Paper Group ANR 725
Embedding Tarskian Semantics in Vector Spaces. The variational Laplace approach to approximate Bayesian inference. Light Field Blind Motion Deblurring. Fixed effects testing in high-dimensional linear mixed models. Discrete Gyrator Transforms: Computational Algorithms and Applications. Political Homophily in Independence Movements: Analysing and Cl …
Embedding Tarskian Semantics in Vector Spaces
| Title | Embedding Tarskian Semantics in Vector Spaces |
| Authors | Taisuke Sato |
| Abstract | We propose a new linear algebraic approach to the computation of Tarskian semantics in logic. We embed a finite model M in first-order logic with N entities in N-dimensional Euclidean space R^N by mapping entities of M to N dimensional one-hot vectors and k-ary relations to order-k adjacency tensors (multi-way arrays). Second given a logical formula F in prenex normal form, we compile F into a set Sigma_F of algebraic formulas in multi-linear algebra with a nonlinear operation. In this compilation, existential quantifiers are compiled into a specific type of tensors, e.g., identity matrices in the case of quantifying two occurrences of a variable. It is shown that a systematic evaluation of Sigma_F in R^N gives the truth value, 1(true) or 0(false), of F in M. Based on this framework, we also propose an unprecedented way of computing the least models defined by Datalog programs in linear spaces via matrix equations and empirically show its effectiveness compared to state-of-the-art approaches. |
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| Published | 2017-03-09 |
| URL | http://arxiv.org/abs/1703.03193v1 |
| http://arxiv.org/pdf/1703.03193v1.pdf | |
| PWC | https://paperswithcode.com/paper/embedding-tarskian-semantics-in-vector-spaces |
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The variational Laplace approach to approximate Bayesian inference
| Title | The variational Laplace approach to approximate Bayesian inference |
| Authors | Jean Daunizeau |
| Abstract | Variational approaches to approximate Bayesian inference provide very efficient means of performing parameter estimation and model selection. Among these, so-called variational-Laplace or VL schemes rely on Gaussian approximations to posterior densities on model parameters. In this note, we review the main variants of VL approaches, that follow from considering nonlinear models of continuous and/or categorical data. En passant, we also derive a few novel theoretical results that complete the portfolio of existing analyses of variational Bayesian approaches, including investigations of their asymptotic convergence. We also suggest practical ways of extending existing VL approaches to hierarchical generative models that include (e.g., precision) hyperparameters. |
| Tasks | Bayesian Inference, Model Selection |
| Published | 2017-03-02 |
| URL | http://arxiv.org/abs/1703.02089v2 |
| http://arxiv.org/pdf/1703.02089v2.pdf | |
| PWC | https://paperswithcode.com/paper/the-variational-laplace-approach-to |
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Light Field Blind Motion Deblurring
| Title | Light Field Blind Motion Deblurring |
| Authors | Pratul P. Srinivasan, Ren Ng, Ravi Ramamoorthi |
| Abstract | We study the problem of deblurring light fields of general 3D scenes captured under 3D camera motion and present both theoretical and practical contributions. By analyzing the motion-blurred light field in the primal and Fourier domains, we develop intuition into the effects of camera motion on the light field, show the advantages of capturing a 4D light field instead of a conventional 2D image for motion deblurring, and derive simple methods of motion deblurring in certain cases. We then present an algorithm to blindly deblur light fields of general scenes without any estimation of scene geometry, and demonstrate that we can recover both the sharp light field and the 3D camera motion path of real and synthetically-blurred light fields. |
| Tasks | Deblurring |
| Published | 2017-04-18 |
| URL | http://arxiv.org/abs/1704.05416v1 |
| http://arxiv.org/pdf/1704.05416v1.pdf | |
| PWC | https://paperswithcode.com/paper/light-field-blind-motion-deblurring |
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Fixed effects testing in high-dimensional linear mixed models
| Title | Fixed effects testing in high-dimensional linear mixed models |
| Authors | Jelena Bradic, Gerda Claeskens, Thomas Gueuning |
