Paper Group ANR 250
Single-Pass, Adaptive Natural Language Filtering: Measuring Value in User Generated Comments on Large-Scale, Social Media News Forums. Minimax Estimation of Bandable Precision Matrices. Reconstruction-Based Disentanglement for Pose-invariant Face Recognition. Fair Forests: Regularized Tree Induction to Minimize Model Bias. A Multi-Objective Learnin …
Single-Pass, Adaptive Natural Language Filtering: Measuring Value in User Generated Comments on Large-Scale, Social Media News Forums
| Title | Single-Pass, Adaptive Natural Language Filtering: Measuring Value in User Generated Comments on Large-Scale, Social Media News Forums |
| Authors | Manuel Amunategui |
| Abstract | There are large amounts of insight and social discovery potential in mining crowd-sourced comments left on popular news forums like Reddit.com, Tumblr.com, Facebook.com and Hacker News. Unfortunately, due the overwhelming amount of participation with its varying quality of commentary, extracting value out of such data isn’t always obvious nor timely. By designing efficient, single-pass and adaptive natural language filters to quickly prune spam, noise, copy-cats, marketing diversions, and out-of-context posts, we can remove over a third of entries and return the comments with a higher probability of relatedness to the original article in question. The approach presented here uses an adaptive, two-step filtering process. It first leverages the original article posted in the thread as a starting corpus to parse comments by matching intersecting words and term-ratio balance per sentence then grows the corpus by adding new words harvested from high-matching comments to increase filtering accuracy over time. |
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| Published | 2017-01-12 |
| URL | http://arxiv.org/abs/1701.03231v1 |
| http://arxiv.org/pdf/1701.03231v1.pdf | |
| PWC | https://paperswithcode.com/paper/single-pass-adaptive-natural-language |
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Minimax Estimation of Bandable Precision Matrices
| Title | Minimax Estimation of Bandable Precision Matrices |
| Authors | Addison Hu, Sahand Negahban |
| Abstract | The inverse covariance matrix provides considerable insight for understanding statistical models in the multivariate setting. In particular, when the distribution over variables is assumed to be multivariate normal, the sparsity pattern in the inverse covariance matrix, commonly referred to as the precision matrix, corresponds to the adjacency matrix representation of the Gauss-Markov graph, which encodes conditional independence statements between variables. Minimax results under the spectral norm have previously been established for covariance matrices, both sparse and banded, and for sparse precision matrices. We establish minimax estimation bounds for estimating banded precision matrices under the spectral norm. Our results greatly improve upon the existing bounds; in particular, we find that the minimax rate for estimating banded precision matrices matches that of estimating banded covariance matrices. The key insight in our analysis is that we are able to obtain barely-noisy estimates of $k \times k$ subblocks of the precision matrix by inverting slightly wider blocks of the empirical covariance matrix along the diagonal. Our theoretical results are complemented by experiments demonstrating the sharpness of our bounds. |
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| Published | 2017-10-19 |
| URL | http://arxiv.org/abs/1710.07006v1 |
| http://arxiv.org/pdf/1710.07006v1.pdf | |
| PWC | https://paperswithcode.com/paper/minimax-estimation-of-bandable-precision |
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Reconstruction-Based Disentanglement for Pose-invariant Face Recognition
| Title | Reconstruction-Based Disentanglement for Pose-invariant Face Recognition |
| Authors | Xi Peng, Xiang Yu, Kihyuk Sohn, Dimitris Metaxas, Manmohan Chandraker |
| Abstract | Deep neural networks (DNNs) trained on large-scale datasets have recently achieved impressive improvements in face recognition. But a persistent challenge remains to develop methods capable of handling large pose variations that are relatively underrepresented in training data. This paper presents a method for learning a feature representation that is invariant to pose, without requiring extensive pose coverage in training data. We first propose to generate non-frontal views from a single frontal face, in order to increase the diversity of training data while preserving accurate facial details that are critical for identity discrimination. Our next contribution is to seek a rich embedding that encodes identity features, as well as non-identity ones such as pose and landmark locations. Finally, we propose a new feature reconstruction metric learning to explicitly disentangle identity and pose, by demanding alignment between the feature reconstructions through various combinations of identity and pose features, which is obtained from two images of the same subject. Experiments on both controlled and in-the-wild face datasets, such as MultiPIE, 300WLP and the profile view database CFP, show that our method consistently outperforms the state-of-the-art, especially on images with large head pose variations. Detail results and resource are referred to https://sites.google.com/site/xipengcshomepage/iccv2017 |
