Paper Group ANR 770
Global Fitting of the Response Surface via Estimating Multiple Contours of a Simulator. Visualizing High Dimensional Dynamical Processes. Deep Learning Approach on Information Diffusion in Heterogeneous Networks. A Convolutional Forward and Back-Projection Model for Fan-Beam Geometry. L 1-norm double backpropagation adversarial defense. Perceptuall …
Global Fitting of the Response Surface via Estimating Multiple Contours of a Simulator
| Title | Global Fitting of the Response Surface via Estimating Multiple Contours of a Simulator |
| Authors | Feng Yang, C. Devon Lin, Pritam Ranjan |
| Abstract | Computer simulators are nowadays widely used to understand complex physical systems in many areas such as aerospace, renewable energy, climate modeling, and manufacturing. One fundamental issue in the study of computer simulators is known as experimental design, that is, how to select the input settings where the computer simulator is run and the corresponding response is collected. Extra care should be taken in the selection process because computer simulators can be computationally expensive to run. The selection shall acknowledge and achieve the goal of the analysis. This article focuses on the goal of producing more accurate prediction which is important for risk assessment and decision making. We propose two new methods of design approaches that sequentially select input settings to achieve this goal. The approaches make novel applications of simultaneous and sequential contour estimations. Numerical examples are employed to demonstrate the effectiveness of the proposed approaches. |
| Tasks | Decision Making |
| Published | 2019-02-04 |
| URL | http://arxiv.org/abs/1902.01011v1 |
| http://arxiv.org/pdf/1902.01011v1.pdf | |
| PWC | https://paperswithcode.com/paper/global-fitting-of-the-response-surface-via |
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Visualizing High Dimensional Dynamical Processes
| Title | Visualizing High Dimensional Dynamical Processes |
| Authors | Andrés F. Duque, Guy Wolf, Kevin R. Moon |
| Abstract | Manifold learning techniques for dynamical systems and time series have shown their utility for a broad spectrum of applications in recent years. While these methods are effective at learning a low-dimensional representation, they are often insufficient for visualizing the global and local structure of the data. In this paper, we present DIG (Dynamical Information Geometry), a visualization method for multivariate time series data that extracts an information geometry from a diffusion framework. Specifically, we implement a novel group of distances in the context of diffusion operators, which may be useful to reveal structure in the data that may not be accessible by the commonly used diffusion distances. Finally, we present a case study applying our visualization tool to EEG data to visualize sleep stages. |
| Tasks | EEG, Time Series |
| Published | 2019-06-25 |
| URL | https://arxiv.org/abs/1906.10725v1 |
| https://arxiv.org/pdf/1906.10725v1.pdf | |
| PWC | https://paperswithcode.com/paper/visualizing-high-dimensional-dynamical |
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Deep Learning Approach on Information Diffusion in Heterogeneous Networks
| Title | Deep Learning Approach on Information Diffusion in Heterogeneous Networks |
| Authors | Soheila Molaei, Hadi Zare, Hadi Veisi |
| Abstract | There are many real-world knowledge based networked systems with multi-type interacting entities that can be regarded as heterogeneous networks including human connections and biological evolutions. One of the main issues in such networks is to predict information diffusion such as shape, growth and size of social events and evolutions in the future. While there exist a variety of works on this topic mainly using a threshold-based approach, they suffer from the local viewpoint on the network and sensitivity to the threshold parameters. In this paper, information diffusion is considered through a latent representation learning of the heterogeneous networks to encode in a deep learning model. To this end, we propose a novel meta-path representation learning approach, Heterogeneous Deep Diffusion(HDD), to exploit meta-paths as main entities in networks. At first, the functional heterogeneous structures of the network are learned by a continuous latent representation through traversing meta-paths with the aim of global end-to-end viewpoint. Then, the well-known deep learning architectures are employed on our generated features to predict diffusion processes in the network. The proposed approach enables us to apply it on different information diffusion tasks such as topic diffusion and cascade prediction. We demonstrate the proposed approach on benchmark network datasets through the well-known evaluation measures. The experimental results show that our approach outperforms the earlier state-of-the-art methods. |
| Tasks | Representation Learning |
| Published | 2019-02-23 |