| Abstract | Many scientific and engineering challenges – ranging from pharmacokinetic drug dosage allocation and personalized medicine to marketing mix (4Ps) recommendations – require an understanding of the unobserved heterogeneity in order to develop the best decision making-processes. In this paper, we develop a hypothesis test and the corresponding p-value for testing for the significance of the homogeneous structure in linear mixed models. A robust matching moment construction is used for creating a test that adapts to the size of the model sparsity. When unobserved heterogeneity at a cluster level is constant, we show that our test is both consistent and unbiased even when the dimension of the model is extremely high. Our theoretical results rely on a new family of adaptive sparse estimators of the fixed effects that do not require consistent estimation of the random effects. Moreover, our inference results do not require consistent model selection. We showcase that moment matching can be extended to nonlinear mixed effects models and to generalized linear mixed effects models. In numerical and real data experiments, we find that the developed method is extremely accurate, that it adapts to the size of the underlying model and is decidedly powerful in the presence of irrelevant covariates. |
| Tasks | Decision Making, Model Selection |
| Published | 2017-08-14 |
| URL | http://arxiv.org/abs/1708.04887v1 |
| http://arxiv.org/pdf/1708.04887v1.pdf | |
| PWC | https://paperswithcode.com/paper/fixed-effects-testing-in-high-dimensional |
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Discrete Gyrator Transforms: Computational Algorithms and Applications
| Title | Discrete Gyrator Transforms: Computational Algorithms and Applications |
| Authors | Soo-Chang Pei, Shih-Gu Huang, Jian-Jiun Ding |
| Abstract | As an extension of the 2D fractional Fourier transform (FRFT) and a special case of the 2D linear canonical transform (LCT), the gyrator transform was introduced to produce rotations in twisted space/spatial-frequency planes. It is a useful tool in optics, signal processing and image processing. In this paper, we develop discrete gyrator transforms (DGTs) based on the 2D LCT. Taking the advantage of the additivity property of the 2D LCT, we propose three kinds of DGTs, each of which is a cascade of low-complexity operators. These DGTs have different constraints, characteristics, and properties, and are realized by different computational algorithms. Besides, we propose a kind of DGT based on the eigenfunctions of the gyrator transform. This DGT is an orthonormal transform, and thus its comprehensive properties, especially the additivity property, make it more useful in many applications. We also develop an efficient computational algorithm to significantly reduce the complexity of this DGT. At the end, a brief review of some important applications of the DGTs is presented, including mode conversion, sampling and reconstruction, watermarking, and image encryption. |
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| Published | 2017-06-03 |
| URL | http://arxiv.org/abs/1707.03689v1 |
| http://arxiv.org/pdf/1707.03689v1.pdf | |
| PWC | https://paperswithcode.com/paper/discrete-gyrator-transforms-computational |
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Political Homophily in Independence Movements: Analysing and Classifying Social Media Users by National Identity
| Title | Political Homophily in Independence Movements: Analysing and Classifying Social Media Users by National Identity |
| Authors | Arkaitz Zubiaga, Bo Wang, Maria Liakata, Rob Procter |
| Abstract | Social media and data mining are increasingly being used to analyse political and societal issues. Here we undertake the classification of social media users as supporting or opposing ongoing independence movements in their territories. Independence movements occur in territories whose citizens have conflicting national identities; users with opposing national identities will then support or oppose the sense of being part of an independent nation that differs from the officially recognised country. We describe a methodology that relies on users’ self-reported location to build large-scale datasets for three territories – Catalonia, the Basque Country and Scotland. An analysis of these datasets shows that homophily plays an important role in determining who people connect with, as users predominantly choose to follow and interact with others from the same national identity. We show that a classifier relying on users’ follow networks can achieve accurate, language-independent classification performances ranging from 85% to 97% for the three territories. |
