July 28, 2019

3009 words 15 mins read

Paper Group ANR 166

Community Identity and User Engagement in a Multi-Community Landscape. Markov Chain Lifting and Distributed ADMM. Mind the Gap: A Well Log Data Analysis. Efficient Image Set Classification using Linear Regression based Image Reconstruction. Object-Centric Photometric Bundle Adjustment with Deep Shape Prior. Single Image Super-resolution via a Light …

Community Identity and User Engagement in a Multi-Community Landscape


Title	Community Identity and User Engagement in a Multi-Community Landscape
Authors	Justine Zhang, William L. Hamilton, Cristian Danescu-Niculescu-Mizil, Dan Jurafsky, Jure Leskovec
Abstract	A community’s identity defines and shapes its internal dynamics. Our current understanding of this interplay is mostly limited to glimpses gathered from isolated studies of individual communities. In this work we provide a systematic exploration of the nature of this relation across a wide variety of online communities. To this end we introduce a quantitative, language-based typology reflecting two key aspects of a community’s identity: how distinctive, and how temporally dynamic it is. By mapping almost 300 Reddit communities into the landscape induced by this typology, we reveal regularities in how patterns of user engagement vary with the characteristics of a community. Our results suggest that the way new and existing users engage with a community depends strongly and systematically on the nature of the collective identity it fosters, in ways that are highly consequential to community maintainers. For example, communities with distinctive and highly dynamic identities are more likely to retain their users. However, such niche communities also exhibit much larger acculturation gaps between existing users and newcomers, which potentially hinder the integration of the latter. More generally, our methodology reveals differences in how various social phenomena manifest across communities, and shows that structuring the multi-community landscape can lead to a better understanding of the systematic nature of this diversity.
Tasks
Published	2017-05-26
URL	http://arxiv.org/abs/1705.09665v1
PDF	http://arxiv.org/pdf/1705.09665v1.pdf
PWC	https://paperswithcode.com/paper/community-identity-and-user-engagement-in-a
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Markov Chain Lifting and Distributed ADMM


Title	Markov Chain Lifting and Distributed ADMM
Authors	Guilherme França, José Bento
Abstract	The time to converge to the steady state of a finite Markov chain can be greatly reduced by a lifting operation, which creates a new Markov chain on an expanded state space. For a class of quadratic objectives, we show an analogous behavior where a distributed ADMM algorithm can be seen as a lifting of Gradient Descent algorithm. This provides a deep insight for its faster convergence rate under optimal parameter tuning. We conjecture that this gain is always present, as opposed to the lifting of a Markov chain which sometimes only provides a marginal speedup.
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Published	2017-03-10
URL	http://arxiv.org/abs/1703.03859v1
PDF	http://arxiv.org/pdf/1703.03859v1.pdf
PWC	https://paperswithcode.com/paper/markov-chain-lifting-and-distributed-admm
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Mind the Gap: A Well Log Data Analysis


Title	Mind the Gap: A Well Log Data Analysis
Authors	Rui L. Lopes, Alípio Jorge
Abstract	The main task in oil and gas exploration is to gain an understanding of the distribution and nature of rocks and fluids in the subsurface. Well logs are records of petro-physical data acquired along a borehole, providing direct information about what is in the subsurface. The data collected by logging wells can have significant economic consequences, due to the costs inherent to drilling wells, and the potential return of oil deposits. In this paper, we describe preliminary work aimed at building a general framework for well log prediction. First, we perform a descriptive and exploratory analysis of the gaps in the neutron porosity logs of more than a thousand wells in the North Sea. Then, we generate artificial gaps in the neutron logs that reflect the statistics collected before. Finally, we compare Artificial Neural Networks, Random Forests, and three algorithms of Linear Regression in the prediction of missing gaps on a well-by-well basis.
Tasks
Published	2017-05-10
URL	http://arxiv.org/abs/1705.03669v1
PDF	http://arxiv.org/pdf/1705.03669v1.pdf
PWC	https://paperswithcode.com/paper/mind-the-gap-a-well-log-data-analysis
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Efficient Image Set Classification using Linear Regression based Image Reconstruction


