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32,802 | AMP: a new time-frequency feature extraction method for intermittent
time-series data | cs.LG | The characterisation of time-series data via their most salient features is
extremely important in a range of machine learning task, not least of all with
regards to classification and clustering. While there exist many feature
extraction techniques suitable for non-intermittent time-series data, these
approaches are n... | computer science |
32,803 | Bandit-Based Task Assignment for Heterogeneous Crowdsourcing | cs.LG | We consider a task assignment problem in crowdsourcing, which is aimed at
collecting as many reliable labels as possible within a limited budget. A
challenge in this scenario is how to cope with the diversity of tasks and the
task-dependent reliability of workers, e.g., a worker may be good at
recognizing the name of s... | computer science |
32,804 | A study of the classification of low-dimensional data with supervised
manifold learning | cs.LG | Supervised manifold learning methods learn data representations by preserving
the geometric structure of data while enhancing the separation between data
samples from different classes. In this work, we propose a theoretical study of
supervised manifold learning for classification. We consider nonlinear
dimensionality ... | computer science |
32,805 | Deep Recurrent Q-Learning for Partially Observable MDPs | cs.LG | Deep Reinforcement Learning has yielded proficient controllers for complex
tasks. However, these controllers have limited memory and rely on being able to
perceive the complete game screen at each decision point. To address these
shortcomings, this article investigates the effects of adding recurrency to a
Deep Q-Netwo... | computer science |
32,806 | A Reinforcement Learning Approach to Online Learning of Decision Trees | cs.LG | Online decision tree learning algorithms typically examine all features of a
new data point to update model parameters. We propose a novel alternative,
Reinforcement Learning- based Decision Trees (RLDT), that uses Reinforcement
Learning (RL) to actively examine a minimal number of features of a data point
to classify ... | computer science |
32,807 | A Framework of Sparse Online Learning and Its Applications | cs.LG | The amount of data in our society has been exploding in the era of big data
today. In this paper, we address several open challenges of big data stream
classification, including high volume, high velocity, high dimensionality, high
sparsity, and high class-imbalance. Many existing studies in data mining
literature solv... | computer science |
32,808 | True Online Emphatic TD($λ$): Quick Reference and Implementation
Guide | cs.LG | This document is a guide to the implementation of true online emphatic
TD($\lambda$), a model-free temporal-difference algorithm for learning to make
long-term predictions which combines the emphasis idea (Sutton, Mahmood & White
2015) and the true-online idea (van Seijen & Sutton 2014). The setting used
here includes ... | computer science |
32,809 | Task Selection for Bandit-Based Task Assignment in Heterogeneous
Crowdsourcing | cs.LG | Task selection (picking an appropriate labeling task) and worker selection
(assigning the labeling task to a suitable worker) are two major challenges in
task assignment for crowdsourcing. Recently, worker selection has been
successfully addressed by the bandit-based task assignment (BBTA) method, while
task selection ... | computer science |
32,810 | Sparse Multidimensional Patient Modeling using Auxiliary Confidence
Labels | cs.LG | In this work, we focus on the problem of learning a classification model that
performs inference on patient Electronic Health Records (EHRs). Often, a large
amount of costly expert supervision is required to learn such a model. To
reduce this cost, we obtain confidence labels that indicate how sure an expert
is in the ... | computer science |
32,811 | Learning Representations for Outlier Detection on a Budget | cs.LG | The problem of detecting a small number of outliers in a large dataset is an
important task in many fields from fraud detection to high-energy physics. Two
approaches have emerged to tackle this problem: unsupervised and supervised.
Supervised approaches require a sufficient amount of labeled data and are
challenged by... | computer science |
32,812 | VMF-SNE: Embedding for Spherical Data | cs.LG | T-SNE is a well-known approach to embedding high-dimensional data and has
been widely used in data visualization. The basic assumption of t-SNE is that
the data are non-constrained in the Euclidean space and the local proximity can
be modelled by Gaussian distributions. This assumption does not hold for a wide
range of... | computer science |
32,813 | Turnover Prediction Of Shares using Data Mining techniques : A Case
Study | cs.LG | Predicting the turnover of a company in the ever fluctuating Stock market has
always proved to be a precarious situation and most certainly a difficult task
in hand. Data mining is a well-known sphere of Computer Science that aims on
extracting meaningful information from large databases. However, despite the
existence... | computer science |
32,814 | An Analytic Framework for Maritime Situation Analysis | cs.LG | Maritime domain awareness is critical for protecting sea lanes, ports,
harbors, offshore structures and critical infrastructures against common
threats and illegal activities. Limited surveillance resources constrain
maritime domain awareness and compromise full security coverage at all times.
