July 27, 2019

3133 words 15 mins read

Paper Group ANR 659

Determining Song Similarity via Machine Learning Techniques and Tagging Information. Learning to Transfer. Predicting Session Length in Media Streaming. Consistent On-Line Off-Policy Evaluation. A Homological Theory of Functions. All the people around me: face discovery in egocentric photo-streams. Supervised Machine Learning for Signals Having RRC …

Determining Song Similarity via Machine Learning Techniques and Tagging Information


Title	Determining Song Similarity via Machine Learning Techniques and Tagging Information
Authors	Renato L. F. Cunha, Evandro Caldeira, Luciana Fujii
Abstract	The task of determining item similarity is a crucial one in a recommender system. This constitutes the base upon which the recommender system will work to determine which items are more likely to be enjoyed by a user, resulting in more user engagement. In this paper we tackle the problem of determining song similarity based solely on song metadata (such as the performer, and song title) and on tags contributed by users. We evaluate our approach under a series of different machine learning algorithms. We conclude that tf-idf achieves better results than Word2Vec to model the dataset to feature vectors. We also conclude that k-NN models have better performance than SVMs and Linear Regression for this problem.
Tasks	Recommendation Systems
Published	2017-04-12
URL	http://arxiv.org/abs/1704.03844v1
PDF	http://arxiv.org/pdf/1704.03844v1.pdf
PWC	https://paperswithcode.com/paper/determining-song-similarity-via-machine
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Learning to Transfer


Title	Learning to Transfer
Authors	Ying Wei, Yu Zhang, Qiang Yang
Abstract	Transfer learning borrows knowledge from a source domain to facilitate learning in a target domain. Two primary issues to be addressed in transfer learning are what and how to transfer. For a pair of domains, adopting different transfer learning algorithms results in different knowledge transferred between them. To discover the optimal transfer learning algorithm that maximally improves the learning performance in the target domain, researchers have to exhaustively explore all existing transfer learning algorithms, which is computationally intractable. As a trade-off, a sub-optimal algorithm is selected, which requires considerable expertise in an ad-hoc way. Meanwhile, it is widely accepted in educational psychology that human beings improve transfer learning skills of deciding what to transfer through meta-cognitive reflection on inductive transfer learning practices. Motivated by this, we propose a novel transfer learning framework known as Learning to Transfer (L2T) to automatically determine what and how to transfer are the best by leveraging previous transfer learning experiences. We establish the L2T framework in two stages: 1) we first learn a reflection function encrypting transfer learning skills from experiences; and 2) we infer what and how to transfer for a newly arrived pair of domains by optimizing the reflection function. Extensive experiments demonstrate the L2T’s superiority over several state-of-the-art transfer learning algorithms and its effectiveness on discovering more transferable knowledge.
Tasks	Transfer Learning
Published	2017-08-18
URL	http://arxiv.org/abs/1708.05629v1
PDF	http://arxiv.org/pdf/1708.05629v1.pdf
PWC	https://paperswithcode.com/paper/learning-to-transfer
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Predicting Session Length in Media Streaming


Title	Predicting Session Length in Media Streaming
Authors	Theodore Vasiloudis, Hossein Vahabi, Ross Kravitz, Valery Rashkov
Abstract	Session length is a very important aspect in determining a user’s satisfaction with a media streaming service. Being able to predict how long a session will last can be of great use for various downstream tasks, such as recommendations and ad scheduling. Most of the related literature on user interaction duration has focused on dwell time for websites, usually in the context of approximating post-click satisfaction either in search results, or display ads. In this work we present the first analysis of session length in a mobile-focused online service, using a real world data-set from a major music streaming service. We use survival analysis techniques to show that the characteristics of the length distributions can differ significantly between users, and use gradient boosted trees with appropriate objectives to predict the length of a session using only information available at its beginning. Our evaluation on real world data illustrates that our proposed technique outperforms the considered baseline.
Tasks	Survival Analysis
Published	2017-08-01
URL	http://arxiv.org/abs/1708.00130v1
PDF	http://arxiv.org/pdf/1708.00130v1.pdf
PWC	https://paperswithcode.com/paper/predicting-session-length-in-media-streaming
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Consistent On-Line Off-Policy Evaluation