Title	Efficient Image Set Classification using Linear Regression based Image Reconstruction
Authors	Syed Afaq Ali Shah, Uzair Nadeem, Mohammed Bennamoun, Ferdous Sohel, Roberto Togneri
Abstract	We propose a novel image set classification technique using linear regression models. Downsampled gallery image sets are interpreted as subspaces of a high dimensional space to avoid the computationally expensive training step. We estimate regression models for each test image using the class specific gallery subspaces. Images of the test set are then reconstructed using the regression models. Based on the minimum reconstruction error between the reconstructed and the original images, a weighted voting strategy is used to classify the test set. We performed extensive evaluation on the benchmark UCSD/Honda, CMU Mobo and YouTube Celebrity datasets for face classification, and ETH-80 dataset for object classification. The results demonstrate that by using only a small amount of training data, our technique achieved competitive classification accuracy and superior computational speed compared with the state-of-the-art methods.
Tasks	Image Reconstruction, Object Classification
Published	2017-01-10
URL	http://arxiv.org/abs/1701.02485v1
PDF	http://arxiv.org/pdf/1701.02485v1.pdf
PWC	https://paperswithcode.com/paper/efficient-image-set-classification-using
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Object-Centric Photometric Bundle Adjustment with Deep Shape Prior


Title	Object-Centric Photometric Bundle Adjustment with Deep Shape Prior
Authors	Rui Zhu, Chaoyang Wang, Chen-Hsuan Lin, Ziyan Wang, Simon Lucey
Abstract	Reconstructing 3D shapes from a sequence of images has long been a problem of interest in computer vision. Classical Structure from Motion (SfM) methods have attempted to solve this problem through projected point displacement & bundle adjustment. More recently, deep methods have attempted to solve this problem by directly learning a relationship between geometry and appearance. There is, however, a significant gap between these two strategies. SfM tackles the problem from purely a geometric perspective, taking no account of the object shape prior. Modern deep methods more often throw away geometric constraints altogether, rendering the results unreliable. In this paper we make an effort to bring these two seemingly disparate strategies together. We introduce learned shape prior in the form of deep shape generators into Photometric Bundle Adjustment (PBA) and propose to accommodate full 3D shape generated by the shape prior within the optimization-based inference framework, demonstrating impressive results.
Tasks
Published	2017-11-04
URL	http://arxiv.org/abs/1711.01470v1
PDF	http://arxiv.org/pdf/1711.01470v1.pdf
PWC	https://paperswithcode.com/paper/object-centric-photometric-bundle-adjustment
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Single Image Super-resolution via a Lightweight Residual Convolutional Neural Network


Title	Single Image Super-resolution via a Lightweight Residual Convolutional Neural Network
Authors	Yudong Liang, Ze Yang, Kai Zhang, Yihui He, Jinjun Wang, Nanning Zheng
Abstract	Recent years have witnessed great success of convolutional neural network (CNN) for various problems both in low and high level visions. Especially noteworthy is the residual network which was originally proposed to handle high-level vision problems and enjoys several merits. This paper aims to extend the merits of residual network, such as skip connection induced fast training, for a typical low-level vision problem, i.e., single image super-resolution. In general, the two main challenges of existing deep CNN for supper-resolution lie in the gradient exploding/vanishing problem and large numbers of parameters or computational cost as CNN goes deeper. Correspondingly, the skip connections or identity mapping shortcuts are utilized to avoid gradient exploding/vanishing problem. In addition, the skip connections have naturally centered the activation which led to better performance. To tackle with the second problem, a lightweight CNN architecture which has carefully designed width, depth and skip connections was proposed. In particular, a strategy of gradually varying the shape of network has been proposed for residual network. Different residual architectures for image super-resolution have also been compared. Experimental results have demonstrated that the proposed CNN model can not only achieve state-of-the-art PSNR and SSIM results for single image super-resolution but also produce visually pleasant results. This paper has extended the mmm 2017 oral conference paper with a considerable new analyses and more experiments especially from the perspective of centering activations and ensemble behaviors of residual network.
Tasks	Image Super-Resolution, Super-Resolution
Published	2017-03-23
URL	http://arxiv.org/abs/1703.08173v2
PDF	http://arxiv.org/pdf/1703.08173v2.pdf
PWC	https://paperswithcode.com/paper/single-image-super-resolution-via-a
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Two-stage Algorithm for Fairness-aware Machine Learning


