May 6, 2019

3156 words 15 mins read

Paper Group ANR 192

Equity forecast: Predicting long term stock price movement using machine learning. Query Complexity of Tournament Solutions. Hierarchical Compound Poisson Factorization. Collaborative creativity with Monte-Carlo Tree Search and Convolutional Neural Networks. A neuro-mathematical model for geometrical optical illusions. Visualisation of Survey Respo …

Equity forecast: Predicting long term stock price movement using machine learning


Title	Equity forecast: Predicting long term stock price movement using machine learning
Authors	Nikola Milosevic
Abstract	Long term investment is one of the major investment strategies. However, calculating intrinsic value of some company and evaluating shares for long term investment is not easy, since analyst have to care about a large number of financial indicators and evaluate them in a right manner. So far, little help in predicting the direction of the company value over the longer period of time has been provided from the machines. In this paper we present a machine learning aided approach to evaluate the equity’s future price over the long time. Our method is able to correctly predict whether some company’s value will be 10% higher or not over the period of one year in 76.5% of cases.
Tasks
Published	2016-03-02
URL	http://arxiv.org/abs/1603.00751v2
PDF	http://arxiv.org/pdf/1603.00751v2.pdf
PWC	https://paperswithcode.com/paper/equity-forecast-predicting-long-term-stock
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Framework

Query Complexity of Tournament Solutions


Title	Query Complexity of Tournament Solutions
Authors	Palash Dey
Abstract	A directed graph where there is exactly one edge between every pair of vertices is called a {\em tournament}. Finding the “best” set of vertices of a tournament is a well studied problem in social choice theory. A {\em tournament solution} takes a tournament as input and outputs a subset of vertices of the input tournament. However, in many applications, for example, choosing the best set of drugs from a given set of drugs, the edges of the tournament are given only implicitly and knowing the orientation of an edge is costly. In such scenarios, we would like to know the best set of vertices (according to some tournament solution) by “querying” as few edges as possible. We, in this paper, precisely study this problem for commonly used tournament solutions: given an oracle access to the edges of a tournament T, find $f(T)$ by querying as few edges as possible, for a tournament solution f. We first show that the set of Condorcet non-losers in a tournament can be found by querying $2n-\lfloor \log n \rfloor -2$ edges only and this is tight in the sense that every algorithm for finding the set of Condorcet non-losers needs to query at least $2n-\lfloor \log n \rfloor -2$ edges in the worst case, where $n$ is the number of vertices in the input tournament. We then move on to study other popular tournament solutions and show that any algorithm for finding the Copeland set, the Slater set, the Markov set, the bipartisan set, the uncovered set, the Banks set, and the top cycle must query $\Omega(n^2)$ edges in the worst case. On the positive side, we are able to circumvent our strong query complexity lower bound results by proving that, if the size of the top cycle of the input tournament is at most $k$, then we can find all the tournament solutions mentioned above by querying $O(nk + \frac{n\log n}{\log(1-\frac{1}{k})})$ edges only.
Tasks
Published	2016-11-18
URL	http://arxiv.org/abs/1611.06189v3
PDF	http://arxiv.org/pdf/1611.06189v3.pdf
PWC	https://paperswithcode.com/paper/query-complexity-of-tournament-solutions
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Framework

Hierarchical Compound Poisson Factorization


Title	Hierarchical Compound Poisson Factorization
Authors	Mehmet E. Basbug, Barbara E. Engelhardt
Abstract	Non-negative matrix factorization models based on a hierarchical Gamma-Poisson structure capture user and item behavior effectively in extremely sparse data sets, making them the ideal choice for collaborative filtering applications. Hierarchical Poisson factorization (HPF) in particular has proved successful for scalable recommendation systems with extreme sparsity. HPF, however, suffers from a tight coupling of sparsity model (absence of a rating) and response model (the value of the rating), which limits the expressiveness of the latter. Here, we introduce hierarchical compound Poisson factorization (HCPF) that has the favorable Gamma-Poisson structure and scalability of HPF to high-dimensional extremely sparse matrices. More importantly, HCPF decouples the sparsity model from the response model, allowing us to choose the most suitable distribution for the response. HCPF can capture binary, non-negative discrete, non-negative continuous, and zero-inflated continuous responses. We compare HCPF with HPF on nine discrete and three continuous data sets and conclude that HCPF captures the relationship between sparsity and response better than HPF.
Tasks	Recommendation Systems
Published	2016-04-13
URL	http://arxiv.org/abs/1604.03853v2
PDF	http://arxiv.org/pdf/1604.03853v2.pdf
PWC	https://paperswithcode.com/paper/hierarchical-compound-poisson-factorization
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Collaborative creativity with Monte-Carlo Tree Search and Convolutional Neural Networks


