modifications. We show that lexical features are more sensitive to text modifications than syntactic ones. However, we also demonstrate that these smaller changes of syntactic features have a stronger influence on classification performance downstream, compared to the impact of changes to lexical features. Results are validated across three datasets representing different text-classification tasks, with different levels of lexical and syntactic complexity of both conversational and written language.
Tasks	Text Classification
Published	2019-09-30
URL	https://arxiv.org/abs/1910.00065v1
PDF	https://arxiv.org/pdf/1910.00065v1.pdf
PWC	https://paperswithcode.com/paper/lexical-features-are-more-vulnerable
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Hybrid Code Networks using a convolutional neural network as an input layer achieves higher turn accuracy


Title	Hybrid Code Networks using a convolutional neural network as an input layer achieves higher turn accuracy
Authors	Petr Marek
Abstract	The dialogue management is a task of conversational artificial intelligence. The goal of the dialogue manager is to select the appropriate response to the conversational partner conditioned by the input message and recent dialogue state. Hybrid Code Networks is one of the models of dialogue managers, which uses an average of word embeddings and bag-of-words as input features. We perform experiments on Dialogue bAbI Task 6 and Alquist Conversational Dataset. The experiments show that the convolutional neural network used as an input layer of the Hybrid Code Network improves the model’s turn accuracy.
Tasks	Dialogue Management, Word Embeddings
Published	2019-07-28
URL	https://arxiv.org/abs/1907.12162v1
PDF	https://arxiv.org/pdf/1907.12162v1.pdf
PWC	https://paperswithcode.com/paper/hybrid-code-networks-using-a-convolutional
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Real-time Analysis of Privacy-(un)aware IoT Applications


Title	Real-time Analysis of Privacy-(un)aware IoT Applications
Authors	Leonardo Babun, Z. Berkay Celik, Patrick McDaniel, A. Selcuk Uluagac
Abstract	Users trust IoT apps to control and automate their smart devices. These apps necessarily have access to sensitive data to implement their functionality. However, users lack visibility into how their sensitive data is used (or leaked), and they often blindly trust the app developers. In this paper, we present IoTWatcH, a novel dynamic analysis tool that uncovers the privacy risks of IoT apps in real-time. We designed and built IoTWatcH based on an IoT privacy survey that considers the privacy needs of IoT users. IoTWatcH provides users with a simple interface to specify their privacy preferences with an IoT app. Then, in runtime, it analyzes both the data that is sent out of the IoT app and its recipients using Natural Language Processing (NLP) techniques. Moreover, IoTWatcH informs the users with its findings to make them aware of the privacy risks with the IoT app. We implemented IoTWatcH on real IoT applications. Specifically, we analyzed 540 IoT apps to train the NLP model and evaluate its effectiveness. IoTWatcH successfully classifies IoT app data sent to external parties to correct privacy labels with an average accuracy of 94.25%, and flags IoT apps that leak privacy data to unauthorized parties. Finally, IoTWatcH yields minimal overhead to an IoT app’s execution, on average 105 ms additional latency.
Tasks
Published	2019-11-24
URL	https://arxiv.org/abs/1911.10461v1
PDF	https://arxiv.org/pdf/1911.10461v1.pdf
PWC	https://paperswithcode.com/paper/real-time-analysis-of-privacy-unaware-iot
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PRSNet: Part Relation and Selection Network for Bone Age Assessment


Title	PRSNet: Part Relation and Selection Network for Bone Age Assessment
Authors	Yuanfeng Ji, Hao Chen, Dan Lin, Xiaohua Wu, Di Lin
Abstract	Bone age is one of the most important indicators for assessing bone’s maturity, which can help to interpret human’s growth development level and potential progress. In the clinical practice, bone age assessment (BAA) of X-ray images requires the joint consideration of the appearance and location information of hand bones. These kinds of information can be effectively captured by the relation of different anatomical parts of hand bone. Recently developed methods differ mostly in how they model the part relation and choose useful parts for BAA. However, these methods neglect the mining of relationship among different parts, which can help to improve the assessment accuracy. In this paper, we propose a novel part relation module, which accurately discovers the underlying concurrency of parts by using multi-scale context information of deep learning feature representation. Furthermore, based on the part relation, we explore a new part selection module, which comprehensively measures the importance of parts and select the top ranking parts for assisting BAA. We jointly train our part relation and selection modules in an end-to-end way, achieving state-of-the-art performance on the public RSNA 2017 Pediatric Bone Age benchmark dataset and outperforming other competitive methods by a significant margin.
Tasks
Published	2019-09-05
URL	https://arxiv.org/abs/1909.05651v1
PDF	https://arxiv.org/pdf/1909.05651v1.pdf
PWC	https://paperswithcode.com/paper/prsnet-part-relation-and-selection-network
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Learning Independent Object Motion from Unlabelled Stereoscopic Videos