Title	Two-stage Algorithm for Fairness-aware Machine Learning
Authors	Junpei Komiyama, Hajime Shimao
Abstract	Algorithmic decision making process now affects many aspects of our lives. Standard tools for machine learning, such as classification and regression, are subject to the bias in data, and thus direct application of such off-the-shelf tools could lead to a specific group being unfairly discriminated. Removing sensitive attributes of data does not solve this problem because a \textit{disparate impact} can arise when non-sensitive attributes and sensitive attributes are correlated. Here, we study a fair machine learning algorithm that avoids such a disparate impact when making a decision. Inspired by the two-stage least squares method that is widely used in the field of economics, we propose a two-stage algorithm that removes bias in the training data. The proposed algorithm is conceptually simple. Unlike most of existing fair algorithms that are designed for classification tasks, the proposed method is able to (i) deal with regression tasks, (ii) combine explanatory attributes to remove reverse discrimination, and (iii) deal with numerical sensitive attributes. The performance and fairness of the proposed algorithm are evaluated in simulations with synthetic and real-world datasets.
Tasks	Decision Making
Published	2017-10-13
URL	http://arxiv.org/abs/1710.04924v1
PDF	http://arxiv.org/pdf/1710.04924v1.pdf
PWC	https://paperswithcode.com/paper/two-stage-algorithm-for-fairness-aware
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Fast semi-supervised discriminant analysis for binary classification of large data-sets


Title	Fast semi-supervised discriminant analysis for binary classification of large data-sets
Authors	Joris Tavernier, Jaak Simm, Karl Meerbergen, Joerg Kurt Wegner, Hugo Ceulemans, Yves Moreau
Abstract	High-dimensional data requires scalable algorithms. We propose and analyze three scalable and related algorithms for semi-supervised discriminant analysis (SDA). These methods are based on Krylov subspace methods which exploit the data sparsity and the shift-invariance of Krylov subspaces. In addition, the problem definition was improved by adding centralization to the semi-supervised setting. The proposed methods are evaluated on a industry-scale data set from a pharmaceutical company to predict compound activity on target proteins. The results show that SDA achieves good predictive performance and our methods only require a few seconds, significantly improving computation time on previous state of the art.
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Published	2017-09-14
URL	http://arxiv.org/abs/1709.04794v2
PDF	http://arxiv.org/pdf/1709.04794v2.pdf
PWC	https://paperswithcode.com/paper/fast-semi-supervised-discriminant-analysis
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Ultimate SLAM? Combining Events, Images, and IMU for Robust Visual SLAM in HDR and High Speed Scenarios


Title	Ultimate SLAM? Combining Events, Images, and IMU for Robust Visual SLAM in HDR and High Speed Scenarios
Authors	Antoni Rosinol Vidal, Henri Rebecq, Timo Horstschaefer, Davide Scaramuzza
Abstract	Event cameras are bio-inspired vision sensors that output pixel-level brightness changes instead of standard intensity frames. These cameras do not suffer from motion blur and have a very high dynamic range, which enables them to provide reliable visual information during high speed motions or in scenes characterized by high dynamic range. However, event cameras output only little information when the amount of motion is limited, such as in the case of almost still motion. Conversely, standard cameras provide instant and rich information about the environment most of the time (in low-speed and good lighting scenarios), but they fail severely in case of fast motions, or difficult lighting such as high dynamic range or low light scenes. In this paper, we present the first state estimation pipeline that leverages the complementary advantages of these two sensors by fusing in a tightly-coupled manner events, standard frames, and inertial measurements. We show on the publicly available Event Camera Dataset that our hybrid pipeline leads to an accuracy improvement of 130% over event-only pipelines, and 85% over standard-frames-only visual-inertial systems, while still being computationally tractable. Furthermore, we use our pipeline to demonstrate - to the best of our knowledge - the first autonomous quadrotor flight using an event camera for state estimation, unlocking flight scenarios that were not reachable with traditional visual-inertial odometry, such as low-light environments and high-dynamic range scenes.
Tasks
Published	2017-09-19
URL	http://arxiv.org/abs/1709.06310v4
PDF	http://arxiv.org/pdf/1709.06310v4.pdf
PWC	https://paperswithcode.com/paper/ultimate-slam-combining-events-images-and-imu
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Adaptive Clustering Using Kernel Density Estimators


