Paper Group ANR 94
A Single Model Explains both Visual and Auditory Precortical Coding. Probing the Geometry of Data with Diffusion Fréchet Functions. Energy Disaggregation for Real-Time Building Flexibility Detection. Adaptive regularization for Lasso models in the context of non-stationary data streams. A Survey of Qualitative Spatial and Temporal Calculi – Algebr …
A Single Model Explains both Visual and Auditory Precortical Coding
| Title | A Single Model Explains both Visual and Auditory Precortical Coding |
| Authors | Honghao Shan, Matthew H. Tong, Garrison W. Cottrell |
| Abstract | Precortical neural systems encode information collected by the senses, but the driving principles of the encoding used have remained a subject of debate. We present a model of retinal coding that is based on three constraints: information preservation, minimization of the neural wiring, and response equalization. The resulting novel version of sparse principal components analysis successfully captures a number of known characteristics of the retinal coding system, such as center-surround receptive fields, color opponency channels, and spatiotemporal responses that correspond to magnocellular and parvocellular pathways. Furthermore, when trained on auditory data, the same model learns receptive fields well fit by gammatone filters, commonly used to model precortical auditory coding. This suggests that efficient coding may be a unifying principle of precortical encoding across modalities. |
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| Published | 2016-02-26 |
| URL | http://arxiv.org/abs/1602.08486v2 |
| http://arxiv.org/pdf/1602.08486v2.pdf | |
| PWC | https://paperswithcode.com/paper/a-single-model-explains-both-visual-and |
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Probing the Geometry of Data with Diffusion Fréchet Functions
| Title | Probing the Geometry of Data with Diffusion Fréchet Functions |
| Authors | Diego Hernán Díaz Martínez, Christine H. Lee, Peter T. Kim, Washington Mio |
| Abstract | Many complex ecosystems, such as those formed by multiple microbial taxa, involve intricate interactions amongst various sub-communities. The most basic relationships are frequently modeled as co-occurrence networks in which the nodes represent the various players in the community and the weighted edges encode levels of interaction. In this setting, the composition of a community may be viewed as a probability distribution on the nodes of the network. This paper develops methods for modeling the organization of such data, as well as their Euclidean counterparts, across spatial scales. Using the notion of diffusion distance, we introduce diffusion Frechet functions and diffusion Frechet vectors associated with probability distributions on Euclidean space and the vertex set of a weighted network, respectively. We prove that these functional statistics are stable with respect to the Wasserstein distance between probability measures, thus yielding robust descriptors of their shapes. We apply the methodology to investigate bacterial communities in the human gut, seeking to characterize divergence from intestinal homeostasis in patients with Clostridium difficile infection (CDI) and the effects of fecal microbiota transplantation, a treatment used in CDI patients that has proven to be significantly more effective than traditional treatment with antibiotics. The proposed method proves useful in deriving a biomarker that might help elucidate the mechanisms that drive these processes. |
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| Published | 2016-05-16 |
| URL | http://arxiv.org/abs/1605.04955v2 |
| http://arxiv.org/pdf/1605.04955v2.pdf | |
| PWC | https://paperswithcode.com/paper/probing-the-geometry-of-data-with-diffusion |
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Energy Disaggregation for Real-Time Building Flexibility Detection
| Title | Energy Disaggregation for Real-Time Building Flexibility Detection |
| Authors | Elena Mocanu, Phuong H. Nguyen, Madeleine Gibescu |
| Abstract | Energy is a limited resource which has to be managed wisely, taking into account both supply-demand matching and capacity constraints in the distribution grid. One aspect of the smart energy management at the building level is given by the problem of real-time detection of flexible demand available. In this paper we propose the use of energy disaggregation techniques to perform this task. Firstly, we investigate the use of existing classification methods to perform energy disaggregation. A comparison is performed between four classifiers, namely Naive Bayes, k-Nearest Neighbors, Support Vector Machine and AdaBoost. Secondly, we propose the use of Restricted Boltzmann Machine to automatically perform feature extraction. The extracted features are then used as inputs to the four classifiers and consequently shown to improve their accuracy. The efficiency of our approach is demonstrated on a real database consisting of detailed appliance-level measurements with high temporal resolution, which has been used for energy disaggregation in previous studies, namely the REDD. The results show robustness and good generalization capabilities to newly presented buildings with at least 96% accuracy. |
