Paper Group ANR 661
Automatic Segmentation of Retinal Vasculature. Sketched Subspace Clustering. Multiqubit and multilevel quantum reinforcement learning with quantum technologies. Improving Factor-Based Quantitative Investing by Forecasting Company Fundamentals. Learning from Comparisons and Choices. Inclusive Flavour Tagging Algorithm. Revenue-based Attribution Mode …
Automatic Segmentation of Retinal Vasculature
| Title | Automatic Segmentation of Retinal Vasculature |
| Authors | Renoh Johnson Chalakkal, Waleed Abdulla |
| Abstract | Segmentation of retinal vessels from retinal fundus images is the key step in the automatic retinal image analysis. In this paper, we propose a new unsupervised automatic method to segment the retinal vessels from retinal fundus images. Contrast enhancement and illumination correction are carried out through a series of image processing steps followed by adaptive histogram equalization and anisotropic diffusion filtering. This image is then converted to a gray scale using weighted scaling. The vessel edges are enhanced by boosting the detail curvelet coefficients. Optic disk pixels are removed before applying fuzzy C-mean classification to avoid the misclassification. Morphological operations and connected component analysis are applied to obtain the segmented retinal vessels. The performance of the proposed method is evaluated using DRIVE database to be able to compare with other state-of-art supervised and unsupervised methods. The overall segmentation accuracy of the proposed method is 95.18% which outperforms the other algorithms. |
| Tasks | |
| Published | 2017-07-19 |
| URL | http://arxiv.org/abs/1707.06323v1 |
| http://arxiv.org/pdf/1707.06323v1.pdf | |
| PWC | https://paperswithcode.com/paper/automatic-segmentation-of-retinal-vasculature |
| Repo | |
| Framework | |
Sketched Subspace Clustering
| Title | Sketched Subspace Clustering |
| Authors | Panagiotis A. Traganitis, Georgios B. Giannakis |
| Abstract | The immense amount of daily generated and communicated data presents unique challenges in their processing. Clustering, the grouping of data without the presence of ground-truth labels, is an important tool for drawing inferences from data. Subspace clustering (SC) is a relatively recent method that is able to successfully classify nonlinearly separable data in a multitude of settings. In spite of their high clustering accuracy, SC methods incur prohibitively high computational complexity when processing large volumes of high-dimensional data. Inspired by random sketching approaches for dimensionality reduction, the present paper introduces a randomized scheme for SC, termed Sketch-SC, tailored for large volumes of high-dimensional data. Sketch-SC accelerates the computationally heavy parts of state-of-the-art SC approaches by compressing the data matrix across both dimensions using random projections, thus enabling fast and accurate large-scale SC. Performance analysis as well as extensive numerical tests on real data corroborate the potential of Sketch-SC and its competitive performance relative to state-of-the-art scalable SC approaches. |
| Tasks | Dimensionality Reduction |
| Published | 2017-07-22 |
| URL | http://arxiv.org/abs/1707.07196v2 |
| http://arxiv.org/pdf/1707.07196v2.pdf | |
| PWC | https://paperswithcode.com/paper/sketched-subspace-clustering |
| Repo | |
| Framework | |
Multiqubit and multilevel quantum reinforcement learning with quantum technologies
| Title | Multiqubit and multilevel quantum reinforcement learning with quantum technologies |
| Authors | F. A. Cárdenas-López, L. Lamata, J. C. Retamal, E. Solano |
| Abstract | We propose a protocol to perform quantum reinforcement learning with quantum technologies. At variance with recent results on quantum reinforcement learning with superconducting circuits, in our current protocol coherent feedback during the learning process is not required, enabling its implementation in a wide variety of quantum systems. We consider diverse possible scenarios for an agent, an environment, and a register that connects them, involving multiqubit and multilevel systems, as well as open-system dynamics. We finally propose possible implementations of this protocol in trapped ions and superconducting circuits. The field of quantum reinforcement learning with quantum technologies will enable enhanced quantum control, as well as more efficient machine learning calculations. |
| Tasks | |
| Published | 2017-09-22 |
| URL | http://arxiv.org/abs/1709.07848v2 |
| http://arxiv.org/pdf/1709.07848v2.pdf | |