Title	Consistent On-Line Off-Policy Evaluation
Authors	Assaf Hallak, Shie Mannor
Abstract	The problem of on-line off-policy evaluation (OPE) has been actively studied in the last decade due to its importance both as a stand-alone problem and as a module in a policy improvement scheme. However, most Temporal Difference (TD) based solutions ignore the discrepancy between the stationary distribution of the behavior and target policies and its effect on the convergence limit when function approximation is applied. In this paper we propose the Consistent Off-Policy Temporal Difference (COP-TD($\lambda$, $\beta$)) algorithm that addresses this issue and reduces this bias at some computational expense. We show that COP-TD($\lambda$, $\beta$) can be designed to converge to the same value that would have been obtained by using on-policy TD($\lambda$) with the target policy. Subsequently, the proposed scheme leads to a related and promising heuristic we call log-COP-TD($\lambda$, $\beta$). Both algorithms have favorable empirical results to the current state of the art on-line OPE algorithms. Finally, our formulation sheds some new light on the recently proposed Emphatic TD learning.
Tasks
Published	2017-02-23
URL	http://arxiv.org/abs/1702.07121v1
PDF	http://arxiv.org/pdf/1702.07121v1.pdf
PWC	https://paperswithcode.com/paper/consistent-on-line-off-policy-evaluation
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A Homological Theory of Functions


Title	A Homological Theory of Functions
Authors	Greg Yang
Abstract	In computational complexity, a complexity class is given by a set of problems or functions, and a basic challenge is to show separations of complexity classes $A \not= B$ especially when $A$ is known to be a subset of $B$. In this paper we introduce a homological theory of functions that can be used to establish complexity separations, while also providing other interesting consequences. We propose to associate a topological space $S_A$ to each class of functions $A$, such that, to separate complexity classes $A \subseteq B'$, it suffices to observe a change in “the number of holes”, i.e. homology, in $S_A$ as a subclass $B$ of $B'$ is added to $A$. In other words, if the homologies of $S_A$ and $S_{A \cup B}$ are different, then $A \not= B'$. We develop the underlying theory of functions based on combinatorial and homological commutative algebra and Stanley-Reisner theory, and recover Minsky and Papert’s 1969 result that parity cannot be computed by nonmaximal degree polynomial threshold functions. In the process, we derive a “maximal principle” for polynomial threshold functions that is used to extend this result further to arbitrary symmetric functions. A surprising coincidence is demonstrated, where the maximal dimension of “holes” in $S_A$ upper bounds the VC dimension of $A$, with equality for common computational cases such as the class of polynomial threshold functions or the class of linear functionals in $\mathbb F_2$, or common algebraic cases such as when the Stanley-Reisner ring of $S_A$ is Cohen-Macaulay. As another interesting application of our theory, we prove a result that a priori has nothing to do with complexity separation: it characterizes when a vector subspace intersects the positive cone, in terms of homological conditions. By analogy to Farkas’ result doing the same with linear conditions, we call our theorem the Homological Farkas Lemma.
Tasks
Published	2017-01-09
URL	http://arxiv.org/abs/1701.02302v3
PDF	http://arxiv.org/pdf/1701.02302v3.pdf
PWC	https://paperswithcode.com/paper/a-homological-theory-of-functions
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All the people around me: face discovery in egocentric photo-streams


Title	All the people around me: face discovery in egocentric photo-streams
Authors	Maedeh Aghaei, Mariella Dimiccoli, Petia Radeva
Abstract	Given an unconstrained stream of images captured by a wearable photo-camera (2fpm), we propose an unsupervised bottom-up approach for automatic clustering appearing faces into the individual identities present in these data. The problem is challenging since images are acquired under real world conditions; hence the visible appearance of the people in the images undergoes intensive variations. Our proposed pipeline consists of first arranging the photo-stream into events, later, localizing the appearance of multiple people in them, and finally, grouping various appearances of the same person across different events. Experimental results performed on a dataset acquired by wearing a photo-camera during one month, demonstrate the effectiveness of the proposed approach for the considered purpose.
Tasks
Published	2017-03-06
URL	http://arxiv.org/abs/1703.01790v2
PDF	http://arxiv.org/pdf/1703.01790v2.pdf
PWC	https://paperswithcode.com/paper/all-the-people-around-me-face-discovery-in
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Supervised Machine Learning for Signals Having RRC Shaped Pulses