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| Published | 2016-05-06 |
| URL | http://arxiv.org/abs/1605.01939v1 |
| http://arxiv.org/pdf/1605.01939v1.pdf | |
| PWC | https://paperswithcode.com/paper/energy-disaggregation-for-real-time-building |
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Adaptive regularization for Lasso models in the context of non-stationary data streams
| Title | Adaptive regularization for Lasso models in the context of non-stationary data streams |
| Authors | Ricardo Pio Monti, Christoforos Anagnostopoulos, Giovanni Montana |
| Abstract | Large scale, streaming datasets are ubiquitous in modern machine learning. Streaming algorithms must be scalable, amenable to incremental training and robust to the presence of non-stationarity. In this work consider the problem of learning $\ell_1$ regularized linear models in the context of streaming data. In particular, the focus of this work revolves around how to select the regularization parameter when data arrives sequentially and the underlying distribution is non-stationary (implying the choice of optimal regularization parameter is itself time-varying). We propose a framework through which to infer an adaptive regularization parameter. Our approach employs an $\ell_1$ penalty constraint where the corresponding sparsity parameter is iteratively updated via stochastic gradient descent. This serves to reformulate the choice of regularization parameter in a principled framework for online learning. The proposed method is derived for linear regression and subsequently extended to generalized linear models. We validate our approach using simulated and real datasets and present an application to a neuroimaging dataset. |
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| Published | 2016-10-28 |
| URL | http://arxiv.org/abs/1610.09127v2 |
| http://arxiv.org/pdf/1610.09127v2.pdf | |
| PWC | https://paperswithcode.com/paper/adaptive-regularization-for-lasso-models-in |
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A Survey of Qualitative Spatial and Temporal Calculi – Algebraic and Computational Properties
| Title | A Survey of Qualitative Spatial and Temporal Calculi – Algebraic and Computational Properties |
| Authors | Frank Dylla, Jae Hee Lee, Till Mossakowski, Thomas Schneider, André Van Delden, Jasper Van De Ven, Diedrich Wolter |
| Abstract | Qualitative Spatial and Temporal Reasoning (QSTR) is concerned with symbolic knowledge representation, typically over infinite domains. The motivations for employing QSTR techniques range from exploiting computational properties that allow efficient reasoning to capture human cognitive concepts in a computational framework. The notion of a qualitative calculus is one of the most prominent QSTR formalisms. This article presents the first overview of all qualitative calculi developed to date and their computational properties, together with generalized definitions of the fundamental concepts and methods, which now encompass all existing calculi. Moreover, we provide a classification of calculi according to their algebraic properties. |
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| Published | 2016-06-01 |
| URL | http://arxiv.org/abs/1606.00133v1 |
| http://arxiv.org/pdf/1606.00133v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-survey-of-qualitative-spatial-and-temporal |
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Convergence of a Grassmannian Gradient Descent Algorithm for Subspace Estimation From Undersampled Data
| Title | Convergence of a Grassmannian Gradient Descent Algorithm for Subspace Estimation From Undersampled Data |
| Authors | Dejiao Zhang, Laura Balzano |
| Abstract | Subspace learning and matrix factorization problems have great many applications in science and engineering, and efficient algorithms are critical as dataset sizes continue to grow. Many relevant problem formulations are non-convex, and in a variety of contexts it has been observed that solving the non-convex problem directly is not only efficient but reliably accurate. We discuss convergence theory for a particular method: first order incremental gradient descent constrained to the Grassmannian. The output of the algorithm is an orthonormal basis for a $d$-dimensional subspace spanned by an input streaming data matrix. We study two sampling cases: where each data vector of the streaming matrix is fully sampled, or where it is undersampled by a sampling matrix $A_t\in \mathbb{R}^{m\times n}$ with $m\ll n$. Our results cover two cases, where $A_t$ is Gaussian or a subset of rows of the identity matrix. We propose an adaptive stepsize scheme that depends only on the sampled data and algorithm outputs. We prove that with fully sampled data, the stepsize scheme maximizes the improvement of our convergence metric at each iteration, and this method converges from any random initialization to the true subspace, despite the non-convex formulation and orthogonality constraints. For the case of undersampled data, we establish monotonic expected improvement on the defined convergence metric for each iteration with high probability. |