| URL | https://arxiv.org/abs/1902.08810v2 |
| https://arxiv.org/pdf/1902.08810v2.pdf | |
| PWC | https://paperswithcode.com/paper/deep-learning-approach-on-information |
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A Convolutional Forward and Back-Projection Model for Fan-Beam Geometry
| Title | A Convolutional Forward and Back-Projection Model for Fan-Beam Geometry |
| Authors | Kai Zhang, Alireza Entezari |
| Abstract | Iterative methods for tomographic image reconstruction have great potential for enabling high quality imaging from low-dose projection data. The computational burden of iterative reconstruction algorithms, however, has been an impediment in their adoption in practical CT reconstruction problems. We present an approach for highly efficient and accurate computation of forward model for image reconstruction in fan-beam geometry in X-ray CT. The efficiency of computations makes this approach suitable for large-scale optimization algorithms with on-the-fly, memory-less, computations of the forward and back-projection. Our experiments demonstrate the improvements in accuracy as well as efficiency of our model, specifically for first-order box splines (i.e., pixel-basis) compared to recently developed methods for this purpose, namely Look-up Table-based Ray Integration (LTRI) and Separable Footprints (SF) in 2-D. |
| Tasks | Image Reconstruction |
| Published | 2019-07-24 |
| URL | https://arxiv.org/abs/1907.10526v1 |
| https://arxiv.org/pdf/1907.10526v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-convolutional-forward-and-back-projection |
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L 1-norm double backpropagation adversarial defense
| Title | L 1-norm double backpropagation adversarial defense |
| Authors | Ismaïla Seck, Gaëlle Loosli, Stephane Canu |
| Abstract | Adversarial examples are a challenging open problem for deep neural networks. We propose in this paper to add a penalization term that forces the decision function to be at in some regions of the input space, such that it becomes, at least locally, less sensitive to attacks. Our proposition is theoretically motivated and shows on a first set of carefully conducted experiments that it behaves as expected when used alone, and seems promising when coupled with adversarial training. |
| Tasks | Adversarial Defense |
| Published | 2019-03-05 |
| URL | http://arxiv.org/abs/1903.01715v1 |
| http://arxiv.org/pdf/1903.01715v1.pdf | |
| PWC | https://paperswithcode.com/paper/l-1-norm-double-backpropagation-adversarial |
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Perceptually Motivated Method for Image Inpainting Comparison
| Title | Perceptually Motivated Method for Image Inpainting Comparison |
| Authors | Ivan Molodetskikh, Mikhail Erofeev, Dmitry Vatolin |
| Abstract | The field of automatic image inpainting has progressed rapidly in recent years, but no one has yet proposed a standard method of evaluating algorithms. This absence is due to the problem’s challenging nature: image-inpainting algorithms strive for realism in the resulting images, but realism is a subjective concept intrinsic to human perception. Existing objective image-quality metrics provide a poor approximation of what humans consider more or less realistic. To improve the situation and to better organize both prior and future research in this field, we conducted a subjective comparison of nine state-of-the-art inpainting algorithms and propose objective quality metrics that exhibit high correlation with the results of our comparison. |
| Tasks | Image Inpainting |
| Published | 2019-07-14 |
| URL | https://arxiv.org/abs/1907.06296v1 |
| https://arxiv.org/pdf/1907.06296v1.pdf | |
| PWC | https://paperswithcode.com/paper/perceptually-motivated-method-for-image |
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Outlier Detection and Data Clustering via Innovation Search
| Title | Outlier Detection and Data Clustering via Innovation Search |
| Authors | Mostafa Rahmani, Ping Li |
| Abstract | The idea of Innovation Search was proposed as a data clustering method in which the directions of innovation were utilized to compute the adjacency matrix and it was shown that Innovation Pursuit can notably outperform the self representation based subspace clustering methods. In this paper, we present a new discovery that the directions of innovation can be used to design a provable and strong robust (to outlier) PCA method. The proposed approach, dubbed iSearch, uses the direction search optimization problem to compute an optimal direction corresponding to each data point. iSearch utilizes the directions of innovation to measure the innovation of the data points and it identifies the outliers as the most innovative data points. Analytical performance guarantees are derived for the proposed robust PCA method under different models for the distribution of the outliers including randomly distributed outliers, clustered outliers, and linearly dependent