| Tasks | Face Recognition, Metric Learning, Robust Face Recognition |
| Published | 2017-02-10 |
| URL | http://arxiv.org/abs/1702.03041v2 |
| http://arxiv.org/pdf/1702.03041v2.pdf | |
| PWC | https://paperswithcode.com/paper/reconstruction-based-disentanglement-for-pose |
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Fair Forests: Regularized Tree Induction to Minimize Model Bias
| Title | Fair Forests: Regularized Tree Induction to Minimize Model Bias |
| Authors | Edward Raff, Jared Sylvester, Steven Mills |
| Abstract | The potential lack of fairness in the outputs of machine learning algorithms has recently gained attention both within the research community as well as in society more broadly. Surprisingly, there is no prior work developing tree-induction algorithms for building fair decision trees or fair random forests. These methods have widespread popularity as they are one of the few to be simultaneously interpretable, non-linear, and easy-to-use. In this paper we develop, to our knowledge, the first technique for the induction of fair decision trees. We show that our “Fair Forest” retains the benefits of the tree-based approach, while providing both greater accuracy and fairness than other alternatives, for both “group fairness” and “individual fairness.'” We also introduce new measures for fairness which are able to handle multinomial and continues attributes as well as regression problems, as opposed to binary attributes and labels only. Finally, we demonstrate a new, more robust evaluation procedure for algorithms that considers the dataset in its entirety rather than only a specific protected attribute. |
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| Published | 2017-12-21 |
| URL | http://arxiv.org/abs/1712.08197v1 |
| http://arxiv.org/pdf/1712.08197v1.pdf | |
| PWC | https://paperswithcode.com/paper/fair-forests-regularized-tree-induction-to |
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A Multi-Objective Learning to re-Rank Approach to Optimize Online Marketplaces for Multiple Stakeholders
| Title | A Multi-Objective Learning to re-Rank Approach to Optimize Online Marketplaces for Multiple Stakeholders |
| Authors | Phong Nguyen, John Dines, Jan Krasnodebski |
| Abstract | Multi-objective recommender systems address the difficult task of recommending items that are relevant to multiple, possibly conflicting, criteria. However these systems are most often designed to address the objective of one single stakeholder, typically, in online commerce, the consumers whose input and purchasing decisions ultimately determine the success of the recommendation systems. In this work, we address the multi-objective, multi-stakeholder, recommendation problem involving one or more objective(s) per stakeholder. In addition to the consumer stakeholder, we also consider two other stakeholders; the suppliers who provide the goods and services for sale and the intermediary who is responsible for helping connect consumers to suppliers via its recommendation algorithms. We analyze the multi-objective, multi-stakeholder, problem from the point of view of the online marketplace intermediary whose objective is to maximize its commission through its recommender system. We define a multi-objective problem relating all our three stakeholders which we solve with a novel learning-to-re-rank approach that makes use of a novel regularization function based on the Kendall tau correlation metric and its kernel version; given an initial ranking of item recommendations built for the consumer, we aim to re-rank it such that the new ranking is also optimized for the secondary objectives while staying close to the initial ranking. We evaluate our approach on a real-world dataset of hotel recommendations provided by Expedia where we show the effectiveness of our approach against a business-rules oriented baseline model. |
| Tasks | Recommendation Systems |
| Published | 2017-08-02 |
| URL | http://arxiv.org/abs/1708.00651v2 |
| http://arxiv.org/pdf/1708.00651v2.pdf | |
| PWC | https://paperswithcode.com/paper/a-multi-objective-learning-to-re-rank |
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Single Image Super Resolution - When Model Adaptation Matters
| Title | Single Image Super Resolution - When Model Adaptation Matters |
| Authors | Yudong Liang, Radu Timofte, Jinjun Wang, Yihong Gong, Nanning Zheng |
