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40,810 | Flying Insect Classification with Inexpensive Sensors | cs.LG | The ability to use inexpensive, noninvasive sensors to accurately classify
flying insects would have significant implications for entomological research,
and allow for the development of many useful applications in vector control for
both medical and agricultural entomology. Given this, the last sixty years have
seen m... | computer science |
40,811 | Balancing Sparsity and Rank Constraints in Quadratic Basis Pursuit | cs.NA | We investigate the methods that simultaneously enforce sparsity and low-rank
structure in a matrix as often employed for sparse phase retrieval problems or
phase calibration problems in compressive sensing. We propose a new approach
for analyzing the trade off between the sparsity and low rank constraints in
these appr... | computer science |
40,812 | Simultaneous Perturbation Algorithms for Batch Off-Policy Search | math.OC | We propose novel policy search algorithms in the context of off-policy, batch
mode reinforcement learning (RL) with continuous state and action spaces. Given
a batch collection of trajectories, we perform off-line policy evaluation using
an algorithm similar to that by [Fonteneau et al., 2010]. Using this
Monte-Carlo l... | computer science |
40,813 | Forecasting Popularity of Videos using Social Media | cs.LG | This paper presents a systematic online prediction method (Social-Forecast)
that is capable to accurately forecast the popularity of videos promoted by
social media. Social-Forecast explicitly considers the dynamically changing and
evolving propagation patterns of videos in social media when making popularity
forecasts... | computer science |
40,814 | AIS-INMACA: A Novel Integrated MACA Based Clonal Classifier for Protein
Coding and Promoter Region Prediction | cs.CE | Most of the problems in bioinformatics are now the challenges in computing.
This paper aims at building a classifier based on Multiple Attractor Cellular
Automata (MACA) which uses fuzzy logic. It is strengthened with an artificial
Immune System Technique (AIS), Clonal algorithm for identifying a protein
coding and pro... | computer science |
40,815 | DeepWalk: Online Learning of Social Representations | cs.SI | We present DeepWalk, a novel approach for learning latent representations of
vertices in a network. These latent representations encode social relations in
a continuous vector space, which is easily exploited by statistical models.
DeepWalk generalizes recent advancements in language modeling and unsupervised
feature l... | computer science |
40,816 | Comparison of Multi-agent and Single-agent Inverse Learning on a
Simulated Soccer Example | cs.LG | We compare the performance of Inverse Reinforcement Learning (IRL) with the
relative new model of Multi-agent Inverse Reinforcement Learning (MIRL). Before
comparing the methods, we extend a published Bayesian IRL approach that is only
applicable to the case where the reward is only state dependent to a general
one cap... | computer science |
40,817 | Relevant Feature Selection Model Using Data Mining for Intrusion
Detection System | cs.CR | Network intrusions have become a significant threat in recent years as a
result of the increased demand of computer networks for critical systems.
Intrusion detection system (IDS) has been widely deployed as a defense measure
for computer networks. Features extracted from network traffic can be used as
sign to detect a... | computer science |
40,818 | Multi-label Ferns for Efficient Recognition of Musical Instruments in
Recordings | cs.LG | In this paper we introduce multi-label ferns, and apply this technique for
automatic classification of musical instruments in audio recordings. We compare
the performance of our proposed method to a set of binary random ferns, using
jazz recordings as input data. Our main result is obtaining much faster
classification ... | computer science |
40,819 | Privacy Tradeoffs in Predictive Analytics | cs.CR | Online services routinely mine user data to predict user preferences, make
recommendations, and place targeted ads. Recent research has demonstrated that
several private user attributes (such as political affiliation, sexual
orientation, and gender) can be inferred from such data. Can a
privacy-conscious user benefit f... | computer science |
40,820 | Complexity of Equivalence and Learning for Multiplicity Tree Automata | cs.LG | We consider the complexity of equivalence and learning for multiplicity tree
automata, i.e., weighted tree automata over a field. We first show that the
equivalence problem is logspace equivalent to polynomial identity testing, the
complexity of which is a longstanding open problem. Secondly, we derive lower
bounds on ... | computer science |
40,821 | Application of Machine Learning Techniques in Aquaculture | cs.CE | In this paper we present applications of different machine learning
algorithms in aquaculture. Machine learning algorithms learn models from
historical data. In aquaculture historical data are obtained from farm
practices, yields, and environmental data sources. Associations between these
different variables can be obt... | computer science |
40,822 | Learning with many experts: model selection and sparsity | stat.ME | Experts classifying data are often imprecise. Recently, several models have
been proposed to train classifiers using the noisy labels generated by these
experts. How to choose between these models? In such situations, the true
labels are unavailable. Thus, one cannot perform model selection using the
standard versions ... | computer science |
40,823 | Efficient classification using parallel and scalable compressed model
and Its application on intrusion detection | cs.LG | In order to achieve high efficiency of classification in intrusion detection,
a compressed model is proposed in this paper which combines horizontal
compression with vertical compression. OneR is utilized as horizontal
com-pression for attribute reduction, and affinity propagation is employed as
vertical compression to... | computer science |
40,824 | Machine Learning in Wireless Sensor Networks: Algorithms, Strategies,
and Applications | cs.NI | Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary rede... | computer science |
40,825 | Predicting Online Video Engagement Using Clickstreams | cs.LG | In the nascent days of e-content delivery, having a superior product was
enough to give companies an edge against the competition. With today's fiercely
competitive market, one needs to be multiple steps ahead, especially when it
comes to understanding consumers. Focusing on a large set of web portals owned
and managed... | computer science |
40,826 | Fast Distributed Coordinate Descent for Non-Strongly Convex Losses | math.OC | We propose an efficient distributed randomized coordinate descent method for
minimizing regularized non-strongly convex loss functions. The method attains
the optimal $O(1/k^2)$ convergence rate, where $k$ is the iteration counter.