This situation calls for ... | computer science |
32,815 | Fixed-point algorithms for learning determinantal point processes | cs.LG | Determinantal point processes (DPPs) offer an elegant tool for encoding
probabilities over subsets of a ground set. Discrete DPPs are parametrized by a
positive semidefinite matrix (called the DPP kernel), and estimating this
kernel is key to learning DPPs from observed data. We consider the task of
learning the DPP ke... | computer science |
32,816 | Dropout Training for SVMs with Data Augmentation | cs.LG | Dropout and other feature noising schemes have shown promising results in
controlling over-fitting by artificially corrupting the training data. Though
extensive theoretical and empirical studies have been performed for generalized
linear models, little work has been done for support vector machines (SVMs),
one of the ... | computer science |
32,817 | Training Conditional Random Fields with Natural Gradient Descent | cs.LG | We propose a novel parameter estimation procedure that works efficiently for
conditional random fields (CRF). This algorithm is an extension to the maximum
likelihood estimation (MLE), using loss functions defined by Bregman
divergences which measure the proximity between the model expectation and the
empirical mean of... | computer science |
32,818 | Normalized Hierarchical SVM | cs.LG | We present improved methods of using structured SVMs in a large-scale
hierarchical classification problem, that is when labels are leaves, or sets of
leaves, in a tree or a DAG. We examine the need to normalize both the
regularization and the margin and show how doing so significantly improves
performance, including al... | computer science |
32,819 | From Cutting Planes Algorithms to Compression Schemes and Active
Learning | cs.LG | Cutting-plane methods are well-studied localization(and optimization)
algorithms. We show that they provide a natural framework to perform
machinelearning ---and not just to solve optimization problems posed by
machinelearning--- in addition to their intended optimization use. In
particular, theyallow one to learn spar... | computer science |
32,820 | Probabilistic Dependency Networks for Prediction and Diagnostics | cs.LG | Research in transportation frequently involve modelling and predicting
attributes of events that occur at regular intervals. The event could be
arrival of a bus at a bus stop, the volume of a traffic at a particular point,
the demand at a particular bus stop etc. In this work, we propose a specific
implementation of pr... | computer science |
32,821 | Hash Function Learning via Codewords | cs.LG | In this paper we introduce a novel hash learning framework that has two main
distinguishing features, when compared to past approaches. First, it utilizes
codewords in the Hamming space as ancillary means to accomplish its hash
learning task. These codewords, which are inferred from the data, attempt to
capture similar... | computer science |
32,822 | A Survey on Contextual Multi-armed Bandits | cs.LG | In this survey we cover a few stochastic and adversarial contextual bandit
algorithms. We analyze each algorithm's assumption and regret bound. | computer science |
32,823 | Multi-Task Learning with Group-Specific Feature Space Sharing | cs.LG | When faced with learning a set of inter-related tasks from a limited amount
of usable data, learning each task independently may lead to poor
generalization performance. Multi-Task Learning (MTL) exploits the latent
relations between tasks and overcomes data scarcity limitations by co-learning
all these tasks simultane... | computer science |
32,824 | End-to-end Learning of LDA by Mirror-Descent Back Propagation over a
Deep Architecture | cs.LG | We develop a fully discriminative learning approach for supervised Latent
Dirichlet Allocation (LDA) model using Back Propagation (i.e., BP-sLDA), which
maximizes the posterior probability of the prediction variable given the input
document. Different from traditional variational learning or Gibbs sampling
approaches, ... | computer science |
32,825 | Predicting Grades | cs.LG | To increase efficacy in traditional classroom courses as well as in Massive
Open Online Courses (MOOCs), automated systems supporting the instructor are
needed. One important problem is to automatically detect students that are
going to do poorly in a course early enough to be able to take remedial
actions. Existing gr... | computer science |
32,826 | ESDF: Ensemble Selection using Diversity and Frequency | cs.LG | Recently ensemble selection for consensus clustering has emerged as a
research problem in Machine Intelligence. Normally consensus clustering
algorithms take into account the entire ensemble of clustering, where there is
a tendency of generating a very large size ensemble before computing its
consensus. One can avoid c... | computer science |
32,827 | Learning to Predict Independent of Span | cs.LG | We consider how to learn multi-step predictions efficiently. Conventional
algorithms wait until observing actual outcomes before performing the
computations to update their predictions. If predictions are made at a high
rate or span over a large amount of time, substantial computation can be
required to store all relev... | computer science |
32,828 | Fault Diagnosis of Helical Gear Box using Large Margin K-Nearest
Neighbors Classifier using Sound Signals | cs.LG | Gear drives are one of the most widely used transmission system in many
machinery. Sound signals of a rotating machine contain the dynamic information
about its health conditions. Not much information available in the literature
reporting suitability of sound signals for fault diagnosis applications.