Title	Collaborative creativity with Monte-Carlo Tree Search and Convolutional Neural Networks
Authors	Memo Akten, Mick Grierson
Abstract	We investigate a human-machine collaborative drawing environment in which an autonomous agent sketches images while optionally allowing a user to directly influence the agent’s trajectory. We combine Monte Carlo Tree Search with image classifiers and test both shallow models (e.g. multinomial logistic regression) and deep Convolutional Neural Networks (e.g. LeNet, Inception v3). We found that using the shallow model, the agent produces a limited variety of images, which are noticably recogonisable by humans. However, using the deeper models, the agent produces a more diverse range of images, and while the agent remains very confident (99.99%) in having achieved its objective, to humans they mostly resemble unrecognisable ‘random’ noise. We relate this to recent research which also discovered that ‘deep neural networks are easily fooled’ \cite{Nguyen2015} and we discuss possible solutions and future directions for the research.
Tasks
Published	2016-12-14
URL	http://arxiv.org/abs/1612.04876v1
PDF	http://arxiv.org/pdf/1612.04876v1.pdf
PWC	https://paperswithcode.com/paper/collaborative-creativity-with-monte-carlo
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Framework

A neuro-mathematical model for geometrical optical illusions


Title	A neuro-mathematical model for geometrical optical illusions
Authors	B. Franceschiello, A. Sarti, G. Citti
Abstract	Geometrical optical illusions have been object of many studies due to the possibility they offer to understand the behaviour of low-level visual processing. They consist in situations in which the perceived geometrical properties of an object differ from those of the object in the visual stimulus. Starting from the geometrical model introduced by Citti and Sarti in [3], we provide a mathematical model and a computational algorithm which allows to interpret these phenomena and to qualitatively reproduce the perceived misperception.
Tasks
Published	2016-11-27
URL	http://arxiv.org/abs/1611.08844v1
PDF	http://arxiv.org/pdf/1611.08844v1.pdf
PWC	https://paperswithcode.com/paper/a-neuro-mathematical-model-for-geometrical
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Framework

Visualisation of Survey Responses using Self-Organising Maps: A Case Study on Diabetes Self-care Factors


Title	Visualisation of Survey Responses using Self-Organising Maps: A Case Study on Diabetes Self-care Factors
Authors	Santosh Tirunagari, Simon Bull, Samaneh Kouchaki, Deborah Cooke, Norman Poh
Abstract	Due to the chronic nature of diabetes, patient self-care factors play an important role in any treatment plan. In order to understand the behaviour of patients in response to medical advice on self-care, clinicians often conduct cross-sectional surveys. When analysing the survey data, statistical machine learning methods can potentially provide additional insight into the data either through deeper understanding of the patterns present or making information available to clinicians in an intuitive manner. In this study, we use self-organising maps (SOMs) to visualise the responses of patients who share similar responses to survey questions, with the goal of helping clinicians understand how patients are managing their treatment and where action should be taken. The principle behavioural patterns revealed through this are that: patients who take the correct dose of insulin also tend to take their injections at the correct time, patients who eat on time also tend to correctly manage their food portions and patients who check their blood glucose with a monitor also tend to adjust their insulin dosage and carry snacks to counter low blood glucose. The identification of these positive behavioural patterns can also help to inform treatment by exploiting their negative corollaries.
Tasks
Published	2016-08-30
URL	http://arxiv.org/abs/1609.05716v1
PDF	http://arxiv.org/pdf/1609.05716v1.pdf
PWC	https://paperswithcode.com/paper/visualisation-of-survey-responses-using-self
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Framework