The core of the work is the theoretical study of stepsize parameters. We have
implemente... | computer science |
40,827 | Node Classification in Uncertain Graphs | cs.DB | In many real applications that use and analyze networked data, the links in
the network graph may be erroneous, or derived from probabilistic techniques.
In such cases, the node classification problem can be challenging, since the
unreliability of the links may affect the final results of the classification
process. If... | computer science |
40,828 | Coupled Item-based Matrix Factorization | cs.LG | The essence of the challenges cold start and sparsity in Recommender Systems
(RS) is that the extant techniques, such as Collaborative Filtering (CF) and
Matrix Factorization (MF), mainly rely on the user-item rating matrix, which
sometimes is not informative enough for predicting recommendations. To solve
these challe... | computer science |
40,829 | Automatic large-scale classification of bird sounds is strongly improved
by unsupervised feature learning | cs.SD | Automatic species classification of birds from their sound is a computational
tool of increasing importance in ecology, conservation monitoring and vocal
communication studies. To make classification useful in practice, it is crucial
to improve its accuracy while ensuring that it can run at big data scales. Many
approa... | computer science |
40,830 | Learning Nash Equilibria in Congestion Games | cs.LG | We study the repeated congestion game, in which multiple populations of
players share resources, and make, at each iteration, a decentralized decision
on which resources to utilize. We investigate the following question: given a
model of how individual players update their strategies, does the resulting
dynamics of str... | computer science |
40,831 | A RobustICA Based Algorithm for Blind Separation of Convolutive Mixtures | cs.LG | We propose a frequency domain method based on robust independent component
analysis (RICA) to address the multichannel Blind Source Separation (BSS)
problem of convolutive speech mixtures in highly reverberant environments. We
impose regularization processes to tackle the ill-conditioning problem of the
covariance matr... | computer science |
40,832 | GraphLab: A New Framework For Parallel Machine Learning | cs.LG | Designing and implementing efficient, provably correct parallel machine
learning (ML) algorithms is challenging. Existing high-level parallel
abstractions like MapReduce are insufficiently expressive while low-level tools
like MPI and Pthreads leave ML experts repeatedly solving the same design
challenges. By targeting... | computer science |
40,833 | R-UCB: a Contextual Bandit Algorithm for Risk-Aware Recommender Systems | cs.IR | Mobile Context-Aware Recommender Systems can be naturally modelled as an
exploration/exploitation trade-off (exr/exp) problem, where the system has to
choose between maximizing its expected rewards dealing with its current
knowledge (exploitation) and learning more about the unknown user's preferences
to improve its kn... | computer science |
40,834 | Likely to stop? Predicting Stopout in Massive Open Online Courses | cs.CY | Understanding why students stopout will help in understanding how students
learn in MOOCs. In this report, part of a 3 unit compendium, we describe how we
build accurate predictive models of MOOC student stopout. We document a
scalable, stopout prediction methodology, end to end, from raw source data to
model analysis.... | computer science |
40,835 | Inverse Reinforcement Learning with Multi-Relational Chains for
Robot-Centered Smart Home | cs.RO | In a robot-centered smart home, the robot observes the home states with its
own sensors, and then it can change certain object states according to an
operator's commands for remote operations, or imitate the operator's behaviors
in the house for autonomous operations. To model the robot's imitation of the
operator's be... | computer science |
40,836 | Down-Sampling coupled to Elastic Kernel Machines for Efficient
Recognition of Isolated Gestures | cs.LG | In the field of gestural action recognition, many studies have focused on
dimensionality reduction along the spatial axis, to reduce both the variability
of gestural sequences expressed in the reduced space, and the computational
complexity of their processing. It is noticeable that very few of these methods
have expli... | computer science |