| PWC | https://paperswithcode.com/paper/multiqubit-and-multilevel-quantum |
| Repo | |
| Framework | |
Improving Factor-Based Quantitative Investing by Forecasting Company Fundamentals
| Title | Improving Factor-Based Quantitative Investing by Forecasting Company Fundamentals |
| Authors | John Alberg, Zachary C. Lipton |
| Abstract | On a periodic basis, publicly traded companies are required to report fundamentals: financial data such as revenue, operating income, debt, among others. These data points provide some insight into the financial health of a company. Academic research has identified some factors, i.e. computed features of the reported data, that are known through retrospective analysis to outperform the market average. Two popular factors are the book value normalized by market capitalization (book-to-market) and the operating income normalized by the enterprise value (EBIT/EV). In this paper: we first show through simulation that if we could (clairvoyantly) select stocks using factors calculated on future fundamentals (via oracle), then our portfolios would far outperform a standard factor approach. Motivated by this analysis, we train deep neural networks to forecast future fundamentals based on a trailing 5-years window. Quantitative analysis demonstrates a significant improvement in MSE over a naive strategy. Moreover, in retrospective analysis using an industry-grade stock portfolio simulator (backtester), we show an improvement in compounded annual return to 17.1% (MLP) vs 14.4% for a standard factor model. |
| Tasks | |
| Published | 2017-11-13 |
| URL | http://arxiv.org/abs/1711.04837v2 |
| http://arxiv.org/pdf/1711.04837v2.pdf | |
| PWC | https://paperswithcode.com/paper/improving-factor-based-quantitative-investing |
| Repo | |
| Framework | |
Learning from Comparisons and Choices
| Title | Learning from Comparisons and Choices |
| Authors | Sahand Negahban, Sewoong Oh, Kiran K. Thekumparampil, Jiaming Xu |
| Abstract | When tracking user-specific online activities, each user’s preference is revealed in the form of choices and comparisons. For example, a user’s purchase history is a record of her choices, i.e. which item was chosen among a subset of offerings. A user’s preferences can be observed either explicitly as in movie ratings or implicitly as in viewing times of news articles. Given such individualized ordinal data in the form of comparisons and choices, we address the problem of collaboratively learning representations of the users and the items. The learned features can be used to predict a user’s preference of an unseen item to be used in recommendation systems. This also allows one to compute similarities among users and items to be used for categorization and search. Motivated by the empirical successes of the MultiNomial Logit (MNL) model in marketing and transportation, and also more recent successes in word embedding and crowdsourced image embedding, we pose this problem as learning the MNL model parameters that best explain the data. We propose a convex relaxation for learning the MNL model, and show that it is minimax optimal up to a logarithmic factor by comparing its performance to a fundamental lower bound. This characterizes the minimax sample complexity of the problem, and proves that the proposed estimator cannot be improved upon other than by a logarithmic factor. Further, the analysis identifies how the accuracy depends on the topology of sampling via the spectrum of the sampling graph. This provides a guideline for designing surveys when one can choose which items are to be compared. This is accompanied by numerical simulations on synthetic and real data sets, confirming our theoretical predictions. |
| Tasks | Recommendation Systems |
| Published | 2017-04-24 |
| URL | http://arxiv.org/abs/1704.07228v2 |
| http://arxiv.org/pdf/1704.07228v2.pdf | |
| PWC | https://paperswithcode.com/paper/learning-from-comparisons-and-choices |
| Repo | |
| Framework | |
Inclusive Flavour Tagging Algorithm
| Title | Inclusive Flavour Tagging Algorithm |
| Authors | Tatiana Likhomanenko, Denis Derkach, Alex Rogozhnikov |
| Abstract | Identifying the flavour of neutral $B$ mesons production is one of the most important components needed in the study of time-dependent $CP$ violation. The harsh environment of the Large Hadron Collider makes it particularly hard to succeed in this task. We present an inclusive flavour-tagging algorithm as an upgrade of the algorithms currently used by the LHCb experiment. Specifically, a probabilistic model which efficiently combines information from reconstructed vertices and tracks using machine learning is proposed. The algorithm does not use information about underlying physics process. It reduces the dependence on the performance of lower level identification capacities and thus increases the overall performance. The proposed inclusive flavour-tagging algorithm is applicable to tag the flavour of $B$ mesons in any proton-proton experiment. |