outliers. In addition, we study the problem of outlier detection in a union of subspaces and it is shown that iSearch provably recovers the span of the inliers when the inliers lie in a union of subspaces. Moreover, we present theoretical studies which show that the proposed measure of innovation remains stable in the presence of noise and the performance of iSearch is robust to noisy data. In the challenging scenarios in which the outliers are close to each other or they are close to the span of the inliers, iSearch is shown to remarkably outperform most of the existing methods. The presented method shows that the directions of innovation are useful representation of the data which can be used to perform both data clustering and outlier detection. |
| Tasks | Outlier Detection |
| Published | 2019-12-30 |
| URL | https://arxiv.org/abs/1912.12988v1 |
| https://arxiv.org/pdf/1912.12988v1.pdf | |
| PWC | https://paperswithcode.com/paper/outlier-detection-and-data-clustering-via |
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A Comparison of Super-Resolution and Nearest Neighbors Interpolation Applied to Object Detection on Satellite Data
| Title | A Comparison of Super-Resolution and Nearest Neighbors Interpolation Applied to Object Detection on Satellite Data |
| Authors | Evan Koester, Cem Safak Sahin |
| Abstract | As Super-Resolution (SR) has matured as a research topic, it has been applied to additional topics beyond image reconstruction. In particular, combining classification or object detection tasks with a super-resolution preprocessing stage has yielded improvements in accuracy especially with objects that are small relative to the scene. While SR has shown promise, a study comparing SR and naive upscaling methods such as Nearest Neighbors (NN) interpolation when applied as a preprocessing step for object detection has not been performed. We apply the topic to satellite data and compare the Multi-scale Deep Super-Resolution (MDSR) system to NN on the xView challenge dataset. To do so, we propose a pipeline for processing satellite data that combines multi-stage image tiling and upscaling, the YOLOv2 object detection architecture, and label stitching. We compare the effects of training models using an upscaling factor of 4, upscaling images from 30cm Ground Sample Distance (GSD) to an effective GSD of 7.5cm. Upscaling by this factor significantly improves detection results, increasing Average Precision (AP) of a generalized vehicle class by 23 percent. We demonstrate that while SR produces upscaled images that are more visually pleasing than their NN counterparts, object detection networks see little difference in accuracy with images upsampled using NN obtaining nearly identical results to the MDSRx4 enhanced images with a difference of 0.0002 AP between the two methods. |
| Tasks | Image Reconstruction, Object Detection, Super-Resolution |
| Published | 2019-07-08 |
| URL | https://arxiv.org/abs/1907.05283v1 |
| https://arxiv.org/pdf/1907.05283v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-comparison-of-super-resolution-and-nearest |
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Learning Domain Adaptive Features with Unlabeled Domain Bridges
| Title | Learning Domain Adaptive Features with Unlabeled Domain Bridges |
| Authors | Yichen Li, Xingchao Peng |
| Abstract | Conventional cross-domain image-to-image translation or unsupervised domain adaptation methods assume that the source domain and target domain are closely related. This neglects a practical scenario where the domain discrepancy between the source and target is excessively large. In this paper, we propose a novel approach to learn domain adaptive features between the largely-gapped source and target domains with unlabeled domain bridges. Firstly, we introduce the framework of Cycle-consistency Flow Generative Adversarial Networks (CFGAN) that utilizes domain bridges to perform image-to-image translation between two distantly distributed domains. Secondly, we propose the Prototypical Adversarial Domain Adaptation (PADA) model which utilizes unlabeled bridge domains to align feature distribution between source and target with a large discrepancy. Extensive quantitative and qualitative experiments are conducted to demonstrate the effectiveness of our proposed models. |
| Tasks | Domain Adaptation, Image-to-Image Translation, Unsupervised Domain Adaptation |
| Published | 2019-12-10 |
| URL | https://arxiv.org/abs/1912.05004v1 |
| https://arxiv.org/pdf/1912.05004v1.pdf | |
| PWC | https://paperswithcode.com/paper/learning-domain-adaptive-features-with |
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Polynomial time guarantees for the Burer-Monteiro method
| Title | Polynomial time guarantees for the Burer-Monteiro method |
| Authors | Diego Cifuentes, Ankur Moitra |