Title	Learning Independent Object Motion from Unlabelled Stereoscopic Videos
Authors	Zhe Cao, Abhishek Kar, Christian Haene, Jitendra Malik
Abstract	We present a system for learning motion of independently moving objects from stereo videos. The only human annotation used in our system are 2D object bounding boxes which introduce the notion of objects to our system. Unlike prior learning based work which has focused on predicting dense pixel-wise optical flow field and/or a depth map for each image, we propose to predict object instance specific 3D scene flow maps and instance masks from which we are able to derive the motion direction and speed for each object instance. Our network takes the 3D geometry of the problem into account which allows it to correlate the input images. We present experiments evaluating the accuracy of our 3D flow vectors, as well as depth maps and projected 2D optical flow where our jointly learned system outperforms earlier approaches trained for each task independently.
Tasks	Optical Flow Estimation
Published	2019-01-07
URL	http://arxiv.org/abs/1901.01971v2
PDF	http://arxiv.org/pdf/1901.01971v2.pdf
PWC	https://paperswithcode.com/paper/learning-independent-object-motion-from
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Estimation and Validation of a Class of Conditional Average Treatment Effects Using Observational Data


Title	Estimation and Validation of a Class of Conditional Average Treatment Effects Using Observational Data
Authors	Steve Yadlowsky, Fabio Pellegrini, Federica Lionetto, Stefan Braune, Lu Tian
Abstract	While sample sizes in randomized clinical trials are large enough to estimate the average treatment effect well, they are often insufficient for estimation of treatment-covariate interactions critical to studying data-driven precision medicine. Observational data from real world practice may play an important role in alleviating this problem. One common approach in trials is to predict the outcome of interest with separate regression models in each treatment arm, and recommend interventions based on the contrast of the predicted outcomes. Unfortunately, this simple approach may induce spurious treatment-covariate interaction in observational studies when the regression model is misspecified. Motivated by the need of modeling the number of relapses in multiple sclerosis patients, where the ratio of relapse rates is a natural choice of the treatment effect, we propose to estimate the conditional average treatment effect (CATE) as the relative ratio of the potential outcomes, and derive a doubly robust estimator of this CATE in a semiparametric model of treatment-covariate interactions. We also provide a validation procedure to check the quality of the estimator on an independent sample. We conduct simulations to demonstrate the finite sample performance of the proposed methods, and illustrate the advantage of this approach on real data examining the treatment effect of dimethyl fumarate compared to teriflunomide in multiple sclerosis patients.
Tasks
Published	2019-12-15
URL	https://arxiv.org/abs/1912.06977v1
PDF	https://arxiv.org/pdf/1912.06977v1.pdf
PWC	https://paperswithcode.com/paper/estimation-and-validation-of-a-class-of
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Distributed Estimation via Network Regularization


Title	Distributed Estimation via Network Regularization
Authors	Lingzhou Hong, Alfredo Garcia, Ceyhun Eksin
Abstract	We propose a new method for distributed estimation of a linear model by a network of local learners with heterogeneously distributed datasets. Unlike other ensemble learning methods, in the proposed method, model averaging is done continuously over time in a distributed and asynchronous manner. To ensure robust estimation, a network regularization term which penalizes models with high local variability is used. We provide a finite-time characterization of convergence of the weighted ensemble average and compare this result to centralized estimation. We illustrate the general applicability of the method in two examples: estimation of a Markov random field using wireless sensor networks and modeling prey escape behavior of birds based on a real-world dataset.
Tasks
Published	2019-10-28
URL	https://arxiv.org/abs/1910.12783v1
PDF	https://arxiv.org/pdf/1910.12783v1.pdf
PWC	https://paperswithcode.com/paper/distributed-estimation-via-network
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Monte-Carlo Tree Search for Policy Optimization