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| Published | 2016-10-01 |
| URL | http://arxiv.org/abs/1610.00199v2 |
| http://arxiv.org/pdf/1610.00199v2.pdf | |
| PWC | https://paperswithcode.com/paper/convergence-of-a-grassmannian-gradient |
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Compressive PCA for Low-Rank Matrices on Graphs
| Title | Compressive PCA for Low-Rank Matrices on Graphs |
| Authors | Nauman Shahid, Nathanael Perraudin, Gilles Puy, Pierre Vandergheynst |
| Abstract | We introduce a novel framework for an approxi- mate recovery of data matrices which are low-rank on graphs, from sampled measurements. The rows and columns of such matrices belong to the span of the first few eigenvectors of the graphs constructed between their rows and columns. We leverage this property to recover the non-linear low-rank structures efficiently from sampled data measurements, with a low cost (linear in n). First, a Resrtricted Isometry Property (RIP) condition is introduced for efficient uniform sampling of the rows and columns of such matrices based on the cumulative coherence of graph eigenvectors. Secondly, a state-of-the-art fast low-rank recovery method is suggested for the sampled data. Finally, several efficient, parallel and parameter-free decoders are presented along with their theoretical analysis for decoding the low-rank and cluster indicators for the full data matrix. Thus, we overcome the computational limitations of the standard linear low-rank recovery methods for big datasets. Our method can also be seen as a major step towards efficient recovery of non- linear low-rank structures. For a matrix of size n X p, on a single core machine, our method gains a speed up of $p^2/k$ over Robust Principal Component Analysis (RPCA), where k « p is the subspace dimension. Numerically, we can recover a low-rank matrix of size 10304 X 1000, 100 times faster than Robust PCA. |
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| Published | 2016-02-05 |
| URL | http://arxiv.org/abs/1602.02070v4 |
| http://arxiv.org/pdf/1602.02070v4.pdf | |
| PWC | https://paperswithcode.com/paper/compressive-pca-for-low-rank-matrices-on |
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Inpainting of long audio segments with similarity graphs
| Title | Inpainting of long audio segments with similarity graphs |
| Authors | Nathanael Perraudin, Nicki Holighaus, Piotr Majdak, Peter Balazs |
| Abstract | We present a novel method for the compensation of long duration data loss in audio signals, in particular music. The concealment of such signal defects is based on a graph that encodes signal structure in terms of time-persistent spectral similarity. A suitable candidate segment for the substitution of the lost content is proposed by an intuitive optimization scheme and smoothly inserted into the gap, i.e. the lost or distorted signal region. Extensive listening tests show that the proposed algorithm provides highly promising results when applied to a variety of real-world music signals. |
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| Published | 2016-07-22 |
| URL | http://arxiv.org/abs/1607.06667v4 |
| http://arxiv.org/pdf/1607.06667v4.pdf | |
| PWC | https://paperswithcode.com/paper/inpainting-of-long-audio-segments-with |
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Linking the Neural Machine Translation and the Prediction of Organic Chemistry Reactions
| Title | Linking the Neural Machine Translation and the Prediction of Organic Chemistry Reactions |
| Authors | Juno Nam, Jurae Kim |
| Abstract | Finding the main product of a chemical reaction is one of the important problems of organic chemistry. This paper describes a method of applying a neural machine translation model to the prediction of organic chemical reactions. In order to translate ‘reactants and reagents’ to ‘products’, a gated recurrent unit based sequence-to-sequence model and a parser to generate input tokens for model from reaction SMILES strings were built. Training sets are composed of reactions from the patent databases, and reactions manually generated applying the elementary reactions in an organic chemistry textbook of Wade. The trained models were tested by examples and problems in the textbook. The prediction process does not need manual encoding of rules (e.g., SMARTS transformations) to predict products, hence it only needs sufficient training reaction sets to learn new types of reactions. |