| Abstract | The Burer-Monteiro method is one of the most widely used techniques for solving large-scale semidefinite programs (SDP). The basic idea is to solve a nonconvex program in $Y$, where $Y$ is an $n \times p$ matrix such that $X = Y Y^T$. In this paper, we show that this method can solve SDPs in polynomial time in an smoothed analysis setting. More precisely, we consider an SDP whose domain satisfies some compactness and smoothness assumptions, and slightly perturb the cost matrix and the constraints. We show that if $p \gtrsim \sqrt{2(1+\eta)m}$, where $m$ is the number of constraints and $\eta>0$ is any fixed constant, then the Burer-Monteiro method can solve SDPs to any desired accuracy in polynomial time, in the setting of smooth analysis. Our bound on $p$ approaches the celebrated Barvinok-Pataki bound in the limit as $\eta$ goes to zero, beneath which it is known that the nonconvex program can be suboptimal. Previous analyses were unable to give polynomial time guarantees for the Burer-Monteiro method, since they either assumed that the criticality conditions are satisfied exactly, or ignored the nontrivial problem of computing an approximately feasible solution. We address the first problem through a novel connection with tubular neighborhoods of algebraic varieties. For the feasibility problem we consider a least squares formulation, and provide the first guarantees that do not rely on the restricted isometry property. |
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| Published | 2019-12-03 |
| URL | https://arxiv.org/abs/1912.01745v1 |
| https://arxiv.org/pdf/1912.01745v1.pdf | |
| PWC | https://paperswithcode.com/paper/polynomial-time-guarantees-for-the-burer |
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Detection of Outlier Events in Continuous-Time Event Sequences
| Title | Detection of Outlier Events in Continuous-Time Event Sequences |
| Authors | Siqi Liu, Milos Hauskrecht |
| Abstract | Continuous-time event sequences represent discrete events occurring in continuous time. Such sequences arise frequently in real-life and cover a wide variety of natural events, such as earthquakes, or events corresponding to human actions, such as medical administrations. Usually we expect the event sequences to follow some regular pattern over time. However, sometimes these regular patterns may be interrupted by unexpected absence or unexpected occurrences of events. Identification of these unexpected cases can be very important as they may point to abnormal situations that need human attention. In this work, we study and develop methods for detecting outliers in continuous-time event sequences, including unexpected absence and unexpected occurrences of events. Since the patterns that event sequences tend to follow may change in different contexts, we develop outlier detection methods based on point processes that take into account different contexts. Our outlier scoring methods are based on Bayesian decision theory and hypothesis testing with theoretical guarantees. To test the performance of the methods, we conduct experiments on both synthetic data and real-world clinical data and show the effectiveness of the proposed methods. |
| Tasks | Outlier Detection, Point Processes |
| Published | 2019-12-19 |
| URL | https://arxiv.org/abs/1912.09522v2 |
| https://arxiv.org/pdf/1912.09522v2.pdf | |
| PWC | https://paperswithcode.com/paper/contextual-outlier-detection-in-continuous |
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Cross-Subject Statistical Shift Estimation for Generalized Electroencephalography-based Mental Workload Assessment
| Title | Cross-Subject Statistical Shift Estimation for Generalized Electroencephalography-based Mental Workload Assessment |
| Authors | Isabela Albuquerque, João Monteiro, Olivier Rosanne, Abhishek Tiwari, Jean-François Gagnon, Tiago H. Falk |
| Abstract | Assessment of mental workload in real world conditions is key to ensure the performance of workers executing tasks which demand sustained attention. Previous literature has employed electroencephalography (EEG) to this end. However, EEG correlates of mental workload vary across subjects and physical strain, thus making it difficult to devise models capable of simultaneously presenting reliable performance across users. The field of domain adaptation (DA) aims at developing methods that allow for generalization across different domains by learning domain-invariant representations. Such DA methods, however, rely on the so-called covariate shift assumption, which typically does not hold for EEG-based applications. As such, in this paper we propose a way to measure the statistical (marginal and conditional) shift observed on data obtained from different users and use this measure to quantitatively assess the effectiveness of different adaptation strategies. In particular, we use EEG data collected from individuals performing a mental task while running in a treadmill and explore the effects of different normalization strategies commonly used to mitigate cross-subject variability. We show the effects that different normalization schemes have on statistical shifts and their relationship with the accuracy of mental workload prediction as assessed on unseen participants at train time. |