Title	Monte-Carlo Tree Search for Policy Optimization
Authors	Xiaobai Ma, Katherine Driggs-Campbell, Zongzhang Zhang, Mykel J. Kochenderfer
Abstract	Gradient-based methods are often used for policy optimization in deep reinforcement learning, despite being vulnerable to local optima and saddle points. Although gradient-free methods (e.g., genetic algorithms or evolution strategies) help mitigate these issues, poor initialization and local optima are still concerns in highly nonconvex spaces. This paper presents a method for policy optimization based on Monte-Carlo tree search and gradient-free optimization. Our method, called Monte-Carlo tree search for policy optimization (MCTSPO), provides a better exploration-exploitation trade-off through the use of the upper confidence bound heuristic. We demonstrate improved performance on reinforcement learning tasks with deceptive or sparse reward functions compared to popular gradient-based and deep genetic algorithm baselines.
Tasks
Published	2019-12-23
URL	https://arxiv.org/abs/1912.10648v1
PDF	https://arxiv.org/pdf/1912.10648v1.pdf
PWC	https://paperswithcode.com/paper/monte-carlo-tree-search-for-policy
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Leveraging Implicit Expert Knowledge for Non-Circular Machine Learning in Sepsis Prediction


Title	Leveraging Implicit Expert Knowledge for Non-Circular Machine Learning in Sepsis Prediction
Authors	Shigehiko Schamoni, Holger A. Lindner, Verena Schneider-Lindner, Manfred Thiel, Stefan Riezler
Abstract	Sepsis is the leading cause of death in non-coronary intensive care units. Moreover, a delay of antibiotic treatment of patients with severe sepsis by only few hours is associated with increased mortality. This insight makes accurate models for early prediction of sepsis a key task in machine learning for healthcare. Previous approaches have achieved high AUROC by learning from electronic health records where sepsis labels were defined automatically following established clinical criteria. We argue that the practice of incorporating the clinical criteria that are used to automatically define ground truth sepsis labels as features of severity scoring models is inherently circular and compromises the validity of the proposed approaches. We propose to create an independent ground truth for sepsis research by exploiting implicit knowledge of clinical practitioners via an electronic questionnaire which records attending physicians’ daily judgements of patients’ sepsis status. We show that despite its small size, our dataset allows to achieve state-of-the-art AUROC scores. An inspection of learned weights for standardized features of the linear model lets us infer potentially surprising feature contributions and allows to interpret seemingly counterintuitive findings.
Tasks
Published	2019-09-20
URL	https://arxiv.org/abs/1909.09557v1
PDF	https://arxiv.org/pdf/1909.09557v1.pdf
PWC	https://paperswithcode.com/paper/leveraging-implicit-expert-knowledge-for-non
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xR-EgoPose: Egocentric 3D Human Pose from an HMD Camera


Title	xR-EgoPose: Egocentric 3D Human Pose from an HMD Camera
Authors	Denis Tome, Patrick Peluse, Lourdes Agapito, Hernan Badino
Abstract	We present a new solution to egocentric 3D body pose estimation from monocular images captured from a downward looking fish-eye camera installed on the rim of a head mounted virtual reality device. This unusual viewpoint, just 2 cm. away from the user’s face, leads to images with unique visual appearance, characterized by severe self-occlusions and strong perspective distortions that result in a drastic difference in resolution between lower and upper body. Our contribution is two-fold. Firstly, we propose a new encoder-decoder architecture with a novel dual branch decoder designed specifically to account for the varying uncertainty in the 2D joint locations. Our quantitative evaluation, both on synthetic and real-world datasets, shows that our strategy leads to substantial improvements in accuracy over state of the art egocentric pose estimation approaches. Our second contribution is a new large-scale photorealistic synthetic dataset - xR-EgoPose - offering 383K frames of high quality renderings of people with a diversity of skin tones, body shapes, clothing, in a variety of backgrounds and lighting conditions, performing a range of actions. Our experiments show that the high variability in our new synthetic training corpus leads to good generalization to real world footage and to state of the art results on real world datasets with ground truth. Moreover, an evaluation on the Human3.6M benchmark shows that the performance of our method is on par with top performing approaches on the more classic problem of 3D human pose from a third person viewpoint.
Tasks	Egocentric Pose Estimation, Pose Estimation
Published	2019-07-23
URL	https://arxiv.org/abs/1907.10045v1
PDF	https://arxiv.org/pdf/1907.10045v1.pdf
PWC	https://paperswithcode.com/paper/xr-egopose-egocentric-3d-human-pose-from-an
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Quantitative Robustness of Localized Support Vector Machines