| Tasks | |
| Published | 2017-05-24 |
| URL | http://arxiv.org/abs/1705.08707v1 |
| http://arxiv.org/pdf/1705.08707v1.pdf | |
| PWC | https://paperswithcode.com/paper/inclusive-flavour-tagging-algorithm |
| Repo | |
| Framework | |
Revenue-based Attribution Modeling for Online Advertising
| Title | Revenue-based Attribution Modeling for Online Advertising |
| Authors | Kaifeng Zhao, Seyed Hanif Mahboobi, Saeed Bagheri |
| Abstract | This paper examines and proposes several attribution modeling methods that quantify how revenue should be attributed to online advertising inputs. We adopt and further develop relative importance method, which is based on regression models that have been extensively studied and utilized to investigate the relationship between advertising efforts and market reaction (revenue). Relative importance method aims at decomposing and allocating marginal contributions to the coefficient of determination (R^2) of regression models as attribution values. In particular, we adopt two alternative submethods to perform this decomposition: dominance analysis and relative weight analysis. Moreover, we demonstrate an extension of the decomposition methods from standard linear model to additive model. We claim that our new approaches are more flexible and accurate in modeling the underlying relationship and calculating the attribution values. We use simulation examples to demonstrate the superior performance of our new approaches over traditional methods. We further illustrate the value of our proposed approaches using a real advertising campaign dataset. |
| Tasks | |
| Published | 2017-10-18 |
| URL | http://arxiv.org/abs/1710.06561v1 |
| http://arxiv.org/pdf/1710.06561v1.pdf | |
| PWC | https://paperswithcode.com/paper/revenue-based-attribution-modeling-for-online |
| Repo | |
| Framework | |
Optimal client recommendation for market makers in illiquid financial products
| Title | Optimal client recommendation for market makers in illiquid financial products |
| Authors | Dieter Hendricks, Stephen J. Roberts |
| Abstract | The process of liquidity provision in financial markets can result in prolonged exposure to illiquid instruments for market makers. In this case, where a proprietary position is not desired, pro-actively targeting the right client who is likely to be interested can be an effective means to offset this position, rather than relying on commensurate interest arising through natural demand. In this paper, we consider the inference of a client profile for the purpose of corporate bond recommendation, based on typical recorded information available to the market maker. Given a historical record of corporate bond transactions and bond meta-data, we use a topic-modelling analogy to develop a probabilistic technique for compiling a curated list of client recommendations for a particular bond that needs to be traded, ranked by probability of interest. We show that a model based on Latent Dirichlet Allocation offers promising performance to deliver relevant recommendations for sales traders. |
| Tasks | |
| Published | 2017-04-27 |
| URL | http://arxiv.org/abs/1704.08488v1 |
| http://arxiv.org/pdf/1704.08488v1.pdf | |
| PWC | https://paperswithcode.com/paper/optimal-client-recommendation-for-market |
| Repo | |
| Framework | |
Mixed Graphical Models for Causal Analysis of Multi-modal Variables
| Title | Mixed Graphical Models for Causal Analysis of Multi-modal Variables |
| Authors | Andrew J Sedgewick, Joseph D. Ramsey, Peter Spirtes, Clark Glymour, Panayiotis V. Benos |