Anomaly Detection and Localisation using Mixed Graphical Models


Title	Anomaly Detection and Localisation using Mixed Graphical Models
Authors	Romain Laby, François Roueff, Alexandre Gramfort
Abstract	We propose a method that performs anomaly detection and localisation within heterogeneous data using a pairwise undirected mixed graphical model. The data are a mixture of categorical and quantitative variables, and the model is learned over a dataset that is supposed not to contain any anomaly. We then use the model over temporal data, potentially a data stream, using a version of the two-sided CUSUM algorithm. The proposed decision statistic is based on a conditional likelihood ratio computed for each variable given the others. Our results show that this function allows to detect anomalies variable by variable, and thus to localise the variables involved in the anomalies more precisely than univariate methods based on simple marginals.
Tasks	Anomaly Detection
Published	2016-07-20
URL	http://arxiv.org/abs/1607.05974v1
PDF	http://arxiv.org/pdf/1607.05974v1.pdf
PWC	https://paperswithcode.com/paper/anomaly-detection-and-localisation-using
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Framework

Learning in the Machine: Random Backpropagation and the Deep Learning Channel


Title	Learning in the Machine: Random Backpropagation and the Deep Learning Channel
Authors	Pierre Baldi, Peter Sadowski, Zhiqin Lu
Abstract	Random backpropagation (RBP) is a variant of the backpropagation algorithm for training neural networks, where the transpose of the forward matrices are replaced by fixed random matrices in the calculation of the weight updates. It is remarkable both because of its effectiveness, in spite of using random matrices to communicate error information, and because it completely removes the taxing requirement of maintaining symmetric weights in a physical neural system. To better understand random backpropagation, we first connect it to the notions of local learning and learning channels. Through this connection, we derive several alternatives to RBP, including skipped RBP (SRPB), adaptive RBP (ARBP), sparse RBP, and their combinations (e.g. ASRBP) and analyze their computational complexity. We then study their behavior through simulations using the MNIST and CIFAR-10 bechnmark datasets. These simulations show that most of these variants work robustly, almost as well as backpropagation, and that multiplication by the derivatives of the activation functions is important. As a follow-up, we study also the low-end of the number of bits required to communicate error information over the learning channel. We then provide partial intuitive explanations for some of the remarkable properties of RBP and its variations. Finally, we prove several mathematical results, including the convergence to fixed points of linear chains of arbitrary length, the convergence to fixed points of linear autoencoders with decorrelated data, the long-term existence of solutions for linear systems with a single hidden layer and convergence in special cases, and the convergence to fixed points of non-linear chains, when the derivative of the activation functions is included.
Tasks
Published	2016-12-08
URL	http://arxiv.org/abs/1612.02734v2
PDF	http://arxiv.org/pdf/1612.02734v2.pdf
PWC	https://paperswithcode.com/paper/learning-in-the-machine-random
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Framework

Benchmark for License Plate Character Segmentation


Title	Benchmark for License Plate Character Segmentation
Authors	Gabriel Resende Gonçalves, Sirlene Pio Gomes da Silva, David Menotti, William Robson Schwartz
Abstract	Automatic License Plate Recognition (ALPR) has been the focus of many researches in the past years. In general, ALPR is divided into the following problems: detection of on-track vehicles, license plates detection, segmention of license plate characters and optical character recognition (OCR). Even though commercial solutions are available for controlled acquisition conditions, e.g., the entrance of a parking lot, ALPR is still an open problem when dealing with data acquired from uncontrolled environments, such as roads and highways when relying only on imaging sensors. Due to the multiple orientations and scales of the license plates captured by the camera, a very challenging task of the ALPR is the License Plate Character Segmentation (LPCS) step, which effectiveness is required to be (near) optimal to achieve a high recognition rate by the OCR. To tackle the LPCS problem, this work proposes a novel benchmark composed of a dataset designed to focus specifically on the character segmentation step of the ALPR within an evaluation protocol. Furthermore, we propose the Jaccard-Centroid coefficient, a new evaluation measure more suitable than the Jaccard coefficient regarding the location of the bounding box within the ground-truth annotation. The dataset is composed of 2,000 Brazilian license plates consisting of 14,000 alphanumeric symbols and their corresponding bounding box annotations. We also present a new straightforward approach to perform LPCS efficiently. Finally, we provide an experimental evaluation for the dataset based on four LPCS approaches and demonstrate the importance of character segmentation for achieving an accurate OCR.
Tasks	License Plate Recognition, Optical Character Recognition
Published	2016-07-11
URL	http://arxiv.org/abs/1607.02937v2
PDF	http://arxiv.org/pdf/1607.02937v2.pdf
PWC	https://paperswithcode.com/paper/benchmark-for-license-plate-character
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Framework

