October 17, 2019

3247 words 16 mins read

Paper Group ANR 859

Contributions to Biclustering of Microarray Data Using Formal Concept Analysis. Towards Fine Grained Network Flow Prediction. Adaptive Regularization Algorithms with Inexact Evaluations for Nonconvex Optimization. Theoretical and Experimental Analysis on the Generalizability of Distribution Regression Network. An Exact Quantized Decentralized Gradi …

Contributions to Biclustering of Microarray Data Using Formal Concept Analysis


Title	Contributions to Biclustering of Microarray Data Using Formal Concept Analysis
Authors	Amina Houari
Abstract	Biclustering is an unsupervised data mining technique that aims to unveil patterns (biclusters) from gene expression data matrices. In the framework of this thesis, we propose new biclustering algorithms for microarray data. The latter is done using data mining techniques. The objective is to identify positively and negatively correlated biclusters. This thesis is divided into two part: In the first part, we present an overview of the pattern-mining techniques and the biclustering of microarray data. In the second part, we present our proposed biclustering algorithms where we rely on two axes. In the first axis, we initially focus on extracting biclusters of positive correlations. For this, we use both Formal Concept Analysis and Association Rules. In the second axis, we focus on the extraction of negatively correlated biclusters. The performed experimental studies highlight the very promising results offered by the proposed algorithms. Our biclustering algorithms are evaluated and compared statistically and biologically.
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Published	2018-11-23
URL	http://arxiv.org/abs/1811.09562v1
PDF	http://arxiv.org/pdf/1811.09562v1.pdf
PWC	https://paperswithcode.com/paper/contributions-to-biclustering-of-microarray
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Towards Fine Grained Network Flow Prediction


Title	Towards Fine Grained Network Flow Prediction
Authors	Patrick Jahnke, Emmanuel Stapf, Jonas Mieseler, Gerhard Neumann, Patrick Eugster
Abstract	One main challenge for the design of networks is that traffic load is not generally known in advance. This makes it hard to adequately devote resources such as to best prevent or mitigate bottlenecks. While several authors have shown how to predict traffic in a coarse grained manner by aggregating flows, fine grained prediction of traffic at the level of individual flows, including bursty traffic, is widely considered to be impossible. This paper shows, to the best of our knowledge, the first approach to fine grained per flow traffic prediction. In short, we introduce the Frequency-based Kernel Kalman Filter (FKKF), which predicts individual flows’ behavior based on measurements. Our FKKF relies on the well known Kalman Filter in combination with a kernel to support the prediction of non linear functions. Furthermore we change the operating space from time to frequency space. In this space, into which we transform the input data via a Short-Time Fourier Transform (STFT), the peak structures of flows can be predicted after gleaning their key characteristics, with a Principal Component Analysis (PCA), from past and ongoing flows that stem from the same socket-to-socket connection. We demonstrate the effectiveness of our approach on popular benchmark traces from a university data center. Our approach predicts traffic on average across 17 out of 20 groups of flows with an average prediction error of 6.43% around 0.49 (average) seconds in advance, whilst existing coarse grained approaches exhibit prediction errors of 77% at best.
Tasks	Traffic Prediction
Published	2018-08-20
URL	http://arxiv.org/abs/1808.06453v1
PDF	http://arxiv.org/pdf/1808.06453v1.pdf
PWC	https://paperswithcode.com/paper/towards-fine-grained-network-flow-prediction
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Adaptive Regularization Algorithms with Inexact Evaluations for Nonconvex Optimization