Maximum numbers of... | computer science |
32,829 | Dither is Better than Dropout for Regularising Deep Neural Networks | cs.LG | Regularisation of deep neural networks (DNN) during training is critical to
performance. By far the most popular method is known as dropout. Here, cast
through the prism of signal processing theory, we compare and contrast the
regularisation effects of dropout with those of dither. We illustrate some
serious inherent l... | computer science |
32,830 | Semi-supervised Learning with Regularized Laplacian | cs.LG | We study a semi-supervised learning method based on the similarity graph and
RegularizedLaplacian. We give convenient optimization formulation of the
Regularized Laplacian method and establishits various properties. In
particular, we show that the kernel of the methodcan be interpreted in terms of
discrete and continuo... | computer science |
32,831 | Greedy methods, randomization approaches and multi-arm bandit algorithms
for efficient sparsity-constrained optimization | cs.LG | Several sparsity-constrained algorithms such as Orthogonal Matching Pursuit
or the Frank-Wolfe algorithm with sparsity constraints work by iteratively
selecting a novel atom to add to the current non-zero set of variables. This
selection step is usually performed by computing the gradient and then by
looking for the gr... | computer science |
32,832 | Online Anomaly Detection via Class-Imbalance Learning | cs.LG | Anomaly detection is an important task in many real world applications such
as fraud detection, suspicious activity detection, health care monitoring etc.
In this paper, we tackle this problem from supervised learning perspective in
online learning setting. We maximize well known \emph{Gmean} metric for
class-imbalance... | computer science |
32,833 | Multi-armed Bandit Problem with Known Trend | cs.LG | We consider a variant of the multi-armed bandit model, which we call
multi-armed bandit problem with known trend, where the gambler knows the shape
of the reward function of each arm but not its distribution. This new problem
is motivated by different online problems like active learning, music and
interface recommenda... | computer science |
32,834 | Competitive and Penalized Clustering Auto-encoder | cs.LG | The paper has been withdrawn since more effective experiments should be
completed.