Maximum Correntropy Unscented Filter


Title	Maximum Correntropy Unscented Filter
Authors	Xi Liu, Badong Chen, Bin Xu, Zongze Wu, Paul Honeine
Abstract	The unscented transformation (UT) is an efficient method to solve the state estimation problem for a non-linear dynamic system, utilizing a derivative-free higher-order approximation by approximating a Gaussian distribution rather than approximating a non-linear function. Applying the UT to a Kalman filter type estimator leads to the well-known unscented Kalman filter (UKF). Although the UKF works very well in Gaussian noises, its performance may deteriorate significantly when the noises are non-Gaussian, especially when the system is disturbed by some heavy-tailed impulsive noises. To improve the robustness of the UKF against impulsive noises, a new filter for nonlinear systems is proposed in this work, namely the maximum correntropy unscented filter (MCUF). In MCUF, the UT is applied to obtain the prior estimates of the state and covariance matrix, and a robust statistical linearization regression based on the maximum correntropy criterion (MCC) is then used to obtain the posterior estimates of the state and covariance. The satisfying performance of the new algorithm is confirmed by two illustrative examples.
Tasks
Published	2016-08-26
URL	http://arxiv.org/abs/1608.07526v1
PDF	http://arxiv.org/pdf/1608.07526v1.pdf
PWC	https://paperswithcode.com/paper/maximum-correntropy-unscented-filter
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Framework

Presenting a New Dataset for the Timeline Generation Problem


Title	Presenting a New Dataset for the Timeline Generation Problem
Authors	Xavier Holt, Will Radford, Ben Hachey
Abstract	The timeline generation task summarises an entity’s biography by selecting stories representing key events from a large pool of relevant documents. This paper addresses the lack of a standard dataset and evaluative methodology for the problem. We present and make publicly available a new dataset of 18,793 news articles covering 39 entities. For each entity, we provide a gold standard timeline and a set of entity-related articles. We propose ROUGE as an evaluation metric and validate our dataset by showing that top Google results outperform straw-man baselines.
Tasks
Published	2016-11-07
URL	http://arxiv.org/abs/1611.02025v1
PDF	http://arxiv.org/pdf/1611.02025v1.pdf
PWC	https://paperswithcode.com/paper/presenting-a-new-dataset-for-the-timeline
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Framework

Deep Feature Fusion Network for Answer Quality Prediction in Community Question Answering


Title	Deep Feature Fusion Network for Answer Quality Prediction in Community Question Answering
Authors	Sai Praneeth Suggu, Kushwanth N. Goutham, Manoj K. Chinnakotla, Manish Shrivastava
Abstract	Community Question Answering (cQA) forums have become a popular medium for soliciting direct answers to specific questions of users from experts or other experienced users on a given topic. However, for a given question, users sometimes have to sift through a large number of low-quality or irrelevant answers to find out the answer which satisfies their information need. To alleviate this, the problem of Answer Quality Prediction (AQP) aims to predict the quality of an answer posted in response to a forum question. Current AQP systems either learn models using - a) various hand-crafted features (HCF) or b) use deep learning (DL) techniques which automatically learn the required feature representations. In this paper, we propose a novel approach for AQP known as - “Deep Feature Fusion Network (DFFN)” which leverages the advantages of both hand-crafted features and deep learning based systems. Given a question-answer pair along with its metadata, DFFN independently - a) learns deep features using a Convolutional Neural Network (CNN) and b) computes hand-crafted features using various external resources and then combines them using a deep neural network trained to predict the final answer quality. DFFN achieves state-of-the-art performance on the standard SemEval-2015 and SemEval-2016 benchmark datasets and outperforms baseline approaches which individually employ either HCF or DL based techniques alone.
Tasks	Community Question Answering, Question Answering
Published	2016-06-22
URL	http://arxiv.org/abs/1606.07103v2
PDF	http://arxiv.org/pdf/1606.07103v2.pdf
PWC	https://paperswithcode.com/paper/deep-feature-fusion-network-for-answer
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Framework