Title	Adaptive Regularization Algorithms with Inexact Evaluations for Nonconvex Optimization
Authors	S. Bellavia, G. Gurioli, B. Morini, Ph. L. Toint
Abstract	A regularization algorithm using inexact function values and inexact derivatives is proposed and its evaluation complexity analyzed. This algorithm is applicable to unconstrained problems and to problems with inexpensive constraints (that is constraints whose evaluation and enforcement has negligible cost) under the assumption that the derivative of highest degree is $\beta$-H"{o}lder continuous. It features a very flexible adaptive mechanism for determining the inexactness which is allowed, at each iteration, when computing objective function values and derivatives. The complexity analysis covers arbitrary optimality order and arbitrary degree of available approximate derivatives. It extends results of Cartis, Gould and Toint (2018) on the evaluation complexity to the inexact case: if a $q$th order minimizer is sought using approximations to the first $p$ derivatives, it is proved that a suitable approximate minimizer within $\epsilon$ is computed by the proposed algorithm in at most $O(\epsilon^{-\frac{p+\beta}{p-q+\beta}})$ iterations and at most $O(\log(\epsilon)\epsilon^{-\frac{p+\beta}{p-q+\beta}})$ approximate evaluations. An algorithmic variant, although more rigid in practice, can be proved to find such an approximate minimizer in $O(\log(\epsilon)+\epsilon^{-\frac{p+\beta}{p-q+\beta}})$ evaluations.While the proposed framework remains so far conceptual for high degrees and orders, it is shown to yield simple and computationally realistic inexact methods when specialized to the unconstrained and bound-constrained first- and second-order cases. The deterministic complexity results are finally extended to the stochastic context, yielding adaptive sample-size rules for subsampling methods typical of machine learning.
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Published	2018-11-09
URL	http://arxiv.org/abs/1811.03831v3
PDF	http://arxiv.org/pdf/1811.03831v3.pdf
PWC	https://paperswithcode.com/paper/deterministic-and-stochastic-inexact
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Theoretical and Experimental Analysis on the Generalizability of Distribution Regression Network


Title	Theoretical and Experimental Analysis on the Generalizability of Distribution Regression Network
Authors	Connie Kou, Hwee Kuan Lee, Jorge Sanz, Teck Khim Ng
Abstract	There is emerging interest in performing regression between distributions. In contrast to prediction on single instances, these machine learning methods can be useful for population-based studies or on problems that are inherently statistical in nature. The recently proposed distribution regression network (DRN) has shown superior performance for the distribution-to-distribution regression task compared to conventional neural networks. However, in Kou et al. (2018) and some other works on distribution regression, there is a lack of comprehensive comparative study on both theoretical basis and generalization abilities of the methods. We derive some mathematical properties of DRN and qualitatively compare it to conventional neural networks. We also perform comprehensive experiments to study the generalizability of distribution regression models, by studying their robustness to limited training data, data sampling noise and task difficulty. DRN consistently outperforms conventional neural networks, requiring fewer training data and maintaining robust performance with noise. Furthermore, the theoretical properties of DRN can be used to provide some explanation on the ability of DRN to achieve better generalization performance than conventional neural networks.
Tasks	Time Series
Published	2018-11-05
URL	https://arxiv.org/abs/1811.01506v3
PDF	https://arxiv.org/pdf/1811.01506v3.pdf
PWC	https://paperswithcode.com/paper/an-efficient-network-for-predicting-time
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An Exact Quantized Decentralized Gradient Descent Algorithm


Title	An Exact Quantized Decentralized Gradient Descent Algorithm
Authors	Amirhossein Reisizadeh, Aryan Mokhtari, Hamed Hassani, Ramtin Pedarsani
Abstract	We consider the problem of decentralized consensus optimization, where the sum of $n$ smooth and strongly convex functions are minimized over $n$ distributed agents that form a connected network. In particular, we consider the case that the communicated local decision variables among nodes are quantized in order to alleviate the communication bottleneck in distributed optimization. We propose the Quantized Decentralized Gradient Descent (QDGD) algorithm, in which nodes update their local decision variables by combining the quantized information received from their neighbors with their local information. We prove that under standard strong convexity and smoothness assumptions for the objective function, QDGD achieves a vanishing mean solution error under customary conditions for quantizers. To the best of our knowledge, this is the first algorithm that achieves vanishing consensus error in the presence of quantization noise. Moreover, we provide simulation results that show tight agreement between our derived theoretical convergence rate and the numerical results.
Tasks	Distributed Optimization, Quantization
Published	2018-06-29
URL	https://arxiv.org/abs/1806.11536v3
PDF	https://arxiv.org/pdf/1806.11536v3.pdf
PWC	https://paperswithcode.com/paper/an-exact-quantized-decentralized-gradient
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Synthetic data generation for Indic handwritten text recognition