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| Published | 2017-02-27 |
| URL | http://arxiv.org/abs/1702.08388v3 |
| http://arxiv.org/pdf/1702.08388v3.pdf | |
| PWC | https://paperswithcode.com/paper/political-homophily-in-independence-movements |
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Machine Translation at Booking.com: Journey and Lessons Learned
| Title | Machine Translation at Booking.com: Journey and Lessons Learned |
| Authors | Pavel Levin, Nishikant Dhanuka, Maxim Khalilov |
| Abstract | We describe our recently developed neural machine translation (NMT) system and benchmark it against our own statistical machine translation (SMT) system as well as two other general purpose online engines (statistical and neural). We present automatic and human evaluation results of the translation output provided by each system. We also analyze the effect of sentence length on the quality of output for SMT and NMT systems. |
| Tasks | Machine Translation |
| Published | 2017-07-25 |
| URL | http://arxiv.org/abs/1707.07911v1 |
| http://arxiv.org/pdf/1707.07911v1.pdf | |
| PWC | https://paperswithcode.com/paper/machine-translation-at-bookingcom-journey-and |
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How much is my car worth? A methodology for predicting used cars prices using Random Forest
| Title | How much is my car worth? A methodology for predicting used cars prices using Random Forest |
| Authors | Nabarun Pal, Priya Arora, Dhanasekar Sundararaman, Puneet Kohli, Sai Sumanth Palakurthy |
| Abstract | Cars are being sold more than ever. Developing countries adopt the lease culture instead of buying a new car due to affordability. Therefore, the rise of used cars sales is exponentially increasing. Car sellers sometimes take advantage of this scenario by listing unrealistic prices owing to the demand. Therefore, arises a need for a model that can assign a price for a vehicle by evaluating its features taking the prices of other cars into consideration. In this paper, we use supervised learning method namely Random Forest to predict the prices of used cars. The model has been chosen after careful exploratory data analysis to determine the impact of each feature on price. A Random Forest with 500 Decision Trees were created to train the data. From experimental results, the training accuracy was found out to be 95.82%, and the testing accuracy was 83.63%. The the model can predict the price of cars accurately by choosing the most correlated features. |
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| Published | 2017-11-19 |
| URL | http://arxiv.org/abs/1711.06970v1 |
| http://arxiv.org/pdf/1711.06970v1.pdf | |
| PWC | https://paperswithcode.com/paper/how-much-is-my-car-worth-a-methodology-for |
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Active Regression via Linear-Sample Sparsification
| Title | Active Regression via Linear-Sample Sparsification |
| Authors | Xue Chen, Eric Price |
| Abstract | We present an approach that improves the sample complexity for a variety of curve fitting problems, including active learning for linear regression, polynomial regression, and continuous sparse Fourier transforms. In the active linear regression problem, one would like to estimate the least squares solution $\beta^$ minimizing $\X\beta - y_2$ given the entire unlabeled dataset $X \in \mathbb{R}^{n \times d}$ but only observing a small number of labels $y_i$. We show that $O(d)$ labels suffice to find a constant factor approximation $\tilde{\beta}$: [ \mathbb{E}[\X\tilde{\beta} - y_2^2] \leq 2 \mathbb{E}[\X \beta^ - y_2^2]. ] This improves on the best previous result of $O(d \log d)$ from leverage score sampling. We also present results for the \emph{inductive} setting, showing when $\tilde{\beta}$ will generalize to fresh samples; these apply to continuous settings such as polynomial regression. Finally, we show how the techniques yield improved results for the non-linear sparse Fourier transform setting. |
| Tasks | Active Learning |
| Published | 2017-11-27 |
| URL | http://arxiv.org/abs/1711.10051v3 |
| http://arxiv.org/pdf/1711.10051v3.pdf | |
| PWC | https://paperswithcode.com/paper/active-regression-via-linear-sample |
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Towards well-specified semi-supervised model-based classifiers via structural adaptation