Title	Adaptive Clustering Using Kernel Density Estimators
Authors	Ingo Steinwart, Bharath K. Sriperumbudur, Philipp Thomann
Abstract	We derive and analyze a generic, recursive algorithm for estimating all splits in a finite cluster tree as well as the corresponding clusters. We further investigate statistical properties of this generic clustering algorithm when it receives level set estimates from a kernel density estimator. In particular, we derive finite sample guarantees, consistency, rates of convergence, and an adaptive data-driven strategy for choosing the kernel bandwidth. For these results we do not need continuity assumptions on the density such as H"{o}lder continuity, but only require intuitive geometric assumptions of non-parametric nature.
Tasks
Published	2017-08-17
URL	https://arxiv.org/abs/1708.05254v2
PDF	https://arxiv.org/pdf/1708.05254v2.pdf
PWC	https://paperswithcode.com/paper/adaptive-clustering-using-kernel-density
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Classical Music Clustering Based on Acoustic Features


Title	Classical Music Clustering Based on Acoustic Features
Authors	Xindi Wang, Syed Arefinul Haque
Abstract	In this paper we cluster 330 classical music pieces collected from MusicNet database based on their musical note sequence. We use shingling and chord trajectory matrices to create signature for each music piece and performed spectral clustering to find the clusters. Based on different resolution, the output clusters distinctively indicate composition from different classical music era and different composing style of the musicians.
Tasks
Published	2017-06-27
URL	http://arxiv.org/abs/1706.08928v1
PDF	http://arxiv.org/pdf/1706.08928v1.pdf
PWC	https://paperswithcode.com/paper/classical-music-clustering-based-on-acoustic
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Unsupervised End-to-end Learning for Deformable Medical Image Registration


Title	Unsupervised End-to-end Learning for Deformable Medical Image Registration
Authors	Siyuan Shan, Wen Yan, Xiaoqing Guo, Eric I-Chao Chang, Yubo Fan, Yan Xu
Abstract	We propose a registration algorithm for 2D CT/MRI medical images with a new unsupervised end-to-end strategy using convolutional neural networks. The contributions of our algorithm are threefold: (1) We transplant traditional image registration algorithms to an end-to-end convolutional neural network framework, while maintaining the unsupervised nature of image registration problems. The image-to-image integrated framework can simultaneously learn both image features and transformation matrix for registration. (2) Training with additional data without any label can further improve the registration performance by approximately 10 %. (3) The registration speed is 100x faster than traditional methods. The proposed network is easy to implement and can be trained efficiently. Experiments demonstrate that our system achieves state-of-the-art results on 2D brain registration and achieves comparable results on 2D liver registration. It can be extended to register other organs beyond liver and brain such as kidney, lung, and heart.
Tasks	Deformable Medical Image Registration, Image Registration, Medical Image Registration
Published	2017-11-23
URL	http://arxiv.org/abs/1711.08608v2
PDF	http://arxiv.org/pdf/1711.08608v2.pdf
PWC	https://paperswithcode.com/paper/unsupervised-end-to-end-learning-for
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Machine Learning for Building Energy and Indoor Environment: A Perspective