| Abstract | In the recent years impressive advances were made for single image super-resolution. Deep learning is behind a big part of this success. Deep(er) architecture design and external priors modeling are the key ingredients. The internal contents of the low resolution input image is neglected with deep modeling despite the earlier works showing the power of using such internal priors. In this paper we propose a novel deep convolutional neural network carefully designed for robustness and efficiency at both learning and testing. Moreover, we propose a couple of model adaptation strategies to the internal contents of the low resolution input image and analyze their strong points and weaknesses. By trading runtime and using internal priors we achieve 0.1 up to 0.3dB PSNR improvements over best reported results on standard datasets. Our adaptation especially favors images with repetitive structures or under large resolutions. Moreover, it can be combined with other simple techniques, such as back-projection or enhanced prediction, for further improvements. |
| Tasks | Image Super-Resolution, Super-Resolution |
| Published | 2017-03-31 |
| URL | http://arxiv.org/abs/1703.10889v1 |
| http://arxiv.org/pdf/1703.10889v1.pdf | |
| PWC | https://paperswithcode.com/paper/single-image-super-resolution-when-model |
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A novel total variation model based on kernel functions and its application
| Title | A novel total variation model based on kernel functions and its application |
| Authors | Zhizheng Liang, Lei Zhang, Jin Liu, Yong Zhou |
| Abstract | The total variation (TV) model and its related variants have already been proposed for image processing in previous literature. In this paper a novel total variation model based on kernel functions is proposed. In this novel model, we first map each pixel value of an image into a Hilbert space by using a nonlinear map, and then define a coupled image of an original image in order to construct a kernel function. Finally, the proposed model is solved in a kernel function space instead of in the projecting space from a nonlinear map. For the proposed model, we theoretically show under what conditions the mapping image is in the space of bounded variation when the original image is in the space of bounded variation. It is also found that the proposed model further extends the generalized TV model and the information from three different channels of color images can be fused by adopting various kernel functions. A series of experiments on some gray and color images are carried out to demonstrate the effectiveness of the proposed model. |
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| Published | 2017-11-19 |
| URL | http://arxiv.org/abs/1711.06948v1 |
| http://arxiv.org/pdf/1711.06948v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-novel-total-variation-model-based-on-kernel |
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Multiple Instance Dictionary Learning for Beat-to-Beat Heart Rate Monitoring from Ballistocardiograms
| Title | Multiple Instance Dictionary Learning for Beat-to-Beat Heart Rate Monitoring from Ballistocardiograms |
| Authors | Changzhe Jiao, Bo-Yu Su, Princess Lyons, Alina Zare, K. C. Ho, Marjorie Skubic |
| Abstract | A multiple instance dictionary learning approach, Dictionary Learning using Functions of Multiple Instances (DL-FUMI), is used to perform beat-to-beat heart rate estimation and to characterize heartbeat signatures from ballistocardiogram (BCG) signals collected with a hydraulic bed sensor. DL-FUMI estimates a “heartbeat concept” that represents an individual’s personal ballistocardiogram heartbeat pattern. DL-FUMI formulates heartbeat detection and heartbeat characterization as a multiple instance learning problem to address the uncertainty inherent in aligning BCG signals with ground truth during training. Experimental results show that the estimated heartbeat concept found by DL-FUMI is an effective heartbeat prototype and achieves superior performance over comparison algorithms. |
| Tasks | Dictionary Learning, Heart rate estimation, Multiple Instance Learning |
| Published | 2017-06-11 |
| URL | http://arxiv.org/abs/1706.03373v2 |
| http://arxiv.org/pdf/1706.03373v2.pdf | |
| PWC | https://paperswithcode.com/paper/multiple-instance-dictionary-learning-for |
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Light Source Point Cluster Selection Based Atmosphere Light Estimation
| Title | Light Source Point Cluster Selection Based Atmosphere Light Estimation |
| Authors | Wenbo Zhang, Xiaorong Hou |