| Tasks | Domain Adaptation, EEG |
| Published | 2019-06-20 |
| URL | https://arxiv.org/abs/1906.08823v1 |
| https://arxiv.org/pdf/1906.08823v1.pdf | |
| PWC | https://paperswithcode.com/paper/cross-subject-statistical-shift-estimation |
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Automated detection of business-relevant outliers in e-commerce conversion rate
| Title | Automated detection of business-relevant outliers in e-commerce conversion rate |
| Authors | Rohan Wickramasuriya, Dean Marchiori |
| Abstract | We evaluate how modern outlier detection methods perform in identifying outliers in e-commerce conversion rate data. Based on the limitations identified, we then present a novel method to detect outliers in e-commerce conversion rate. This unsupervised method is made more business relevant by letting it automatically adjust the sensitivity based on the activity observed on the e-commerce platform. We call this outlier detection method the fluid IQR. Using real e-commerce conversion data acquired from a known store, we compare the performance of the existing and the new outlier detection methods. Fluid IQR method outperforms the existing outlier detection methods by a large margin when it comes to business-relevance. Furthermore, the fluids IQR method is the most robust outlier detection method in the presence of clusters of extreme outliers or level shifts. Future research will evaluate how the fluid IQR method perform in diverse e-business settings. |
| Tasks | Outlier Detection |
| Published | 2019-05-15 |
| URL | https://arxiv.org/abs/1905.05938v2 |
| https://arxiv.org/pdf/1905.05938v2.pdf | |
| PWC | https://paperswithcode.com/paper/automated-detection-of-business-relevant |
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Improving generation quality of pointer networks via guided attention
| Title | Improving generation quality of pointer networks via guided attention |
| Authors | Kushal Chawla, Kundan Krishna, Balaji Vasan Srinivasan |
| Abstract | Pointer generator networks have been used successfully for abstractive summarization. Along with the capability to generate novel words, it also allows the model to copy from the input text to handle out-of-vocabulary words. In this paper, we point out two key shortcomings of the summaries generated with this framework via manual inspection, statistical analysis and human evaluation. The first shortcoming is the extractive nature of the generated summaries, since the network eventually learns to copy from the input article most of the times, affecting the abstractive nature of the generated summaries. The second shortcoming is the factual inaccuracies in the generated text despite grammatical correctness. Our analysis indicates that this arises due to incorrect attention transition between different parts of the article. We propose an initial attempt towards addressing both these shortcomings by externally appending traditional linguistic information parsed from the input text, thereby teaching networks on the structure of the underlying text. Results indicate feasibility and potential of such additional cues for improved generation. |
| Tasks | Abstractive Text Summarization |
| Published | 2019-01-20 |
| URL | http://arxiv.org/abs/1901.11492v1 |
| http://arxiv.org/pdf/1901.11492v1.pdf | |
| PWC | https://paperswithcode.com/paper/improving-generation-quality-of-pointer |
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Semi-parametric dynamic contextual pricing
| Title | Semi-parametric dynamic contextual pricing |
| Authors | Virag Shah, Jose Blanchet, Ramesh Johari |
| Abstract | Motivated by the application of real-time pricing in e-commerce platforms, we consider the problem of revenue-maximization in a setting where the seller can leverage contextual information describing the customer’s history and the product’s type to predict her valuation of the product. However, her true valuation is unobservable to the seller, only binary outcome in the form of success-failure of a transaction is observed. Unlike in usual contextual bandit settings, the optimal price/arm given a covariate in our setting is sensitive to the detailed characteristics of the residual uncertainty distribution. We develop a semi-parametric model in which the residual distribution is non-parametric and provide the first algorithm which learns both regression parameters and residual distribution with $\tilde O(\sqrt{n})$ regret. We empirically test a scalable implementation of our algorithm and observe good performance. |
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| Published | 2019-01-07 |
| URL | https://arxiv.org/abs/1901.02045v4 |
| https://arxiv.org/pdf/1901.02045v4.pdf | |
| PWC | https://paperswithcode.com/paper/semi-parametric-dynamic-contextual-pricing |
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