Title	Machine Learning for Building Energy and Indoor Environment: A Perspective
Authors	Zhijian Liu, Di Wu, Hongyu Wei, Guoqing Cao
Abstract	Machine learning is a promising technique for many practical applications. In this perspective, we illustrate the development and application for machine learning. It is indicated that the theories and applications of machine learning method in the field of energy conservation and indoor environment are not mature, due to the difficulty of the determination for model structure with better prediction. In order to significantly contribute to the problems, we utilize the ANN model to predict the indoor culturable fungi concentration, which achieves the better accuracy and convenience. The proposal of hybrid method is further expand the application fields of machine learning method. Further, ANN model based on HTS was successfully applied for the optimization of building energy system. We hope that this novel method could capture more attention from investigators via our introduction and perspective, due to its potential development with accuracy and reliability. However, its feasibility in other fields needs to be promoted further.
Tasks
Published	2017-12-31
URL	http://arxiv.org/abs/1801.00779v1
PDF	http://arxiv.org/pdf/1801.00779v1.pdf
PWC	https://paperswithcode.com/paper/machine-learning-for-building-energy-and
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Inductive Representation Learning in Large Attributed Graphs


Title	Inductive Representation Learning in Large Attributed Graphs
Authors	Nesreen K. Ahmed, Ryan A. Rossi, Rong Zhou, John Boaz Lee, Xiangnan Kong, Theodore L. Willke, Hoda Eldardiry
Abstract	Graphs (networks) are ubiquitous and allow us to model entities (nodes) and the dependencies (edges) between them. Learning a useful feature representation from graph data lies at the heart and success of many machine learning tasks such as classification, anomaly detection, link prediction, among many others. Many existing techniques use random walks as a basis for learning features or estimating the parameters of a graph model for a downstream prediction task. Examples include recent node embedding methods such as DeepWalk, node2vec, as well as graph-based deep learning algorithms. However, the simple random walk used by these methods is fundamentally tied to the identity of the node. This has three main disadvantages. First, these approaches are inherently transductive and do not generalize to unseen nodes and other graphs. Second, they are not space-efficient as a feature vector is learned for each node which is impractical for large graphs. Third, most of these approaches lack support for attributed graphs. To make these methods more generally applicable, we propose a framework for inductive network representation learning based on the notion of attributed random walk that is not tied to node identity and is instead based on learning a function $\Phi : \mathrm{\rm \bf x} \rightarrow w$ that maps a node attribute vector $\mathrm{\rm \bf x}$ to a type $w$. This framework serves as a basis for generalizing existing methods such as DeepWalk, node2vec, and many other previous methods that leverage traditional random walks.
Tasks	Anomaly Detection, Link Prediction, Representation Learning
Published	2017-10-25
URL	http://arxiv.org/abs/1710.09471v2
PDF	http://arxiv.org/pdf/1710.09471v2.pdf
PWC	https://paperswithcode.com/paper/inductive-representation-learning-in-large
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Im2Pano3D: Extrapolating 360 Structure and Semantics Beyond the Field of View


Title	Im2Pano3D: Extrapolating 360 Structure and Semantics Beyond the Field of View
Authors	Shuran Song, Andy Zeng, Angel X. Chang, Manolis Savva, Silvio Savarese, Thomas Funkhouser
Abstract	We present Im2Pano3D, a convolutional neural network that generates a dense prediction of 3D structure and a probability distribution of semantic labels for a full 360 panoramic view of an indoor scene when given only a partial observation (<= 50%) in the form of an RGB-D image. To make this possible, Im2Pano3D leverages strong contextual priors learned from large-scale synthetic and real-world indoor scenes. To ease the prediction of 3D structure, we propose to parameterize 3D surfaces with their plane equations and train the model to predict these parameters directly. To provide meaningful training supervision, we use multiple loss functions that consider both pixel level accuracy and global context consistency. Experiments demon- strate that Im2Pano3D is able to predict the semantics and 3D structure of the unobserved scene with more than 56% pixel accuracy and less than 0.52m average distance error, which is significantly better than alternative approaches.
Tasks
Published	2017-12-12
URL	http://arxiv.org/abs/1712.04569v1
PDF	http://arxiv.org/pdf/1712.04569v1.pdf
PWC	https://paperswithcode.com/paper/im2pano3d-extrapolating-360-structure-and
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