40,837 | Computing Multi-Relational Sufficient Statistics for Large Databases | cs.LG | Databases contain information about which relationships do and do not hold
among entities. To make this information accessible for statistical analysis
requires computing sufficient statistics that combine information from
different database tables. Such statistics may involve any number of {\em
positive and negative} ... | computer science |
40,838 | Recursive Total Least-Squares Algorithm Based on Inverse Power Method
and Dichotomous Coordinate-Descent Iterations | cs.SY | We develop a recursive total least-squares (RTLS) algorithm for
errors-in-variables system identification utilizing the inverse power method
and the dichotomous coordinate-descent (DCD) iterations. The proposed
algorithm, called DCD-RTLS, outperforms the previously-proposed RTLS
algorithms, which are based on the line-... | computer science |
40,839 | A Random Matrix Theoretical Approach to Early Event Detection in Smart
Grid | stat.ME | Power systems are developing very fast nowadays, both in size and in
complexity; this situation is a challenge for Early Event Detection (EED). This
paper proposes a data- driven unsupervised learning method to handle this
challenge. Specifically, the random matrix theories (RMTs) are introduced as
the statistical foun... | computer science |
40,840 | Twitter Hash Tag Recommendation | cs.IR | The rise in popularity of microblogging services like Twitter has led to
increased use of content annotation strategies like the hashtag. Hashtags
provide users with a tagging mechanism to help organize, group, and create
visibility for their posts. This is a simple idea but can be challenging for
the user in practice ... | computer science |
40,841 | Personalized Web Search | cs.IR | Personalization is important for search engines to improve user experience.
Most of the existing work do pure feature engineering and extract a lot of
session-style features and then train a ranking model. Here we proposed a novel
way to model both long term and short term user behavior using Multi-armed
bandit algorit... | computer science |
40,842 | A Simple Expression for Mill's Ratio of the Student's $t$-Distribution | cs.LG | I show a simple expression of the Mill's ratio of the Student's
t-Distribution. I use it to prove Conjecture 1 in P. Auer, N. Cesa-Bianchi, and
P. Fischer. Finite-time analysis of the multiarmed bandit problem. Mach.
Learn., 47(2-3):235--256, May 2002. | computer science |
40,843 | Arrhythmia Detection using Mutual Information-Based Integration Method | cs.CE | The aim of this paper is to propose an application of mutual
information-based ensemble methods to the analysis and classification of heart
beats associated with different types of Arrhythmia. Models of multilayer
perceptrons, support vector machines, and radial basis function neural networks
were trained and tested us... | computer science |
40,844 | Equilibrated adaptive learning rates for non-convex optimization | cs.LG | Parameter-specific adaptive learning rate methods are computationally
efficient ways to reduce the ill-conditioning problems encountered when
training large deep networks. Following recent work that strongly suggests that
most of the critical points encountered when training such networks are saddle
points, we find how... | computer science |
40,845 | On Sex, Evolution, and the Multiplicative Weights Update Algorithm | cs.LG | We consider a recent innovative theory by Chastain et al. on the role of sex
in evolution [PNAS'14]. In short, the theory suggests that the evolutionary
process of gene recombination implements the celebrated multiplicative weights
updates algorithm (MWUA). They prove that the population dynamics induced by
sexual repr... | computer science |
40,846 | Dengue disease prediction using weka data mining tool | cs.CY | Dengue is a life threatening disease prevalent in several developed as well
as developing countries like India.In this paper we discuss various algorithm
approaches of data mining that have been utilized for dengue disease
prediction. Data mining is a well known technique used by health organizations
for classification... | computer science |
40,847 | Optimizing Text Quantifiers for Multivariate Loss Functions | cs.LG | We address the problem of \emph{quantification}, a supervised learning task
whose goal is, given a class, to estimate the relative frequency (or
\emph{prevalence}) of the class in a dataset of unlabelled items.