Title	Supervised Machine Learning for Signals Having RRC Shaped Pulses
Authors	Mohammad Bari, Hussain Taher, Syed Saad Sherazi, Milos Doroslovacki
Abstract	Classification performances of the supervised machine learning techniques such as support vector machines, neural networks and logistic regression are compared for modulation recognition purposes. The simple and robust features are used to distinguish continuous-phase FSK from QAM-PSK signals. Signals having root-raised-cosine shaped pulses are simulated in extreme noisy conditions having joint impurities of block fading, lack of symbol and sampling synchronization, carrier offset, and additive white Gaussian noise. The features are based on sample mean and sample variance of the imaginary part of the product of two consecutive complex signal values.
Tasks
Published	2017-05-17
URL	http://arxiv.org/abs/1705.06299v1
PDF	http://arxiv.org/pdf/1705.06299v1.pdf
PWC	https://paperswithcode.com/paper/supervised-machine-learning-for-signals
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Adaptive Neighboring Selection Algorithm Based on Curvature Prediction in Manifold Learning


Title	Adaptive Neighboring Selection Algorithm Based on Curvature Prediction in Manifold Learning
Authors	Lin Ma, Caifa Zhou, Xi Liu, Yubin Xu
Abstract	Recently manifold learning algorithm for dimensionality reduction attracts more and more interests, and various linear and nonlinear, global and local algorithms are proposed. The key step of manifold learning algorithm is the neighboring region selection. However, so far for the references we know, few of which propose a generally accepted algorithm to well select the neighboring region. So in this paper, we propose an adaptive neighboring selection algorithm, which successfully applies the LLE and ISOMAP algorithms in the test. It is an algorithm that can find the optimal K nearest neighbors of the data points on the manifold. And the theoretical basis of the algorithm is the approximated curvature of the data point on the manifold. Based on Riemann Geometry, Jacob matrix is a proper mathematical concept to predict the approximated curvature. By verifying the proposed algorithm on embedding Swiss roll from R3 to R2 based on LLE and ISOMAP algorithm, the simulation results show that the proposed adaptive neighboring selection algorithm is feasible and able to find the optimal value of K, making the residual variance relatively small and better visualization of the results. By quantitative analysis, the embedding quality measured by residual variance is increased 45.45% after using the proposed algorithm in LLE.
Tasks	Dimensionality Reduction
Published	2017-04-13
URL	http://arxiv.org/abs/1704.04050v1
PDF	http://arxiv.org/pdf/1704.04050v1.pdf
PWC	https://paperswithcode.com/paper/adaptive-neighboring-selection-algorithm
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MIT-QCRI Arabic Dialect Identification System for the 2017 Multi-Genre Broadcast Challenge


Title	MIT-QCRI Arabic Dialect Identification System for the 2017 Multi-Genre Broadcast Challenge
Authors	Suwon Shon, Ahmed Ali, James Glass
Abstract	In order to successfully annotate the Arabic speech con- tent found in open-domain media broadcasts, it is essential to be able to process a diverse set of Arabic dialects. For the 2017 Multi-Genre Broadcast challenge (MGB-3) there were two possible tasks: Arabic speech recognition, and Arabic Dialect Identification (ADI). In this paper, we describe our efforts to create an ADI system for the MGB-3 challenge, with the goal of distinguishing amongst four major Arabic dialects, as well as Modern Standard Arabic. Our research fo- cused on dialect variability and domain mismatches between the training and test domain. In order to achieve a robust ADI system, we explored both Siamese neural network models to learn similarity and dissimilarities among Arabic dialects, as well as i-vector post-processing to adapt domain mismatches. Both Acoustic and linguistic features were used for the final MGB-3 submissions, with the best primary system achieving 75% accuracy on the official 10hr test set.
Tasks	Speech Recognition
Published	2017-08-28
URL	http://arxiv.org/abs/1709.00387v1
PDF	http://arxiv.org/pdf/1709.00387v1.pdf
PWC	https://paperswithcode.com/paper/mit-qcri-arabic-dialect-identification-system
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Comparative study on supervised learning methods for identifying phytoplankton species