| Abstract | Graphical causal models are an important tool for knowledge discovery because they can represent both the causal relations between variables and the multivariate probability distributions over the data. Once learned, causal graphs can be used for classification, feature selection and hypothesis generation, while revealing the underlying causal network structure and thus allowing for arbitrary likelihood queries over the data. However, current algorithms for learning sparse directed graphs are generally designed to handle only one type of data (continuous-only or discrete-only), which limits their applicability to a large class of multi-modal biological datasets that include mixed type variables. To address this issue, we developed new methods that modify and combine existing methods for finding undirected graphs with methods for finding directed graphs. These hybrid methods are not only faster, but also perform better than the directed graph estimation methods alone for a variety of parameter settings and data set sizes. Here, we describe a new conditional independence test for learning directed graphs over mixed data types and we compare performances of different graph learning strategies on synthetic data. |
| Tasks | Feature Selection |
| Published | 2017-04-09 |
| URL | http://arxiv.org/abs/1704.02621v1 |
| http://arxiv.org/pdf/1704.02621v1.pdf | |
| PWC | https://paperswithcode.com/paper/mixed-graphical-models-for-causal-analysis-of |
| Repo | |
| Framework | |
Annotation and Detection of Emotion in Text-based Dialogue Systems with CNN
| Title | Annotation and Detection of Emotion in Text-based Dialogue Systems with CNN |
| Authors | Jialiang Zhao, Qi Gao |
| Abstract | Knowledge of users’ emotion states helps improve human-computer interaction. In this work, we presented EmoNet, an emotion detector of Chinese daily dialogues based on deep convolutional neural networks. In order to maintain the original linguistic features, such as the order, commonly used methods like segmentation and keywords extraction were not adopted, instead we increased the depth of CNN and tried to let CNN learn inner linguistic relationships. Our main contribution is that we presented a new model and a new pipeline which can be used in multi-language environment to solve sentimental problems. Experimental results shows EmoNet has a great capacity in learning the emotion of dialogues and achieves a better result than other state of art detectors do. |
| Tasks | |
| Published | 2017-10-03 |
| URL | http://arxiv.org/abs/1710.00987v1 |
| http://arxiv.org/pdf/1710.00987v1.pdf | |
| PWC | https://paperswithcode.com/paper/annotation-and-detection-of-emotion-in-text |
| Repo | |
| Framework | |
Structured low-rank matrix learning: algorithms and applications
| Title | Structured low-rank matrix learning: algorithms and applications |
| Authors | Pratik Jawanpuria, Bamdev Mishra |
| Abstract | We consider the problem of learning a low-rank matrix, constrained to lie in a linear subspace, and introduce a novel factorization for modeling such matrices. A salient feature of the proposed factorization scheme is it decouples the low-rank and the structural constraints onto separate factors. We formulate the optimization problem on the Riemannian spectrahedron manifold, where the Riemannian framework allows to develop computationally efficient conjugate gradient and trust-region algorithms. Experiments on problems such as standard/robust/non-negative matrix completion, Hankel matrix learning and multi-task learning demonstrate the efficacy of our approach. A shorter version of this work has been published in ICML’18. |
| Tasks | Matrix Completion, Multi-Task Learning |
| Published | 2017-04-24 |
| URL | http://arxiv.org/abs/1704.07352v5 |
| http://arxiv.org/pdf/1704.07352v5.pdf | |
| PWC | https://paperswithcode.com/paper/structured-low-rank-matrix-learning |
| Repo | |
| Framework | |
Variational Bayesian Inference For A Scale Mixture Of Normal Distributions Handling Missing Data
| Title | Variational Bayesian Inference For A Scale Mixture Of Normal Distributions Handling Missing Data |
| Authors | G. Revillon, A. Djafari, C. Enderli |
| Abstract | In this paper, a scale mixture of Normal distributions model is developed for classification and clustering of data having outliers and missing values. The classification method, based on a mixture model, focuses on the introduction of latent variables that gives us the possibility to handle sensitivity of model to outliers and to allow a less restrictive modelling of missing data. Inference is processed through a Variational Bayesian Approximation and a Bayesian treatment is adopted for model learning, supervised classification and clustering. |
| Tasks | Bayesian Inference |
| Published | 2017-11-22 |
| URL | http://arxiv.org/abs/1711.08374v1 |
| http://arxiv.org/pdf/1711.08374v1.pdf | |
| PWC | https://paperswithcode.com/paper/variational-bayesian-inference-for-a-scale |
| Repo | |
| Framework | |
Invariances and Data Augmentation for Supervised Music Transcription
| Title | Invariances and Data Augmentation for Supervised Music Transcription |