Quantification has several applications in data and text mining, such as
estimating the prevalence of positiv... | computer science |
40,848 | NeuroSVM: A Graphical User Interface for Identification of Liver
Patients | cs.LG | Diagnosis of liver infection at preliminary stage is important for better
treatment. In todays scenario devices like sensors are used for detection of
infections. Accurate classification techniques are required for automatic
identification of disease samples. In this context, this study utilizes data
mining approaches ... | computer science |
40,849 | Adaptive system optimization using random directions stochastic
approximation | math.OC | We present novel algorithms for simulation optimization using random
directions stochastic approximation (RDSA). These include first-order
(gradient) as well as second-order (Newton) schemes. We incorporate both
continuous-valued as well as discrete-valued perturbations into both our
algorithms. The former are chosen t... | computer science |
40,850 | Scale-Free Algorithms for Online Linear Optimization | cs.LG | We design algorithms for online linear optimization that have optimal regret
and at the same time do not need to know any upper or lower bounds on the norm
of the loss vectors. We achieve adaptiveness to norms of loss vectors by scale
invariance, i.e., our algorithms make exactly the same decisions if the
sequence of l... | computer science |
40,852 | A provably convergent alternating minimization method for mean field
inference | cs.LG | Mean-Field is an efficient way to approximate a posterior distribution in
complex graphical models and constitutes the most popular class of Bayesian
variational approximation methods. In most applications, the mean field
distribution parameters are computed using an alternate coordinate
minimization. However, the conv... | computer science |
40,853 | A Data Mining framework to model Consumer Indebtedness with
Psychological Factors | cs.LG | Modelling Consumer Indebtedness has proven to be a problem of complex nature.
In this work we utilise Data Mining techniques and methods to explore the
multifaceted aspect of Consumer Indebtedness by examining the contribution of
Psychological Factors, like Impulsivity to the analysis of Consumer Debt. Our
results conf... | computer science |
40,854 | The fundamental nature of the log loss function | cs.LG | The standard loss functions used in the literature on probabilistic
prediction are the log loss function, the Brier loss function, and the
spherical loss function; however, any computable proper loss function can be
used for comparison of prediction algorithms. This note shows that the log loss
function is most selecti... | computer science |
40,855 | Bandit Convex Optimization: sqrt{T} Regret in One Dimension | cs.LG | We analyze the minimax regret of the adversarial bandit convex optimization
problem. Focusing on the one-dimensional case, we prove that the minimax regret
is $\widetilde\Theta(\sqrt{T})$ and partially resolve a decade-old open
problem. Our analysis is non-constructive, as we do not present a concrete
algorithm that at... | computer science |
40,856 | Efficient Geometric-based Computation of the String Subsequence Kernel | cs.LG | Kernel methods are powerful tools in machine learning. They have to be
computationally efficient. In this paper, we present a novel Geometric-based
approach to compute efficiently the string subsequence kernel (SSK). Our main
idea is that the SSK computation reduces to range query problem. We started by
the constructio... | computer science |
40,857 | An Online Convex Optimization Approach to Blackwell's Approachability | cs.GT | The notion of approachability in repeated games with vector payoffs was
introduced by Blackwell in the 1950s, along with geometric conditions for
approachability and corresponding strategies that rely on computing {\em
steering directions} as projections from the current average payoff vector to
the (convex) target set... | computer science |
40,858 | Personalising Mobile Advertising Based on Users Installed Apps | cs.CY | Mobile advertising is a billion pound industry that is rapidly expanding. The
success of an advert is measured based on how users interact with it. In this
paper we investigate whether the application of unsupervised learning and
association rule mining could be used to enable personalised targeting of
mobile adverts w... | computer science |
40,859 | Normalization based K means Clustering Algorithm | cs.LG | K-means is an effective clustering technique used to separate similar data
into groups based on initial centroids of clusters. In this paper,
Normalization based K-means clustering algorithm(N-K means) is proposed.
Proposed N-K means clustering algorithm applies normalization prior to
clustering on the available data a... | computer science |
40,860 | Scalable Iterative Algorithm for Robust Subspace Clustering | cs.DS | Subspace clustering (SC) is a popular method for dimensionality reduction of
high-dimensional data, where it generalizes Principal Component Analysis (PCA).