| Tasks | Machine Translation |
| Published | 2016-12-29 |
| URL | http://arxiv.org/abs/1612.09529v1 |
| http://arxiv.org/pdf/1612.09529v1.pdf | |
| PWC | https://paperswithcode.com/paper/linking-the-neural-machine-translation-and |
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Fast Non-Parametric Tests of Relative Dependency and Similarity
| Title | Fast Non-Parametric Tests of Relative Dependency and Similarity |
| Authors | Wacha Bounliphone, Eugene Belilovsky, Arthur Tenenhaus, Ioannis Antonoglou, Arthur Gretton, Matthew B. Blashcko |
| Abstract | We introduce two novel non-parametric statistical hypothesis tests. The first test, called the relative test of dependency, enables us to determine whether one source variable is significantly more dependent on a first target variable or a second. Dependence is measured via the Hilbert-Schmidt Independence Criterion (HSIC). The second test, called the relative test of similarity, is use to determine which of the two samples from arbitrary distributions is significantly closer to a reference sample of interest and the relative measure of similarity is based on the Maximum Mean Discrepancy (MMD). To construct these tests, we have used as our test statistics the difference of HSIC statistics and of MMD statistics, respectively. The resulting tests are consistent and unbiased, and have favorable convergence properties. The effectiveness of the relative dependency test is demonstrated on several real-world problems: we identify languages groups from a multilingual parallel corpus, and we show that tumor location is more dependent on gene expression than chromosome imbalance. We also demonstrate the performance of the relative test of similarity over a broad selection of model comparisons problems in deep generative models. |
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| Published | 2016-11-17 |
| URL | http://arxiv.org/abs/1611.05740v1 |
| http://arxiv.org/pdf/1611.05740v1.pdf | |
| PWC | https://paperswithcode.com/paper/fast-non-parametric-tests-of-relative |
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A matter of words: NLP for quality evaluation of Wikipedia medical articles
| Title | A matter of words: NLP for quality evaluation of Wikipedia medical articles |
| Authors | Vittoria Cozza, Marinella Petrocchi, Angelo Spognardi |
| Abstract | Automatic quality evaluation of Web information is a task with many fields of applications and of great relevance, especially in critical domains like the medical one. We move from the intuition that the quality of content of medical Web documents is affected by features related with the specific domain. First, the usage of a specific vocabulary (Domain Informativeness); then, the adoption of specific codes (like those used in the infoboxes of Wikipedia articles) and the type of document (e.g., historical and technical ones). In this paper, we propose to leverage specific domain features to improve the results of the evaluation of Wikipedia medical articles. In particular, we evaluate the articles adopting an “actionable” model, whose features are related to the content of the articles, so that the model can also directly suggest strategies for improving a given article quality. We rely on Natural Language Processing (NLP) and dictionaries-based techniques in order to extract the bio-medical concepts in a text. We prove the effectiveness of our approach by classifying the medical articles of the Wikipedia Medicine Portal, which have been previously manually labeled by the Wiki Project team. The results of our experiments confirm that, by considering domain-oriented features, it is possible to obtain sensible improvements with respect to existing solutions, mainly for those articles that other approaches have less correctly classified. Other than being interesting by their own, the results call for further research in the area of domain specific features suitable for Web data quality assessment. |
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| Published | 2016-03-07 |
| URL | http://arxiv.org/abs/1603.01987v1 |
| http://arxiv.org/pdf/1603.01987v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-matter-of-words-nlp-for-quality-evaluation |
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Mesh Interest Point Detection Based on Geometric Measures and Sparse Refinement
| Title | Mesh Interest Point Detection Based on Geometric Measures and Sparse Refinement |
| Authors | Xinyu Lin, Ce Zhu, Yipeng Liu |