Auto-encoders (AE) has been widely applied in different fields of machine
learning. However, as a deep model, there are a large amount of learnable
parameters in the AE, which would cause over-fitting and slow learning speed in
practic... | computer science |
32,835 | Differentially Private Online Learning for Cloud-Based Video
Recommendation with Multimedia Big Data in Social Networks | cs.LG | With the rapid growth in multimedia services and the enormous offers of video
contents in online social networks, users have difficulty in obtaining their
interests. Therefore, various personalized recommendation systems have been
proposed. However, they ignore that the accelerated proliferation of social
media data ha... | computer science |
32,836 | Sensor-Type Classification in Buildings | cs.LG | Many sensors/meters are deployed in commercial buildings to monitor and
optimize their performance. However, because sensor metadata is inconsistent
across buildings, software-based solutions are tightly coupled to the sensor
metadata conventions (i.e. schemas and naming) for each building. Running the
same software ac... | computer science |
32,837 | On-the-Fly Learning in a Perpetual Learning Machine | cs.LG | Despite the promise of brain-inspired machine learning, deep neural networks
(DNN) have frustratingly failed to bridge the deceptively large gap between
learning and memory. Here, we introduce a Perpetual Learning Machine; a new
type of DNN that is capable of brain-like dynamic 'on the fly' learning because
it exists i... | computer science |
32,838 | Training a Restricted Boltzmann Machine for Classification by Labeling
Model Samples | cs.LG | We propose an alternative method for training a classification model. Using
the MNIST set of handwritten digits and Restricted Boltzmann Machines, it is
possible to reach a classification performance competitive to semi-supervised
learning if we first train a model in an unsupervised fashion on unlabeled data
only, and... | computer science |
32,839 | A tree-based kernel for graphs with continuous attributes | cs.LG | The availability of graph data with node attributes that can be either
discrete or real-valued is constantly increasing. While existing kernel methods
are effective techniques for dealing with graphs having discrete node labels,
their adaptation to non-discrete or continuous node attributes has been
limited, mainly for... | computer science |
32,840 | Machine Learning Methods to Analyze Arabidopsis Thaliana Plant Root
Growth | cs.LG | One of the challenging problems in biology is to classify plants based on
their reaction on genetic mutation. Arabidopsis Thaliana is a plant that is so
interesting, because its genetic structure has some similarities with that of
human beings. Biologists classify the type of this plant to mutated and not
mutated (wild... | computer science |
32,841 | Probabilistic Neural Network Training for Semi-Supervised Classifiers | cs.LG | In this paper, we propose another version of help-training approach by
employing a Probabilistic Neural Network (PNN) that improves the performance of
the main discriminative classifier in the semi-supervised strategy. We
introduce the PNN-training algorithm and use it for training the support vector
machine (SVM) with... | computer science |
32,842 | Deep Broad Learning - Big Models for Big Data | cs.LG | Deep learning has demonstrated the power of detailed modeling of complex
high-order (multivariate) interactions in data. For some learning tasks there
is power in learning models that are not only Deep but also Broad. By Broad, we
mean models that incorporate evidence from large numbers of features. This is
of especial... | computer science |
32,843 | Parallel and Distributed Approaches for Graph Based Semi-supervised
Learning | cs.LG | Two approaches for graph based semi-supervised learning are proposed. The
firstapproach is based on iteration of an affine map. A key element of the
affine map iteration is sparsematrix-vector multiplication, which has several
very efficient parallel implementations. The secondapproach belongs to the
class of Markov Ch... | computer science |
32,844 | Efficient Sampling for k-Determinantal Point Processes | cs.LG | Determinantal Point Processes (DPPs) are elegant probabilistic models of
repulsion and diversity over discrete sets of items. But their applicability to
large sets is hindered by expensive cubic-complexity matrix operations for
basic tasks such as sampling. In light of this, we propose a new method for
approximate samp... | computer science |
32,845 | Gravitational Clustering | cs.LG | The downfall of many supervised learning algorithms, such as neural networks,
is the inherent need for a large amount of training data. Although there is a
lot of buzz about big data, there is still the problem of doing classification
from a small dataset. Other methods such as support vector machines, although
capable... | computer science |
32,846 | Theoretic Analysis and Extremely Easy Algorithms for Domain Adaptive
Feature Learning | cs.LG | Domain adaptation problems arise in a variety of applications, where a
training dataset from the \textit{source} domain and a test dataset from the
\textit{target} domain typically follow different distributions. The primary
difficulty in designing effective learning models to solve such problems lies
in how to bridge ... | computer science |