Recently, several methods have been proposed to enhance the robustness of PCA
and SC, while most of them are computationally very expensive, in particular,
for hig... | computer science |
40,861 | Financial Market Prediction | cs.CE | Given financial data from popular sites like Yahoo and the London Exchange,
the presented paper attempts to model and predict stocks that can be considered
"good investments". Stocks are characterized by 125 features ranging from gross
domestic product to EDIBTA, and are labeled by discrepancies between stock and
marke... | computer science |
40,862 | Scalable Nuclear-norm Minimization by Subspace Pursuit Proximal
Riemannian Gradient | cs.LG | Nuclear-norm regularization plays a vital role in many learning tasks, such
as low-rank matrix recovery (MR), and low-rank representation (LRR). Solving
this problem directly can be computationally expensive due to the unknown rank
of variables or large-rank singular value decompositions (SVDs). To address
this, we pro... | computer science |
40,863 | Efficient Learning of Linear Separators under Bounded Noise | cs.LG | We study the learnability of linear separators in $\Re^d$ in the presence of
bounded (a.k.a Massart) noise. This is a realistic generalization of the random
classification noise model, where the adversary can flip each example $x$ with
probability $\eta(x) \leq \eta$. We provide the first polynomial time algorithm
that... | computer science |
40,864 | On Graduated Optimization for Stochastic Non-Convex Problems | cs.LG | The graduated optimization approach, also known as the continuation method,
is a popular heuristic to solving non-convex problems that has received renewed
interest over the last decade. Despite its popularity, very little is known in
terms of theoretical convergence analysis. In this paper we describe a new
first-orde... | computer science |
40,865 | More General Queries and Less Generalization Error in Adaptive Data
Analysis | cs.LG | Adaptivity is an important feature of data analysis---typically the choice of
questions asked about a dataset depends on previous interactions with the same
dataset. However, generalization error is typically bounded in a non-adaptive
model, where all questions are specified before the dataset is drawn. Recent
work by ... | computer science |
40,866 | Energy Sharing for Multiple Sensor Nodes with Finite Buffers | cs.NI | We consider the problem of finding optimal energy sharing policies that
maximize the network performance of a system comprising of multiple sensor
nodes and a single energy harvesting (EH) source. Sensor nodes periodically
sense the random field and generate data, which is stored in the corresponding
data queues. The E... | computer science |
40,867 | Rank Subspace Learning for Compact Hash Codes | cs.LG | The era of Big Data has spawned unprecedented interests in developing hashing
algorithms for efficient storage and fast nearest neighbor search. Most
existing work learn hash functions that are numeric quantizations of feature
values in projected feature space. In this work, we propose a novel hash
learning framework t... | computer science |
40,868 | Costing Generated Runtime Execution Plans for Large-Scale Machine
Learning Programs | cs.DC | Declarative large-scale machine learning (ML) aims at the specification of ML
algorithms in a high-level language and automatic generation of hybrid runtime
execution plans ranging from single node, in-memory computations to distributed
computations on MapReduce (MR) or similar frameworks like Spark. The
compilation of... | computer science |
40,869 | On Lower and Upper Bounds for Smooth and Strongly Convex Optimization
Problems | math.OC | We develop a novel framework to study smooth and strongly convex optimization
algorithms, both deterministic and stochastic. Focusing on quadratic functions
we are able to examine optimization algorithms as a recursive application of
linear operators. This, in turn, reveals a powerful connection between a class
of opti... | computer science |
40,870 | Analysis of Spectrum Occupancy Using Machine Learning Algorithms | cs.NI | In this paper, we analyze the spectrum occupancy using different machine
learning techniques. Both supervised techniques (naive Bayesian classifier
(NBC), decision trees (DT), support vector machine (SVM), linear regression
(LR)) and unsupervised algorithm (hidden markov model (HMM)) are studied to
find the best techni... | computer science |
40,871 | RankMap: A Platform-Aware Framework for Distributed Learning from Dense
Datasets | cs.DC | This paper introduces RankMap, a platform-aware end-to-end framework for
efficient execution of a broad class of iterative learning algorithms for
massive and dense datasets. Our framework exploits data structure to factorize
it into an ensemble of lower rank subspaces. The factorization creates sparse
low-dimensional ... | computer science |
40,872 | Average Distance Queries through Weighted Samples in Graphs and Metric
Spaces: High Scalability with Tight Statistical Guarantees | cs.SI | The average distance from a node to all other nodes in a graph, or from a
query point in a metric space to a set of points, is a fundamental quantity in
data analysis. The inverse of the average distance, known as the (classic)
closeness centrality of a node, is a popular importance measure in the study of
social netwo... | computer science |
40,873 | Sparse plus low-rank autoregressive identification in neuroimaging time
series | cs.LG | This paper considers the problem of identifying multivariate autoregressive
(AR) sparse plus low-rank graphical models. Based on the corresponding problem
formulation recently presented, we use the alternating direction method of
multipliers (ADMM) to efficiently solve it and scale it to sizes encountered in
neuroimagi... | computer science |
40,874 | Comparison of Bayesian predictive methods for model selection | stat.ME | The goal of this paper is to compare several widely used Bayesian model
selection methods in practical model selection problems, highlight their
differences and give recommendations about the preferred approaches. We focus
on the variable subset selection for regression and classification and perform
several numerical ... | computer science |
40,875 | Founding Digital Currency on Imprecise Commodity | cs.CY | Current digital currency schemes provide instantaneous exchange on precise
commodity, in which "precise" means a buyer can possibly verify the function of
the commodity without error. However, imprecise commodities, e.g. statistical
data, with error existing are abundant in digital world. Existing digital
currency sche... | computer science |
40,876 | Learning Definite Horn Formulas from Closure Queries | cs.LG | A definite Horn theory is a set of n-dimensional Boolean vectors whose
characteristic function is expressible as a definite Horn formula, that is, as
conjunction of definite Horn clauses. The class of definite Horn theories is
known to be learnable under different query learning settings, such as learning
from membersh... | computer science |
40,877 | Extracting Implicit Social Relation for Social Recommendation Techniques
in User Rating Prediction | cs.SI | Recommendation plays an increasingly important role in our daily lives.