| Title | Towards well-specified semi-supervised model-based classifiers via structural adaptation |
| Authors | Zhaocai Sun, William K. Cheung, Xiaofeng Zhang, Jun Yang |
| Abstract | Semi-supervised learning plays an important role in large-scale machine learning. Properly using additional unlabeled data (largely available nowadays) often can improve the machine learning accuracy. However, if the machine learning model is misspecified for the underlying true data distribution, the model performance could be seriously jeopardized. This issue is known as model misspecification. To address this issue, we focus on generative models and propose a criterion to detect the onset of model misspecification by measuring the performance difference between models obtained using supervised and semi-supervised learning. Then, we propose to automatically modify the generative models during model training to achieve an unbiased generative model. Rigorous experiments were carried out to evaluate the proposed method using two image classification data sets PASCAL VOC’07 and MIR Flickr. Our proposed method has been demonstrated to outperform a number of state-of-the-art semi-supervised learning approaches for the classification task. |
| Tasks | Image Classification |
| Published | 2017-05-01 |
| URL | http://arxiv.org/abs/1705.00597v1 |
| http://arxiv.org/pdf/1705.00597v1.pdf | |
| PWC | https://paperswithcode.com/paper/towards-well-specified-semi-supervised-model |
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BP-STDP: Approximating Backpropagation using Spike Timing Dependent Plasticity
| Title | BP-STDP: Approximating Backpropagation using Spike Timing Dependent Plasticity |
| Authors | Amirhossein Tavanaei, Anthony S. Maida |
| Abstract | The problem of training spiking neural networks (SNNs) is a necessary precondition to understanding computations within the brain, a field still in its infancy. Previous work has shown that supervised learning in multi-layer SNNs enables bio-inspired networks to recognize patterns of stimuli through hierarchical feature acquisition. Although gradient descent has shown impressive performance in multi-layer (and deep) SNNs, it is generally not considered biologically plausible and is also computationally expensive. This paper proposes a novel supervised learning approach based on an event-based spike-timing-dependent plasticity (STDP) rule embedded in a network of integrate-and-fire (IF) neurons. The proposed temporally local learning rule follows the backpropagation weight change updates applied at each time step. This approach enjoys benefits of both accurate gradient descent and temporally local, efficient STDP. Thus, this method is able to address some open questions regarding accurate and efficient computations that occur in the brain. The experimental results on the XOR problem, the Iris data, and the MNIST dataset demonstrate that the proposed SNN performs as successfully as the traditional NNs. Our approach also compares favorably with the state-of-the-art multi-layer SNNs. |
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| Published | 2017-11-12 |
| URL | http://arxiv.org/abs/1711.04214v2 |
| http://arxiv.org/pdf/1711.04214v2.pdf | |
| PWC | https://paperswithcode.com/paper/bp-stdp-approximating-backpropagation-using |
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Fast Stochastic Variance Reduced ADMM for Stochastic Composition Optimization
| Title | Fast Stochastic Variance Reduced ADMM for Stochastic Composition Optimization |
| Authors | Yue Yu, Longbo Huang |
| Abstract | We consider the stochastic composition optimization problem proposed in \cite{wang2017stochastic}, which has applications ranging from estimation to statistical and machine learning. We propose the first ADMM-based algorithm named com-SVR-ADMM, and show that com-SVR-ADMM converges linearly for strongly convex and Lipschitz smooth objectives, and has a convergence rate of $O( \log S/S)$, which improves upon the $O(S^{-4/9})$ rate in \cite{wang2016accelerating} when the objective is convex and Lipschitz smooth. Moreover, com-SVR-ADMM possesses a rate of $O(1/\sqrt{S})$ when the objective is convex but without Lipschitz smoothness. We also conduct experiments and show that it outperforms existing algorithms. |
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| Published | 2017-05-11 |
| URL | http://arxiv.org/abs/1705.04138v2 |
| http://arxiv.org/pdf/1705.04138v2.pdf | |