Title	Synthetic data generation for Indic handwritten text recognition
Authors	Partha Pratim Roy, Akash Mohta, Bidyut B. Chaudhuri
Abstract	This paper presents a novel approach to generate synthetic dataset for handwritten word recognition systems. It is difficult to recognize handwritten scripts for which sufficient training data is not readily available or it may be expensive to collect such data. Hence, it becomes hard to train recognition systems owing to lack of proper dataset. To overcome such problems, synthetic data could be used to create or expand the existing training dataset to improve recognition performance. Any available digital data from online newspaper and such sources can be used to generate synthetic data. In this paper, we propose to add distortion/deformation to digital data in such a way that the underlying pattern is preserved, so that the image so produced bears a close similarity to actual handwritten samples. The images thus produced can be used independently to train the system or be combined with natural handwritten data to augment the original dataset and improve the recognition system. We experimented using synthetic data to improve the recognition accuracy of isolated characters and words. The framework is tested on 2 Indic scripts - Devanagari (Hindi) and Bengali (Bangla), for numeral, character and word recognition. We have obtained encouraging results from the experiment. Finally, the experiment with Latin text verifies the utility of the approach.
Tasks	Synthetic Data Generation
Published	2018-04-17
URL	http://arxiv.org/abs/1804.06254v1
PDF	http://arxiv.org/pdf/1804.06254v1.pdf
PWC	https://paperswithcode.com/paper/synthetic-data-generation-for-indic
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Driving Scene Perception Network: Real-time Joint Detection, Depth Estimation and Semantic Segmentation


Title	Driving Scene Perception Network: Real-time Joint Detection, Depth Estimation and Semantic Segmentation
Authors	Liangfu Chen, Zeng Yang, Jianjun Ma, Zheng Luo
Abstract	As the demand for enabling high-level autonomous driving has increased in recent years and visual perception is one of the critical features to enable fully autonomous driving, in this paper, we introduce an efficient approach for simultaneous object detection, depth estimation and pixel-level semantic segmentation using a shared convolutional architecture. The proposed network model, which we named Driving Scene Perception Network (DSPNet), uses multi-level feature maps and multi-task learning to improve the accuracy and efficiency of object detection, depth estimation and image segmentation tasks from a single input image. Hence, the resulting network model uses less than 850 MiB of GPU memory and achieves 14.0 fps on NVIDIA GeForce GTX 1080 with a 1024x512 input image, and both precision and efficiency have been improved over combination of single tasks.
Tasks	Autonomous Driving, Depth Estimation, Multi-Task Learning, Object Detection, Semantic Segmentation
Published	2018-03-10
URL	http://arxiv.org/abs/1803.03778v1
PDF	http://arxiv.org/pdf/1803.03778v1.pdf
PWC	https://paperswithcode.com/paper/driving-scene-perception-network-real-time
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Multi-Scale Generalized Plane Match for Optical Flow


Title	Multi-Scale Generalized Plane Match for Optical Flow
Authors	Inchul Choi, Arunava Banerjee
Abstract	Despite recent advances, estimating optical flow remains a challenging problem in the presence of illumination change, large occlusions or fast movement. In this paper, we propose a novel optical flow estimation framework which can provide accurate dense correspondence and occlusion localization through a multi-scale generalized plane matching approach. In our method, we regard the scene as a collection of planes at multiple scales, and for each such plane, compensate motion in consensus to improve match quality. We estimate the square patch plane distortion using a robust plane model detection method and iteratively apply a plane matching scheme within a multi-scale framework. During the flow estimation process, our enhanced plane matching method also clearly localizes the occluded regions. In experiments on MPI-Sintel datasets, our method robustly estimated optical flow from given noisy correspondences, and also revealed the occluded regions accurately. Compared to other state-of-the-art optical flow methods, our method shows accurate occlusion localization, comparable optical flow quality, and better thin object detection.
Tasks	Object Detection, Optical Flow Estimation
Published	2018-04-11
URL	http://arxiv.org/abs/1804.03787v1
PDF	http://arxiv.org/pdf/1804.03787v1.pdf
PWC	https://paperswithcode.com/paper/multi-scale-generalized-plane-match-for
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Learning Graph Weighted Models on Pictures