Recommender systems automatically suggest items to users that might be
interesting for them. Recent studies illustrate that incorporating social trust
in Matrix Factorization methods demonstrably improves accuracy of rating
prediction. Such approac... | computer science |
40,878 | Understanding and Optimizing the Performance of Distributed Machine
Learning Applications on Apache Spark | cs.DC | In this paper we explore the performance limits of Apache Spark for machine
learning applications. We begin by analyzing the characteristics of a
state-of-the-art distributed machine learning algorithm implemented in Spark
and compare it to an equivalent reference implementation using the high
performance computing fra... | computer science |
40,879 | Bridging Medical Data Inference to Achilles Tendon Rupture
Rehabilitation | cs.LG | Imputing incomplete medical tests and predicting patient outcomes are crucial
for guiding the decision making for therapy, such as after an Achilles Tendon
Rupture (ATR). We formulate the problem of data imputation and prediction for
ATR relevant medical measurements into a recommender system framework. By
applying Mat... | computer science |
40,880 | Interactive Prior Elicitation of Feature Similarities for Small Sample
Size Prediction | cs.LG | Regression under the "small $n$, large $p$" conditions, of small sample size
$n$ and large number of features $p$ in the learning data set, is a recurring
setting in which learning from data is difficult. With prior knowledge about
relationships of the features, $p$ can effectively be reduced, but explicating
such prio... | computer science |
40,881 | Clipper: A Low-Latency Online Prediction Serving System | cs.DC | Machine learning is being deployed in a growing number of applications which
demand real-time, accurate, and robust predictions under heavy query load.
However, most machine learning frameworks and systems only address model
training and not deployment.
In this paper, we introduce Clipper, a general-purpose low-laten... | computer science |
40,882 | An Empirical Study of ADMM for Nonconvex Problems | math.OC | The alternating direction method of multipliers (ADMM) is a common
optimization tool for solving constrained and non-differentiable problems. We
provide an empirical study of the practical performance of ADMM on several
nonconvex applications, including l0 regularized linear regression, l0
regularized image denoising, ... | computer science |
40,883 | Joint Bayesian Gaussian discriminant analysis for speaker verification | cs.SD | State-of-the-art i-vector based speaker verification relies on variants of
Probabilistic Linear Discriminant Analysis (PLDA) for discriminant analysis. We
are mainly motivated by the recent work of the joint Bayesian (JB) method,
which is originally proposed for discriminant analysis in face verification. We
apply JB t... | computer science |
40,884 | Corporate Disruption in the Science of Machine Learning | cs.CY | This MSc dissertation considers the effects of the current corporate interest
on researchers in the field of machine learning. Situated within the field's
cyclical history of academic, public and corporate interest, this dissertation
investigates how current researchers view recent developments and negotiate
their own ... | computer science |
40,885 | TF.Learn: TensorFlow's High-level Module for Distributed Machine
Learning | cs.DC | TF.Learn is a high-level Python module for distributed machine learning
inside TensorFlow. It provides an easy-to-use Scikit-learn style interface to
simplify the process of creating, configuring, training, evaluating, and
experimenting a machine learning model. TF.Learn integrates a wide range of
state-of-art machine ... | computer science |
40,886 | Identification of Cancer Patient Subgroups via Smoothed Shortest Path
Graph Kernel | cs.CE | Characterizing patient somatic mutations through next-generation sequencing
technologies opens up possibilities for refining cancer subtypes. However,
catalogues of mutations reveal that only a small fraction of the genes are
altered frequently in patients. On the other hand different genomic alterations
may perturb th... | computer science |
40,887 | Feature Learning for Chord Recognition: The Deep Chroma Extractor | cs.SD | We explore frame-level audio feature learning for chord recognition using
artificial neural networks. We present the argument that chroma vectors
potentially hold enough information to model harmonic content of audio for
chord recognition, but that standard chroma extractors compute too noisy
features. This leads us to... | computer science |
40,888 | A Fully Convolutional Deep Auditory Model for Musical Chord Recognition | cs.LG | Chord recognition systems depend on robust feature extraction pipelines.