Title	Quantitative Robustness of Localized Support Vector Machines
Authors	Florian Dumpert
Abstract	The huge amount of available data nowadays is a challenge for kernel-based machine learning algorithms like SVMs with respect to runtime and storage capacities. Local approaches might help to relieve these issues and to improve statistical accuracy. It has already been shown that these local approaches are consistent and robust in a basic sense. This article refines the analysis of robustness properties towards the so-called influence function which expresses the differentiability of the learning method: We show that there is a differentiable dependency of our locally learned predictor on the underlying distribution. The assumptions of the proven theorems can be verified without knowing anything about this distribution. This makes the results interesting also from an applied point of view.
Tasks
Published	2019-03-01
URL	http://arxiv.org/abs/1903.01334v1
PDF	http://arxiv.org/pdf/1903.01334v1.pdf
PWC	https://paperswithcode.com/paper/quantitative-robustness-of-localized-support
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Framework

Assessing the Robustness of Visual Question Answering


Title	Assessing the Robustness of Visual Question Answering
Authors	Jia-Hong Huang, Modar Alfadly, Bernard Ghanem, Marcel Worring
Abstract	Deep neural networks have been playing an essential role in the task of Visual Question Answering (VQA). Until recently, their accuracy has been the main focus of research. Now there is a trend toward assessing the robustness of these models against adversarial attacks by evaluating the accuracy of these models under increasing levels of noisiness in the inputs of VQA models. In VQA, the attack can target the image and/or the proposed query question, dubbed main question, and yet there is a lack of proper analysis of this aspect of VQA. In this work, we propose a new method that uses semantically related questions, dubbed basic questions, acting as noise to evaluate the robustness of VQA models. We hypothesize that as the similarity of a basic question to the main question decreases, the level of noise increases. To generate a reasonable noise level for a given main question, we rank a pool of basic questions based on their similarity with this main question. We cast this ranking problem as a LASSO optimization problem. We also propose a novel robustness measure Rscore and two large-scale basic question datasets in order to standardize robustness analysis of VQA models. The experimental results demonstrate that the proposed evaluation method is able to effectively analyze the robustness of VQA models. To foster the VQA research, we will publish our proposed datasets.
Tasks	Question Answering, Visual Question Answering
Published	2019-11-30
URL	https://arxiv.org/abs/1912.01452v1
PDF	https://arxiv.org/pdf/1912.01452v1.pdf
PWC	https://paperswithcode.com/paper/assessing-the-robustness-of-visual-question
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Framework

Learning Gaussian Graphical Models with Ordered Weighted L1 Regularization


Title	Learning Gaussian Graphical Models with Ordered Weighted L1 Regularization
Authors	Cody Mazza-Anthony, Bogdan Mazoure, Mark Coates
Abstract	We address the task of identifying densely connected subsets of multivariate Gaussian random variables within a graphical model framework. We propose two novel estimators based on the Ordered Weighted $\ell_1$ (OWL) norm: 1) The Graphical OWL (GOWL) is a penalized likelihood method that applies the OWL norm to the lower triangle components of the precision matrix. 2) The column-by-column Graphical OWL (ccGOWL) estimates the precision matrix by performing OWL regularized linear regressions. Both methods can simultaneously identify highly correlated groups of variables and control the sparsity in the resulting precision matrix. We formulate GOWL such that it solves a composite optimization problem and establish that the estimator has a unique global solution. In addition, we prove sufficient grouping conditions for each column of the ccGOWL precision matrix estimate. We propose proximal descent algorithms to find the optimum for both estimators. For synthetic data where group structure is present, the ccGOWL estimator requires significantly reduced computation and achieves similar or greater accuracy than state-of-the-art estimators. Timing comparisons are presented and demonstrates the superior computational efficiency of the ccGOWL. We illustrate the grouping performance of the ccGOWL method on a cancer gene expression data set and an equities data set.
Tasks
Published	2019-06-06
URL	https://arxiv.org/abs/1906.02719v1
PDF	https://arxiv.org/pdf/1906.02719v1.pdf
PWC	https://paperswithcode.com/paper/learning-gaussian-graphical-models-with-3
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