32,847 | Use it or Lose it: Selective Memory and Forgetting in a Perpetual
Learning Machine | cs.LG | In a recent article we described a new type of deep neural network - a
Perpetual Learning Machine (PLM) - which is capable of learning 'on the fly'
like a brain by existing in a state of Perpetual Stochastic Gradient Descent
(PSGD). Here, by simulating the process of practice, we demonstrate both
selective memory and s... | computer science |
32,848 | A new Initial Centroid finding Method based on Dissimilarity Tree for
K-means Algorithm | cs.LG | Cluster analysis is one of the primary data analysis technique in data mining
and K-means is one of the commonly used partitioning clustering algorithm. In
K-means algorithm, resulting set of clusters depend on the choice of initial
centroids. If we can find initial centroids which are coherent with the
arrangement of ... | computer science |
32,849 | Toward better feature weighting algorithms: a focus on Relief | cs.LG | Feature weighting algorithms try to solve a problem of great importance
nowadays in machine learning: The search of a relevance measure for the
features of a given domain. This relevance is primarily used for feature
selection as feature weighting can be seen as a generalization of it, but it is
also useful to better u... | computer science |
32,850 | Voted Kernel Regularization | cs.LG | This paper presents an algorithm, Voted Kernel Regularization , that provides
the flexibility of using potentially very complex kernel functions such as
predictors based on much higher-degree polynomial kernels, while benefitting
from strong learning guarantees. The success of our algorithm arises from
derived bounds t... | computer science |
32,851 | Towards Making High Dimensional Distance Metric Learning Practical | cs.LG | In this work, we study distance metric learning (DML) for high dimensional
data. A typical approach for DML with high dimensional data is to perform the
dimensionality reduction first before learning the distance metric. The main
shortcoming of this approach is that it may result in a suboptimal solution due
to the sub... | computer science |
32,852 | Forecasting Method for Grouped Time Series with the Use of k-Means
Algorithm | cs.LG | The paper is focused on the forecasting method for time series groups with
the use of algorithms for cluster analysis. $K$-means algorithm is suggested to
be a basic one for clustering. The coordinates of the centers of clusters have
been put in correspondence with summarizing time series data the centroids of
the clus... | computer science |
32,853 | Taming the ReLU with Parallel Dither in a Deep Neural Network | cs.LG | Rectified Linear Units (ReLU) seem to have displaced traditional 'smooth'
nonlinearities as activation-function-du-jour in many - but not all - deep
neural network (DNN) applications. However, nobody seems to know why. In this
article, we argue that ReLU are useful because they are ideal demodulators -
this helps them ... | computer science |
32,854 | Learning to Hash for Indexing Big Data - A Survey | cs.LG | The explosive growth in big data has attracted much attention in designing
efficient indexing and search methods recently. In many critical applications
such as large-scale search and pattern matching, finding the nearest neighbors
to a query is a fundamental research problem. However, the straightforward
solution usin... | computer science |
32,855 | "Oddball SGD": Novelty Driven Stochastic Gradient Descent for Training
Deep Neural Networks | cs.LG | Stochastic Gradient Descent (SGD) is arguably the most popular of the machine
learning methods applied to training deep neural networks (DNN) today. It has
recently been demonstrated that SGD can be statistically biased so that certain
elements of the training set are learned more rapidly than others. In this
article, ... | computer science |
32,856 | The Utility of Clustering in Prediction Tasks | cs.LG | We explore the utility of clustering in reducing error in various prediction
tasks. Previous work has hinted at the improvement in prediction accuracy
attributed to clustering algorithms if used to pre-process the data. In this
work we more deeply investigate the direct utility of using clustering to
improve prediction... | computer science |
32,857 | Deep Reinforcement Learning with Double Q-learning | cs.LG | The popular Q-learning algorithm is known to overestimate action values under
certain conditions. It was not previously known whether, in practice, such
overestimations are common, whether they harm performance, and whether they can
generally be prevented. In this paper, we answer all these questions
affirmatively. In ... | computer science |
32,858 | Learning Wake-Sleep Recurrent Attention Models | cs.LG | Despite their success, convolutional neural networks are computationally
expensive because they must examine all image locations. Stochastic
attention-based models have been shown to improve computational efficiency at
test time, but they remain difficult to train because of intractable posterior
inference and high var... | computer science |
32,859 | On The Direct Maximization of Quadratic Weighted Kappa | cs.LG | In recent years, quadratic weighted kappa has been growing in popularity in
the machine learning community as an evaluation metric in domains where the
target labels to be predicted are drawn from integer ratings, usually obtained