While these pipelines are traditionally hand-crafted, recent advances in
end-to-end machine learning have begun to inspire researchers to explore
data-driven methods for such tasks. In this paper, we present a chord
recognition system that uses a ... | computer science |
40,889 | On the Potential of Simple Framewise Approaches to Piano Transcription | cs.SD | In an attempt at exploring the limitations of simple approaches to the task
of piano transcription (as usually defined in MIR), we conduct an in-depth
analysis of neural network-based framewise transcription. We systematically
compare different popular input representations for transcription systems to
determine the on... | computer science |
40,890 | Neural networks based EEG-Speech Models | cs.SD | In this paper, we propose an end-to-end neural network (NN) based EEG-speech
(NES) modeling framework, in which three network structures are developed to
map imagined EEG signals to phonemes. The proposed NES models incorporate a
language model based EEG feature extraction layer, an acoustic feature mapping
layer, and ... | computer science |
40,891 | VAST : The Virtual Acoustic Space Traveler Dataset | cs.SD | This paper introduces a new paradigm for sound source lo-calization referred
to as virtual acoustic space traveling (VAST) and presents a first dataset
designed for this purpose. Existing sound source localization methods are
either based on an approximate physical model (physics-driven) or on a
specific-purpose calibr... | computer science |
40,892 | Challenging Personalized Video Recommendation | cs.IR | The online videos are generated at an unprecedented speed in recent years. As
a result, how to generate personalized recommendation from the large volume of
videos becomes more and more challenging. In this paper, we propose to extract
the non-textual contents from the videos themselves to enhance the personalized
vide... | computer science |
40,893 | Classification and Learning-to-rank Approaches for Cross-Device Matching
at CIKM Cup 2016 | cs.IR | In this paper, we propose two methods for tackling the problem of
cross-device matching for online advertising at CIKM Cup 2016. The first method
considers the matching problem as a binary classification task and solve it by
utilizing ensemble learning techniques. The second method defines the matching
problem as a ran... | computer science |
40,894 | Distributed Dictionary Learning | math.OC | The paper studies distributed Dictionary Learning (DL) problems where the
learning task is distributed over a multi-agent network with time-varying
(nonsymmetric) connectivity. This formulation is relevant, for instance, in
big-data scenarios where massive amounts of data are collected/stored in
different spatial locat... | computer science |
40,895 | On Coreset Constructions for the Fuzzy $K$-Means Problem | cs.LG | The fuzzy $K$-means problem is a generalization of the $K$-means problem to
soft clusterings. Although popular in practice, the first
$(1+\epsilon)$-approximation algorithms for this problem have been proposed
only recently. In this paper, we pursue the analysis of the fuzzy $K$-means
problem further by making use of c... | computer science |
40,896 | Logic-based Clustering and Learning for Time-Series Data | cs.LG | To effectively analyze and design cyberphysical systems (CPS), designers
today have to combat the data deluge problem, i.e., the burden of processing
intractably large amounts of data produced by complex models and experiments.
In this work, we utilize monotonic Parametric Signal Temporal Logic (PSTL) to
design feature... | computer science |
40,897 | ASAP: Asynchronous Approximate Data-Parallel Computation | cs.DC | Emerging workloads, such as graph processing and machine learning are
approximate because of the scale of data involved and the stochastic nature of
the underlying algorithms. These algorithms are often distributed over multiple
machines using bulk-synchronous processing (BSP) or other synchronous
processing paradigms ... | computer science |
40,898 | Sequence-to-point learning with neural networks for nonintrusive load
monitoring | stat.AP | Energy disaggregation (a.k.a nonintrusive load monitoring, NILM), a
single-channel blind source separation problem, aims to decompose the mains
which records the whole house electricity consumption into appliance-wise
readings. This problem is difficult because it is inherently unidentifiable.