| PWC | https://paperswithcode.com/paper/fast-stochastic-variance-reduced-admm-for |
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Causality and Temporal Dependencies in the Design of Fault Management Systems
| Title | Causality and Temporal Dependencies in the Design of Fault Management Systems |
| Authors | Marco Bozzano |
| Abstract | Reasoning about causes and effects naturally arises in the engineering of safety-critical systems. A classical example is Fault Tree Analysis, a deductive technique used for system safety assessment, whereby an undesired state is reduced to the set of its immediate causes. The design of fault management systems also requires reasoning on causality relationships. In particular, a fail-operational system needs to ensure timely detection and identification of faults, i.e. recognize the occurrence of run-time faults through their observable effects on the system. Even more complex scenarios arise when multiple faults are involved and may interact in subtle ways. In this work, we propose a formal approach to fault management for complex systems. We first introduce the notions of fault tree and minimal cut sets. We then present a formal framework for the specification and analysis of diagnosability, and for the design of fault detection and identification (FDI) components. Finally, we review recent advances in fault propagation analysis, based on the Timed Failure Propagation Graphs (TFPG) formalism. |
| Tasks | Fault Detection |
| Published | 2017-10-10 |
| URL | http://arxiv.org/abs/1710.03392v1 |
| http://arxiv.org/pdf/1710.03392v1.pdf | |
| PWC | https://paperswithcode.com/paper/causality-and-temporal-dependencies-in-the |
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Probabilistic Typology: Deep Generative Models of Vowel Inventories
| Title | Probabilistic Typology: Deep Generative Models of Vowel Inventories |
| Authors | Ryan Cotterell, Jason Eisner |
| Abstract | Linguistic typology studies the range of structures present in human language. The main goal of the field is to discover which sets of possible phenomena are universal, and which are merely frequent. For example, all languages have vowels, while most—but not all—languages have an /u/ sound. In this paper we present the first probabilistic treatment of a basic question in phonological typology: What makes a natural vowel inventory? We introduce a series of deep stochastic point processes, and contrast them with previous computational, simulation-based approaches. We provide a comprehensive suite of experiments on over 200 distinct languages. |
| Tasks | Point Processes |
| Published | 2017-05-04 |
| URL | http://arxiv.org/abs/1705.01684v1 |
| http://arxiv.org/pdf/1705.01684v1.pdf | |
| PWC | https://paperswithcode.com/paper/probabilistic-typology-deep-generative-models |
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Clinical Patient Tracking in the Presence of Transient and Permanent Occlusions via Geodesic Feature
| Title | Clinical Patient Tracking in the Presence of Transient and Permanent Occlusions via Geodesic Feature |
| Authors | Kun Li, Joel W. Burdick |
| Abstract | This paper develops a method to use RGB-D cameras to track the motions of a human spinal cord injury patient undergoing spinal stimulation and physical rehabilitation. Because clinicians must remain close to the patient during training sessions, the patient is usually under permanent and transient occlusions due to the training equipment and the movements of the attending clinicians. These occlusions can significantly degrade the accuracy of existing human tracking methods. To improve the data association problem in these circumstances, we present a new global feature based on the geodesic distances of surface mesh points to a set of anchor points. Transient occlusions are handled via a multi-hypothesis tracking framework. To evaluate the method, we simulated different occlusion sizes on a data set captured from a human in varying movement patterns, and compared the proposed feature with other tracking methods. The results show that the proposed method achieves robustness to both surface deformations and transient occlusions. |
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| Published | 2017-07-22 |
| URL | http://arxiv.org/abs/1707.07139v2 |
| http://arxiv.org/pdf/1707.07139v2.pdf | |
| PWC | https://paperswithcode.com/paper/clinical-patient-tracking-in-the-presence-of |
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