Title	Learning Graph Weighted Models on Pictures
Authors	Philip Amortila, Guillaume Rabusseau
Abstract	Graph Weighted Models (GWMs) have recently been proposed as a natural generalization of weighted automata over strings and trees to arbitrary families of labeled graphs (and hypergraphs). A GWM generically associates a labeled graph with a tensor network and computes a value by successive contractions directed by its edges. In this paper, we consider the problem of learning GWMs defined over the graph family of pictures (or 2-dimensional words). As a proof of concept, we consider regression and classification tasks over the simple Bars & Stripes and Shifting Bits picture languages and provide an experimental study investigating whether these languages can be learned in the form of a GWM from positive and negative examples using gradient-based methods. Our results suggest that this is indeed possible and that investigating the use of gradient-based methods to learn picture series and functions computed by GWMs over other families of graphs could be a fruitful direction.
Tasks
Published	2018-06-21
URL	http://arxiv.org/abs/1806.08297v2
PDF	http://arxiv.org/pdf/1806.08297v2.pdf
PWC	https://paperswithcode.com/paper/learning-graph-weighted-models-on-pictures
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Stream attention-based multi-array end-to-end speech recognition


Title	Stream attention-based multi-array end-to-end speech recognition
Authors	Xiaofei Wang, Ruizhi Li, Sri Harish Mallid, Takaaki Hori, Shinji Watanabe, Hynek Hermansky
Abstract	Automatic Speech Recognition (ASR) using multiple microphone arrays has achieved great success in the far-field robustness. Taking advantage of all the information that each array shares and contributes is crucial in this task. Motivated by the advances of joint Connectionist Temporal Classification (CTC)/attention mechanism in the End-to-End (E2E) ASR, a stream attention-based multi-array framework is proposed in this work. Microphone arrays, acting as information streams, are activated by separate encoders and decoded under the instruction of both CTC and attention networks. In terms of attention, a hierarchical structure is adopted. On top of the regular attention networks, stream attention is introduced to steer the decoder toward the most informative encoders. Experiments have been conducted on AMI and DIRHA multi-array corpora using the encoder-decoder architecture. Compared with the best single-array results, the proposed framework has achieved relative Word Error Rates (WERs) reduction of 3.7% and 9.7% in the two datasets, respectively, which is better than conventional strategies as well.
Tasks	End-To-End Speech Recognition, Speech Recognition
Published	2018-11-12
URL	http://arxiv.org/abs/1811.04903v2
PDF	http://arxiv.org/pdf/1811.04903v2.pdf
PWC	https://paperswithcode.com/paper/stream-attention-based-multi-array-end-to-end
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MaxMin Linear Initialization for Fuzzy C-Means


Title	MaxMin Linear Initialization for Fuzzy C-Means
Authors	Aybükë Oztürk, Stéphane Lallich, Jérôme Darmont, Sylvie Yona Waksman
Abstract	Clustering is an extensive research area in data science. The aim of clustering is to discover groups and to identify interesting patterns in datasets. Crisp (hard) clustering considers that each data point belongs to one and only one cluster. However, it is inadequate as some data points may belong to several clusters, as is the case in text categorization. Thus, we need more flexible clustering. Fuzzy clustering methods, where each data point can belong to several clusters, are an interesting alternative. Yet, seeding iterative fuzzy algorithms to achieve high quality clustering is an issue. In this paper, we propose a new linear and efficient initialization algorithm MaxMin Linear to deal with this problem. Then, we validate our theoretical results through extensive experiments on a variety of numerical real-world and artificial datasets. We also test several validity indices, including a new validity index that we propose, Transformed Standardized Fuzzy Difference (TSFD).
Tasks	Text Categorization
Published	2018-08-01
URL	http://arxiv.org/abs/1808.00197v1
PDF	http://arxiv.org/pdf/1808.00197v1.pdf
PWC	https://paperswithcode.com/paper/maxmin-linear-initialization-for-fuzzy-c
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A Study of Recent Contributions on Information Extraction


Title	A Study of Recent Contributions on Information Extraction
Authors	Parisa Naderi Golshan, HosseinAli Rahmani Dashti, Shahrzad Azizi, Leila Safari
Abstract	This paper reports on modern approaches in Information Extraction (IE) and its two main sub-tasks of Named Entity Recognition (NER) and Relation Extraction (RE). Basic concepts and the most recent approaches in this area are reviewed, which mainly include Machine Learning (ML) based approaches and the more recent trend to Deep Learning (DL) based methods.
Tasks	Named Entity Recognition, Relation Extraction
Published	2018-03-15
URL	http://arxiv.org/abs/1803.05667v1
PDF	http://arxiv.org/pdf/1803.05667v1.pdf
PWC	https://paperswithcode.com/paper/a-study-of-recent-contributions-on
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On feature selection and evaluation of transportation mode prediction strategies