Title	Comparative study on supervised learning methods for identifying phytoplankton species
Authors	Thi-Thu-Hong Phan, Emilie Poisson Caillault, André Bigand
Abstract	Phytoplankton plays an important role in marine ecosystem. It is defined as a biological factor to assess marine quality. The identification of phytoplankton species has a high potential for monitoring environmental, climate changes and for evaluating water quality. However, phytoplankton species identification is not an easy task owing to their variability and ambiguity due to thousands of micro and pico-plankton species. Therefore, the aim of this paper is to build a framework for identifying phytoplankton species and to perform a comparison on different features types and classifiers. We propose a new features type extracted from raw signals of phytoplankton species. We then analyze the performance of various classifiers on the proposed features type as well as two other features types for finding the robust one. Through experiments, it is found that Random Forest using the proposed features gives the best classification results with average accuracy up to 98.24%.
Tasks
Published	2017-01-23
URL	http://arxiv.org/abs/1701.06421v1
PDF	http://arxiv.org/pdf/1701.06421v1.pdf
PWC	https://paperswithcode.com/paper/comparative-study-on-supervised-learning
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Unfolding Hidden Barriers by Active Enhanced Sampling


Title	Unfolding Hidden Barriers by Active Enhanced Sampling
Authors	Jing Zhang, Ming Chen
Abstract	Collective variable (CV) or order parameter based enhanced sampling algorithms have achieved great success due to their ability to efficiently explore the rough potential energy landscapes of complex systems. However, the degeneracy of microscopic configurations, originating from the orthogonal space perpendicular to the CVs, is likely to shadow “hidden barriers” and greatly reduce the efficiency of CV-based sampling. Here we demonstrate that systematic machine learning CV, through enhanced sampling, can iteratively lift such degeneracies on the fly. We introduce an active learning scheme that consists of a parametric CV learner based on deep neural network and a CV-based enhanced sampler. Our active enhanced sampling (AES) algorithm is capable of identifying the least informative regions based on a historical sample, forming a positive feedback loop between the CV learner and sampler. This approach is able to globally preserve kinetic characteristics by incrementally enhancing both sample completeness and CV quality.
Tasks	Active Learning
Published	2017-05-21
URL	http://arxiv.org/abs/1705.07414v2
PDF	http://arxiv.org/pdf/1705.07414v2.pdf
PWC	https://paperswithcode.com/paper/unfolding-hidden-barriers-by-active-enhanced
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Unsupervised Feature Selection Based on Space Filling Concept


Title	Unsupervised Feature Selection Based on Space Filling Concept
Authors	Mohamed Laib, Mikhail Kanevski
Abstract	The paper deals with the adaptation of a new measure for the unsupervised feature selection problems. The proposed measure is based on space filling concept and is called the coverage measure. This measure was used for judging the quality of an experimental space filling design. In the present work, the coverage measure is adapted for selecting the smallest informative subset of variables by reducing redundancy in data. This paper proposes a simple analogy to apply this measure. It is implemented in a filter algorithm for unsupervised feature selection problems. The proposed filter algorithm is robust with high dimensional data and can be implemented without extra parameters. Further, it is tested with simulated data and real world case studies including environmental data and hyperspectral image. Finally, the results are evaluated by using random forest algorithm.
Tasks	Feature Selection
Published	2017-06-27
URL	http://arxiv.org/abs/1706.08894v1
PDF	http://arxiv.org/pdf/1706.08894v1.pdf
PWC	https://paperswithcode.com/paper/unsupervised-feature-selection-based-on-space
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Framework

A successive difference-of-convex approximation method for a class of nonconvex nonsmooth optimization problems