| Abstract | Three dimensional (3D) interest point detection plays a fundamental role in 3D computer vision and graphics. In this paper, we introduce a new method for detecting mesh interest points based on geometric measures and sparse refinement (GMSR). The key point of our approach is to calculate the 3D interest point response function using two intuitive and effective geometric properties of the local surface on a 3D mesh model, namely Euclidean distances between the neighborhood vertices to the tangent plane of a vertex and the angles of normal vectors of them. The response function is defined in multi-scale space and can be utilized to effectively distinguish 3D interest points from edges and flat areas. Those points with local maximal 3D interest point response value are selected as the candidates of 3D interest points. Finally, we utilize an $\ell_0$ norm based optimization method to refine the candidates of 3D interest points by constraining its quality and quantity. Numerical experiments demonstrate that our proposed GMSR based 3D interest point detector outperforms current several state-of-the-art methods for different kinds of 3D mesh models. |
| Tasks | Interest Point Detection |
| Published | 2016-04-29 |
| URL | http://arxiv.org/abs/1604.08806v3 |
| http://arxiv.org/pdf/1604.08806v3.pdf | |
| PWC | https://paperswithcode.com/paper/mesh-interest-point-detection-based-on |
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3D Face Tracking and Texture Fusion in the Wild
| Title | 3D Face Tracking and Texture Fusion in the Wild |
| Authors | Patrik Huber, Philipp Kopp, Matthias Rätsch, William Christmas, Josef Kittler |
| Abstract | We present a fully automatic approach to real-time 3D face reconstruction from monocular in-the-wild videos. With the use of a cascaded-regressor based face tracking and a 3D Morphable Face Model shape fitting, we obtain a semi-dense 3D face shape. We further use the texture information from multiple frames to build a holistic 3D face representation from the video frames. Our system is able to capture facial expressions and does not require any person-specific training. We demonstrate the robustness of our approach on the challenging 300 Videos in the Wild (300-VW) dataset. Our real-time fitting framework is available as an open source library at http://4dface.org. |
| Tasks | 3D Face Reconstruction, Face Reconstruction |
| Published | 2016-05-22 |
| URL | http://arxiv.org/abs/1605.06764v1 |
| http://arxiv.org/pdf/1605.06764v1.pdf | |
| PWC | https://paperswithcode.com/paper/3d-face-tracking-and-texture-fusion-in-the |
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Latent Bi-constraint SVM for Video-based Object Recognition
| Title | Latent Bi-constraint SVM for Video-based Object Recognition |
| Authors | Yang Liu, Minh Hoai, Mang Shao, Tae-Kyun Kim |
| Abstract | We address the task of recognizing objects from video input. This important problem is relatively unexplored, compared with image-based object recognition. To this end, we make the following contributions. First, we introduce two comprehensive datasets for video-based object recognition. Second, we propose Latent Bi-constraint SVM (LBSVM), a maximum-margin framework for video-based object recognition. LBSVM is based on Structured-Output SVM, but extends it to handle noisy video data and ensure consistency of the output decision throughout time. We apply LBSVM to recognize office objects and museum sculptures, and we demonstrate its benefits over image-based, set-based, and other video-based object recognition. |
| Tasks | Object Recognition |
| Published | 2016-05-31 |
| URL | http://arxiv.org/abs/1605.09452v1 |
| http://arxiv.org/pdf/1605.09452v1.pdf | |
| PWC | https://paperswithcode.com/paper/latent-bi-constraint-svm-for-video-based |
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IISCNLP at SemEval-2016 Task 2: Interpretable STS with ILP based Multiple Chunk Aligner
| Title | IISCNLP at SemEval-2016 Task 2: Interpretable STS with ILP based Multiple Chunk Aligner |
| Authors | Lavanya Sita Tekumalla, Sharmistha |
| Abstract | Interpretable semantic textual similarity (iSTS) task adds a crucial explanatory layer to pairwise sentence similarity. We address various components of this task: chunk level semantic alignment along with assignment of similarity type and score for aligned chunks with a novel system presented in this paper. We propose an algorithm, iMATCH, for the alignment of multiple non-contiguous chunks based on Integer Linear Programming (ILP). Similarity type and score assignment for pairs of chunks is done using a supervised multiclass classification technique based on Random Forrest Classifier. Results show that our algorithm iMATCH has low execution time and outperforms most other participating systems in terms of alignment score. Of the three datasets, we are top ranked for answer- students dataset in terms of overall score and have top alignment score for headlines dataset in the gold chunks track. |
| Tasks | Semantic Textual Similarity |
| Published | 2016-05-04 |
| URL | http://arxiv.org/abs/1605.01194v1 |
| http://arxiv.org/pdf/1605.01194v1.pdf | |
| PWC | https://paperswithcode.com/paper/iiscnlp-at-semeval-2016-task-2-interpretable-1 |
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