Title	On feature selection and evaluation of transportation mode prediction strategies
Authors	Mohammad Etemad, Amilcar Soares Junior, Stan Matwin
Abstract	Transportation modes prediction is a fundamental task for decision making in smart cities and traffic management systems. Traffic policies designed based on trajectory mining can save money and time for authorities and the public. It may reduce the fuel consumption and commute time and moreover, may provide more pleasant moments for residents and tourists. Since the number of features that may be used to predict a user transportation mode can be substantial, finding a subset of features that maximizes a performance measure is worth investigating. In this work, we explore wrapper and information retrieval methods to find the best subset of trajectory features. After finding the best classifier and the best feature subset, our results were compared with two related papers that applied deep learning methods and the results showed that our framework achieved better performance. Furthermore, two types of cross-validation approaches were investigated, and the performance results show that the random cross-validation method provides optimistic results.
Tasks	Decision Making, Feature Selection, Information Retrieval
Published	2018-08-09
URL	http://arxiv.org/abs/1808.03096v2
PDF	http://arxiv.org/pdf/1808.03096v2.pdf
PWC	https://paperswithcode.com/paper/on-feature-selection-and-evaluation-of
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Convolutional Neural Network: Text Classification Model for Open Domain Question Answering System


Title	Convolutional Neural Network: Text Classification Model for Open Domain Question Answering System
Authors	Muhammad Zain Amin, Noman Nadeem
Abstract	Recently machine learning is being applied to almost every data domain one of which is Question Answering Systems (QAS). A typical Question Answering System is fairly an information retrieval system, which matches documents or text and retrieve the most accurate one. The idea of open domain question answering system put forth, involves convolutional neural network text classifiers. The Classification model presented in this paper is multi-class text classifier. The neural network classifier can be trained on large dataset. We report series of experiments conducted on Convolution Neural Network (CNN) by training it on two different datasets. Neural network model is trained on top of word embedding. Softmax layer is applied to calculate loss and mapping of semantically related words. Gathered results can help justify the fact that proposed hypothetical QAS is feasible. We further propose a method to integrate Convolutional Neural Network Classifier to an open domain question answering system. The idea of Open domain will be further explained, but the generality of it indicates to the system of domain specific trainable models, thus making it an open domain.
Tasks	Information Retrieval, Open-Domain Question Answering, Question Answering, Text Classification
Published	2018-09-07
URL	https://arxiv.org/abs/1809.02479v2
PDF	https://arxiv.org/pdf/1809.02479v2.pdf
PWC	https://paperswithcode.com/paper/convolutional-neural-network-text
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Leveraging Aspect Phrase Embeddings for Cross-Domain Review Rating Prediction


Title	Leveraging Aspect Phrase Embeddings for Cross-Domain Review Rating Prediction
Authors	Aiqi Jiang, Arkaitz Zubiaga
Abstract	Online review platforms are a popular way for users to post reviews by expressing their opinions towards a product or service, as well as they are valuable for other users and companies to find out the overall opinions of customers. These reviews tend to be accompanied by a rating, where the star rating has become the most common approach for users to give their feedback in a quantitative way, generally as a likert scale of 1-5 stars. In other social media platforms like Facebook or Twitter, an automated review rating prediction system can be useful to determine the rating that a user would have given to the product or service. Existing work on review rating prediction focuses on specific domains, such as restaurants or hotels. This, however, ignores the fact that some review domains which are less frequently rated, such as dentists, lack sufficient data to build a reliable prediction model. In this paper, we experiment on 12 datasets pertaining to 12 different review domains of varying level of popularity to assess the performance of predictions across different domains. We introduce a model that leverages aspect phrase embeddings extracted from the reviews, which enables the development of both in-domain and cross-domain review rating prediction systems. Our experiments show that both of our review rating prediction systems outperform all other baselines. The cross-domain review rating prediction system is particularly significant for the least popular review domains, where leveraging training data from other domains leads to remarkable improvements in performance. The in-domain review rating prediction system is instead more suitable for popular review domains, provided that a model built from training data pertaining to the target domain is more suitable when this data is abundant.
Tasks
Published	2018-11-14
URL	http://arxiv.org/abs/1811.05689v1
PDF	http://arxiv.org/pdf/1811.05689v1.pdf
PWC	https://paperswithcode.com/paper/leveraging-aspect-phrase-embeddings-for-cross
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