Title	A successive difference-of-convex approximation method for a class of nonconvex nonsmooth optimization problems
Authors	Tianxiang Liu, Ting Kei Pong, Akiko Takeda
Abstract	We consider a class of nonconvex nonsmooth optimization problems whose objective is the sum of a smooth function and a finite number of nonnegative proper closed possibly nonsmooth functions (whose proximal mappings are easy to compute), some of which are further composed with linear maps. This kind of problems arises naturally in various applications when different regularizers are introduced for inducing simultaneous structures in the solutions. Solving these problems, however, can be challenging because of the coupled nonsmooth functions: the corresponding proximal mapping can be hard to compute so that standard first-order methods such as the proximal gradient algorithm cannot be applied efficiently. In this paper, we propose a successive difference-of-convex approximation method for solving this kind of problems. In this algorithm, we approximate the nonsmooth functions by their Moreau envelopes in each iteration. Making use of the simple observation that Moreau envelopes of nonnegative proper closed functions are continuous {\em difference-of-convex} functions, we can then approximately minimize the approximation function by first-order methods with suitable majorization techniques. These first-order methods can be implemented efficiently thanks to the fact that the proximal mapping of {\em each} nonsmooth function is easy to compute. Under suitable assumptions, we prove that the sequence generated by our method is bounded and any accumulation point is a stationary point of the objective. We also discuss how our method can be applied to concrete applications such as nonconvex fused regularized optimization problems and simultaneously structured matrix optimization problems, and illustrate the performance numerically for these two specific applications.
Tasks
Published	2017-10-16
URL	http://arxiv.org/abs/1710.05778v2
PDF	http://arxiv.org/pdf/1710.05778v2.pdf
PWC	https://paperswithcode.com/paper/a-successive-difference-of-convex
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End-to-end Learning for Short Text Expansion


Title	End-to-end Learning for Short Text Expansion
Authors	Jian Tang, Yue Wang, Kai Zheng, Qiaozhu Mei
Abstract	Effectively making sense of short texts is a critical task for many real world applications such as search engines, social media services, and recommender systems. The task is particularly challenging as a short text contains very sparse information, often too sparse for a machine learning algorithm to pick up useful signals. A common practice for analyzing short text is to first expand it with external information, which is usually harvested from a large collection of longer texts. In literature, short text expansion has been done with all kinds of heuristics. We propose an end-to-end solution that automatically learns how to expand short text to optimize a given learning task. A novel deep memory network is proposed to automatically find relevant information from a collection of longer documents and reformulate the short text through a gating mechanism. Using short text classification as a demonstrating task, we show that the deep memory network significantly outperforms classical text expansion methods with comprehensive experiments on real world data sets.
Tasks	Recommendation Systems, Text Classification
Published	2017-08-30
URL	http://arxiv.org/abs/1709.00389v1
PDF	http://arxiv.org/pdf/1709.00389v1.pdf
PWC	https://paperswithcode.com/paper/end-to-end-learning-for-short-text-expansion
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Bridging the Gap between Probabilistic and Deterministic Models: A Simulation Study on a Variational Bayes Predictive Coding Recurrent Neural Network Model


Title	Bridging the Gap between Probabilistic and Deterministic Models: A Simulation Study on a Variational Bayes Predictive Coding Recurrent Neural Network Model
Authors	Ahmadreza Ahmadi, Jun Tani
Abstract	The current paper proposes a novel variational Bayes predictive coding RNN model, which can learn to generate fluctuated temporal patterns from exemplars. The model learns to maximize the lower bound of the weighted sum of the regularization and reconstruction error terms. We examined how this weighting can affect development of different types of information processing while learning fluctuated temporal patterns. Simulation results show that strong weighting of the reconstruction term causes the development of deterministic chaos for imitating the randomness observed in target sequences, while strong weighting of the regularization term causes the development of stochastic dynamics imitating probabilistic processes observed in targets. Moreover, results indicate that the most generalized learning emerges between these two extremes. The paper concludes with implications in terms of the underlying neuronal mechanisms for autism spectrum disorder and for free action.
Tasks
Published	2017-06-30
URL	http://arxiv.org/abs/1706.10240v2
PDF	http://arxiv.org/pdf/1706.10240v2.pdf
PWC	https://paperswithcode.com/paper/bridging-the-gap-between-probabilistic-and
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