Fast Cosine Similarity Search in Binary Space with Angular Multi-index Hashing


Title	Fast Cosine Similarity Search in Binary Space with Angular Multi-index Hashing
Authors	Sepehr Eghbali, Ladan Tahvildari
Abstract	Given a large dataset of binary codes and a binary query point, we address how to efficiently find $K$ codes in the dataset that yield the largest cosine similarities to the query. The straightforward answer to this problem is to compare the query with all items in the dataset, but this is practical only for small datasets. One potential solution to enhance the search time and achieve sublinear cost is to use a hash table populated with binary codes of the dataset and then look up the nearby buckets to the query to retrieve the nearest neighbors. However, if codes are compared in terms of cosine similarity rather than the Hamming distance, then the main issue is that the order of buckets to probe is not evident. To examine this issue, we first elaborate on the connection between the Hamming distance and the cosine similarity. Doing this allows us to systematically find the probing sequence in the hash table. However, solving the nearest neighbor search with a single table is only practical for short binary codes. To address this issue, we propose the angular multi-index hashing search algorithm which relies on building multiple hash tables on binary code substrings. The proposed search algorithm solves the exact angular $K$ nearest neighbor problem in a time that is often orders of magnitude faster than the linear scan baseline and even approximation methods.
Tasks
Published	2016-09-14
URL	http://arxiv.org/abs/1610.00574v2
PDF	http://arxiv.org/pdf/1610.00574v2.pdf
PWC	https://paperswithcode.com/paper/fast-cosine-similarity-search-in-binary-space
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Framework

Focused Meeting Summarization via Unsupervised Relation Extraction


Title	Focused Meeting Summarization via Unsupervised Relation Extraction
Authors	Lu Wang, Claire Cardie
Abstract	We present a novel unsupervised framework for focused meeting summarization that views the problem as an instance of relation extraction. We adapt an existing in-domain relation learner (Chen et al., 2011) by exploiting a set of task-specific constraints and features. We evaluate the approach on a decision summarization task and show that it outperforms unsupervised utterance-level extractive summarization baselines as well as an existing generic relation-extraction-based summarization method. Moreover, our approach produces summaries competitive with those generated by supervised methods in terms of the standard ROUGE score.
Tasks	Meeting Summarization, Relation Extraction
Published	2016-06-24
URL	http://arxiv.org/abs/1606.07849v1
PDF	http://arxiv.org/pdf/1606.07849v1.pdf
PWC	https://paperswithcode.com/paper/focused-meeting-summarization-via
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A Compositional Approach to Language Modeling


Title	A Compositional Approach to Language Modeling
Authors	Kushal Arora, Anand Rangarajan
Abstract	Traditional language models treat language as a finite state automaton on a probability space over words. This is a very strong assumption when modeling something inherently complex such as language. In this paper, we challenge this by showing how the linear chain assumption inherent in previous work can be translated into a sequential composition tree. We then propose a new model that marginalizes over all possible composition trees thereby removing any underlying structural assumptions. As the partition function of this new model is intractable, we use a recently proposed sentence level evaluation metric Contrastive Entropy to evaluate our model. Given this new evaluation metric, we report more than 100% improvement across distortion levels over current state of the art recurrent neural network based language models.
Tasks	Language Modelling
Published	2016-04-01
URL	http://arxiv.org/abs/1604.00100v1
PDF	http://arxiv.org/pdf/1604.00100v1.pdf
PWC	https://paperswithcode.com/paper/a-compositional-approach-to-language-modeling
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