| Authors | John Thickstun, Zaid Harchaoui, Dean Foster, Sham M. Kakade |
| Abstract | This paper explores a variety of models for frame-based music transcription, with an emphasis on the methods needed to reach state-of-the-art on human recordings. The translation-invariant network discussed in this paper, which combines a traditional filterbank with a convolutional neural network, was the top-performing model in the 2017 MIREX Multiple Fundamental Frequency Estimation evaluation. This class of models shares parameters in the log-frequency domain, which exploits the frequency invariance of music to reduce the number of model parameters and avoid overfitting to the training data. All models in this paper were trained with supervision by labeled data from the MusicNet dataset, augmented by random label-preserving pitch-shift transformations. |
| Tasks | Data Augmentation |
| Published | 2017-11-13 |
| URL | http://arxiv.org/abs/1711.04845v1 |
| http://arxiv.org/pdf/1711.04845v1.pdf | |
| PWC | https://paperswithcode.com/paper/invariances-and-data-augmentation-for |
| Repo | |
| Framework | |
Lazy stochastic principal component analysis
| Title | Lazy stochastic principal component analysis |
| Authors | Michael Wojnowicz, Dinh Nguyen, Li Li, Xuan Zhao |
| Abstract | Stochastic principal component analysis (SPCA) has become a popular dimensionality reduction strategy for large, high-dimensional datasets. We derive a simplified algorithm, called Lazy SPCA, which has reduced computational complexity and is better suited for large-scale distributed computation. We prove that SPCA and Lazy SPCA find the same approximations to the principal subspace, and that the pairwise distances between samples in the lower-dimensional space is invariant to whether SPCA is executed lazily or not. Empirical studies find downstream predictive performance to be identical for both methods, and superior to random projections, across a range of predictive models (linear regression, logistic lasso, and random forests). In our largest experiment with 4.6 million samples, Lazy SPCA reduced 43.7 hours of computation to 9.9 hours. Overall, Lazy SPCA relies exclusively on matrix multiplications, besides an operation on a small square matrix whose size depends only on the target dimensionality. |
| Tasks | Dimensionality Reduction |
| Published | 2017-09-21 |
| URL | http://arxiv.org/abs/1709.07175v1 |
| http://arxiv.org/pdf/1709.07175v1.pdf | |
| PWC | https://paperswithcode.com/paper/lazy-stochastic-principal-component-analysis |
| Repo | |
| Framework | |
Multi-Agent Diverse Generative Adversarial Networks
| Title | Multi-Agent Diverse Generative Adversarial Networks |
| Authors | Arnab Ghosh, Viveka Kulharia, Vinay Namboodiri, Philip H. S. Torr, Puneet K. Dokania |
| Abstract | We propose MAD-GAN, an intuitive generalization to the Generative Adversarial Networks (GANs) and its conditional variants to address the well known problem of mode collapse. First, MAD-GAN is a multi-agent GAN architecture incorporating multiple generators and one discriminator. Second, to enforce that different generators capture diverse high probability modes, the discriminator of MAD-GAN is designed such that along with finding the real and fake samples, it is also required to identify the generator that generated the given fake sample. Intuitively, to succeed in this task, the discriminator must learn to push different generators towards different identifiable modes. We perform extensive experiments on synthetic and real datasets and compare MAD-GAN with different variants of GAN. We show high quality diverse sample generations for challenging tasks such as image-to-image translation and face generation. In addition, we also show that MAD-GAN is able to disentangle different modalities when trained using highly challenging diverse-class dataset (e.g. dataset with images of forests, icebergs, and bedrooms). In the end, we show its efficacy on the unsupervised feature representation task. In Appendix, we introduce a similarity based competing objective (MAD-GAN-Sim) which encourages different generators to generate diverse samples based on a user defined similarity metric. We show its performance on the image-to-image translation, and also show its effectiveness on the unsupervised feature representation task. |
| Tasks | Face Generation, Image-to-Image Translation |
| Published | 2017-04-10 |
| URL | http://arxiv.org/abs/1704.02906v3 |
| http://arxiv.org/pdf/1704.02906v3.pdf | |
| PWC | https://paperswithcode.com/paper/multi-agent-diverse-generative-adversarial |
| Repo | |
| Framework | |