| Abstract | Atmosphere light value is a highly critical parameter in defogging algorithms that are based on an atmosphere scattering model. Any error in atmosphere light value will produce a direct impact on the accuracy of scattering computation and thus bring chromatic distortion to restored images. To address this problem, this paper propose a method that relies on clustering statistics to estimate atmosphere light value. It starts by selecting in the original image some potential atmosphere light source points, which are grouped into point clusters by means of clustering technique. From these clusters, a number of clusters containing candidate atmosphere light source points are selected, the points are then analyzed statistically, and the cluster containing the most candidate points is used for estimating atmosphere light value. The mean brightness vector of the candidate atmosphere light points in the chosen point cluster is taken as the estimate of atmosphere light value, while their geometric center in the image is accepted as the location of atmosphere light. Experimental results suggest that this statistics clustering method produces more accurate atmosphere brightness vectors and light source locations. This accuracy translates to, from a subjective perspective, more natural defogging effect on the one hand and to the improvement in various objective image quality indicators on the other hand. |
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| Published | 2017-01-12 |
| URL | http://arxiv.org/abs/1701.03244v1 |
| http://arxiv.org/pdf/1701.03244v1.pdf | |
| PWC | https://paperswithcode.com/paper/light-source-point-cluster-selection-based |
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On the Importance of Consistency in Training Deep Neural Networks
| Title | On the Importance of Consistency in Training Deep Neural Networks |
| Authors | Chengxi Ye, Yezhou Yang, Cornelia Fermuller, Yiannis Aloimonos |
| Abstract | We explain that the difficulties of training deep neural networks come from a syndrome of three consistency issues. This paper describes our efforts in their analysis and treatment. The first issue is the training speed inconsistency in different layers. We propose to address it with an intuitive, simple-to-implement, low footprint second-order method. The second issue is the scale inconsistency between the layer inputs and the layer residuals. We explain how second-order information provides favorable convenience in removing this roadblock. The third and most challenging issue is the inconsistency in residual propagation. Based on the fundamental theorem of linear algebra, we provide a mathematical characterization of the famous vanishing gradient problem. Thus, an important design principle for future optimization and neural network design is derived. We conclude this paper with the construction of a novel contractive neural network. |
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| Published | 2017-08-02 |
| URL | http://arxiv.org/abs/1708.00631v1 |
| http://arxiv.org/pdf/1708.00631v1.pdf | |
| PWC | https://paperswithcode.com/paper/on-the-importance-of-consistency-in-training |
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Online Learning with Many Experts
| Title | Online Learning with Many Experts |
| Authors | Alon Cohen, Shie Mannor |
| Abstract | We study the problem of prediction with expert advice when the number of experts in question may be extremely large or even infinite. We devise an algorithm that obtains a tight regret bound of $\widetilde{O}(\epsilon T + N + \sqrt{NT})$, where $N$ is the empirical $\epsilon$-covering number of the sequence of loss functions generated by the environment. In addition, we present a hedging procedure that allows us to find the optimal $\epsilon$ in hindsight. Finally, we discuss a few interesting applications of our algorithm. We show how our algorithm is applicable in the approximately low rank experts model of Hazan et al. (2016), and discuss the case of experts with bounded variation, in which there is a surprisingly large gap between the regret bounds obtained in the statistical and online settings. |
| Tasks | |
| Published | 2017-02-25 |
| URL | http://arxiv.org/abs/1702.07870v1 |
| http://arxiv.org/pdf/1702.07870v1.pdf | |
| PWC | https://paperswithcode.com/paper/online-learning-with-many-experts |
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High-Level Concepts for Affective Understanding of Images
| Title | High-Level Concepts for Affective Understanding of Images |
| Authors | Afsheen Rafaqat Ali, Usman Shahid, Mohsen Ali, Jeffrey Ho |
| Abstract | This paper aims to bridge the affective gap between image content and the emotional response of the viewer it elicits by using High-Level Concepts (HLCs). In contrast to previous work that relied solely on low-level features or used convolutional neural network (CNN) as a black-box, we use HLCs generated by pretrained CNNs in an explicit way to investigate the relations/associations between these HLCs and a (small) set of Ekman’s emotional classes. As a proof-of-concept, we first propose a linear admixture model for modeling these relations, and the resulting computational framework allows us to determine the associations between each emotion class and certain HLCs (objects and places). This linear model is further extended to a nonlinear model using support vector regression (SVR) that aims to predict the viewer’s emotional response using both low-level image features and HLCs extracted from images. These class-specific regressors are then assembled into a regressor ensemble that provide a flexible and effective predictor for predicting viewer’s emotional responses from images. Experimental results have demonstrated that our results are comparable to existing methods, with a clear view of the association between HLCs and emotional classes that is ostensibly missing in most existing work. |