Recent approaches have sh... | computer science |
40,899 | The on-line shortest path problem under partial monitoring | cs.LG | The on-line shortest path problem is considered under various models of
partial monitoring. Given a weighted directed acyclic graph whose edge weights
can change in an arbitrary (adversarial) way, a decision maker has to choose in
each round of a game a path between two distinguished vertices such that the
loss of the ... | computer science |
40,900 | A Note on the Inapproximability of Correlation Clustering | cs.LG | We consider inapproximability of the correlation clustering problem defined
as follows: Given a graph $G = (V,E)$ where each edge is labeled either "+"
(similar) or "-" (dissimilar), correlation clustering seeks to partition the
vertices into clusters so that the number of pairs correctly (resp.
incorrectly) classified... | computer science |
40,901 | A Tutorial on Spectral Clustering | cs.DS | In recent years, spectral clustering has become one of the most popular
modern clustering algorithms. It is simple to implement, can be solved
efficiently by standard linear algebra software, and very often outperforms
traditional clustering algorithms such as the k-means algorithm. On the first
glance spectral cluster... | computer science |
40,902 | Faster Rates for training Max-Margin Markov Networks | cs.LG | Structured output prediction is an important machine learning problem both in
theory and practice, and the max-margin Markov network (\mcn) is an effective
approach. All state-of-the-art algorithms for optimizing \mcn\ objectives take
at least $O(1/\epsilon)$ number of iterations to find an $\epsilon$ accurate
solution... | computer science |
40,903 | Evaluation of E-Learners Behaviour using Different Fuzzy Clustering
Models: A Comparative Study | cs.CY | This paper introduces an evaluation methodologies for the e-learners'
behaviour that will be a feedback to the decision makers in e-learning system.
Learner's profile plays a crucial role in the evaluation process to improve the
e-learning process performance. The work focuses on the clustering of the
e-learners based ... | computer science |
40,904 | A Survey of Naïve Bayes Machine Learning approach in Text Document
Classification | cs.LG | Text Document classification aims in associating one or more predefined
categories based on the likelihood suggested by the training set of labeled
documents. Many machine learning algorithms play a vital role in training the
system with predefined categories among which Na\"ive Bayes has some intriguing
facts that it ... | computer science |
40,905 | Near-Optimal Evasion of Convex-Inducing Classifiers | cs.LG | Classifiers are often used to detect miscreant activities. We study how an
adversary can efficiently query a classifier to elicit information that allows
the adversary to evade detection at near-minimal cost. We generalize results of
Lowd and Meek (2005) to convex-inducing classifiers. We present algorithms that
constr... | computer science |
40,906 | Unbeatable Imitation | cs.GT | We show that for many classes of symmetric two-player games, the simple
decision rule "imitate-the-best" can hardly be beaten by any other decision
rule. We provide necessary and sufficient conditions for imitation to be
unbeatable and show that it can only be beaten by much in games that are of the
rock-scissors-paper... | computer science |
40,907 | Spoken Language Identification Using Hybrid Feature Extraction Methods | cs.SD | This paper introduces and motivates the use of hybrid robust feature
extraction technique for spoken language identification (LID) system. The
speech recognizers use a parametric form of a signal to get the most important
distinguishable features of speech signal for recognition task. In this paper
Mel-frequency cepstr... | computer science |
40,908 | Wavelet-Based Mel-Frequency Cepstral Coefficients for Speaker
Identification using Hidden Markov Models | cs.SD | To improve the performance of speaker identification systems, an effective
and robust method is proposed to extract speech features, capable of operating
in noisy environment. Based on the time-frequency multi-resolution property of
wavelet transform, the input speech signal is decomposed into various frequency
channel... | computer science |
40,909 | Additive Non-negative Matrix Factorization for Missing Data | cs.NA | Non-negative matrix factorization (NMF) has previously been shown to be a
useful decomposition for multivariate data. We interpret the factorization in a
new way and use it to generate missing attributes from test data. We provide a
joint optimization scheme for the missing attributes as well as the NMF
factors. We pro... | computer science |
40,910 | Online Algorithms for the Multi-Armed Bandit Problem with Markovian
Rewards | math.OC | We consider the classical multi-armed bandit problem with Markovian rewards.
When played an arm changes its state in a Markovian fashion while it remains
frozen when not played. The player receives a state-dependent reward each time
it plays an arm. The number of states and the state transition probabilities of
an arm ... | computer science |
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