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| Published | 2017-05-08 |
| URL | http://arxiv.org/abs/1705.02751v1 |
| http://arxiv.org/pdf/1705.02751v1.pdf | |
| PWC | https://paperswithcode.com/paper/high-level-concepts-for-affective |
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Mobile Keyboard Input Decoding with Finite-State Transducers
| Title | Mobile Keyboard Input Decoding with Finite-State Transducers |
| Authors | Tom Ouyang, David Rybach, Françoise Beaufays, Michael Riley |
| Abstract | We propose a finite-state transducer (FST) representation for the models used to decode keyboard inputs on mobile devices. Drawing from learnings from the field of speech recognition, we describe a decoding framework that can satisfy the strict memory and latency constraints of keyboard input. We extend this framework to support functionalities typically not present in speech recognition, such as literal decoding, autocorrections, word completions, and next word predictions. We describe the general framework of what we call for short the keyboard “FST decoder” as well as the implementation details that are new compared to a speech FST decoder. We demonstrate that the FST decoder enables new UX features such as post-corrections. Finally, we sketch how this decoder can support advanced features such as personalization and contextualization. |
| Tasks | Speech Recognition |
| Published | 2017-04-13 |
| URL | http://arxiv.org/abs/1704.03987v1 |
| http://arxiv.org/pdf/1704.03987v1.pdf | |
| PWC | https://paperswithcode.com/paper/mobile-keyboard-input-decoding-with-finite |
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Generator Reversal
| Title | Generator Reversal |
| Authors | Yannic Kilcher, Aurélien Lucchi, Thomas Hofmann |
| Abstract | We consider the problem of training generative models with deep neural networks as generators, i.e. to map latent codes to data points. Whereas the dominant paradigm combines simple priors over codes with complex deterministic models, we propose instead to use more flexible code distributions. These distributions are estimated non-parametrically by reversing the generator map during training. The benefits include: more powerful generative models, better modeling of latent structure and explicit control of the degree of generalization. |
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| Published | 2017-07-28 |
| URL | http://arxiv.org/abs/1707.09241v1 |
| http://arxiv.org/pdf/1707.09241v1.pdf | |
| PWC | https://paperswithcode.com/paper/generator-reversal |
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Correcting boundary over-exploration deficiencies in Bayesian optimization with virtual derivative sign observations
| Title | Correcting boundary over-exploration deficiencies in Bayesian optimization with virtual derivative sign observations |
| Authors | Eero Siivola, Aki Vehtari, Jarno Vanhatalo, Javier González, Michael Riis Andersen |
| Abstract | Bayesian optimization (BO) is a global optimization strategy designed to find the minimum of an expensive black-box function, typically defined on a compact subset of $\mathcal{R}^d$, by using a Gaussian process (GP) as a surrogate model for the objective. Although currently available acquisition functions address this goal with different degree of success, an over-exploration effect of the contour of the search space is typically observed. However, in problems like the configuration of machine learning algorithms, the function domain is conservatively large and with a high probability the global minimum does not sit on the boundary of the domain. We propose a method to incorporate this knowledge into the search process by adding virtual derivative observations in the \gp at the boundary of the search space. We use the properties of GPs to impose conditions on the partial derivatives of the objective. The method is applicable with any acquisition function, it is easy to use and consistently reduces the number of evaluations required to optimize the objective irrespective of the acquisition used. We illustrate the benefits of our approach in an extensive experimental comparison. |
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| Published | 2017-04-04 |
| URL | http://arxiv.org/abs/1704.00963v3 |
| http://arxiv.org/pdf/1704.00963v3.pdf | |
| PWC | https://paperswithcode.com/paper/correcting-boundary-over-exploration |
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