from human experts. For example, it was the metric of choice in several recent,
high prof... | computer science |
32,860 | Sparsity-based Correction of Exponential Artifacts | cs.LG | This paper describes an exponential transient excision algorithm (ETEA). In
biomedical time series analysis, e.g., in vivo neural recording and
electrocorticography (ECoG), some measurement artifacts take the form of
piecewise exponential transients. The proposed method is formulated as an
unconstrained convex optimiza... | computer science |
32,861 | Spatially Encoding Temporal Correlations to Classify Temporal Data Using
Convolutional Neural Networks | cs.LG | We propose an off-line approach to explicitly encode temporal patterns
spatially as different types of images, namely, Gramian Angular Fields and
Markov Transition Fields. This enables the use of techniques from computer
vision for feature learning and classification. We used Tiled Convolutional
Neural Networks to lear... | computer science |
32,862 | Online Stochastic Linear Optimization under One-bit Feedback | cs.LG | In this paper, we study a special bandit setting of online stochastic linear
optimization, where only one-bit of information is revealed to the learner at
each round. This problem has found many applications including online
advertisement and online recommendation. We assume the binary feedback is a
random variable gen... | computer science |
32,863 | A Mathematical Theory for Clustering in Metric Spaces | cs.LG | Clustering is one of the most fundamental problems in data analysis and it
has been studied extensively in the literature. Though many clustering
algorithms have been proposed, clustering theories that justify the use of
these clustering algorithms are still unsatisfactory. In particular, one of the
fundamental challen... | computer science |
32,864 | Algorithms for Linear Bandits on Polyhedral Sets | cs.LG | We study stochastic linear optimization problem with bandit feedback. The set
of arms take values in an $N$-dimensional space and belong to a bounded
polyhedron described by finitely many linear inequalities. We provide a lower
bound for the expected regret that scales as $\Omega(N\log T)$. We then provide
a nearly opt... | computer science |
32,865 | Super-Resolution Off the Grid | cs.LG | Super-resolution is the problem of recovering a superposition of point
sources using bandlimited measurements, which may be corrupted with noise. This
signal processing problem arises in numerous imaging problems, ranging from
astronomy to biology to spectroscopy, where it is common to take (coarse)
Fourier measurement... | computer science |
32,866 | Discriminative Learning of the Prototype Set for Nearest Neighbor
Classification | cs.LG | The nearest neighbor rule is a classic yet essential classification model,
particularly in problems where the supervising information is given by pairwise
dissimilarities and the embedding function are not easily obtained. Prototype
selection provides means of generalization and improving efficiency of the
nearest neig... | computer science |
32,867 | Feature Selection for classification of hyperspectral data by minimizing
a tight bound on the VC dimension | cs.LG | Hyperspectral data consists of large number of features which require
sophisticated analysis to be extracted. A popular approach to reduce
computational cost, facilitate information representation and accelerate
knowledge discovery is to eliminate bands that do not improve the
classification and analysis methods being ... | computer science |
32,868 | How to Formulate and Solve Statistical Recognition and Learning Problems | cs.LG | We formulate problems of statistical recognition and learning in a common
framework of complex hypothesis testing. Based on arguments from multi-criteria
optimization, we identify strategies that are improper for solving these
problems and derive a common form of the remaining strategies. We show that
some widely used ... | computer science |
32,869 | A Semi-Supervised Method for Predicting Cancer Survival Using Incomplete
Clinical Data | cs.LG | Prediction of survival for cancer patients is an open area of research.
However, many of these studies focus on datasets with a large number of
patients. We present a novel method that is specifically designed to address
the challenge of data scarcity, which is often the case for cancer datasets.
Our method is able to ... | computer science |
32,870 | Distributed Weighted Parameter Averaging for SVM Training on Big Data | cs.LG | Two popular approaches for distributed training of SVMs on big data are
parameter averaging and ADMM. Parameter averaging is efficient but suffers from
loss of accuracy with increase in number of partitions, while ADMM in the
feature space is accurate but suffers from slow convergence. In this paper, we
report a hybrid... | computer science |
32,871 | Deep Haar Scattering Networks | cs.LG | An orthogonal Haar scattering transform is a deep network, computed with a
hierarchy of additions, subtractions and absolute values, over pairs of
coefficients. It provides a simple mathematical model for unsupervised deep
network learning. It implements non-linear contractions, which are optimized
for classification, ... | computer science |
32,872 | Rapidly Mixing Gibbs Sampling for a Class of Factor Graphs Using
Hierarchy Width | cs.LG | Gibbs sampling on factor graphs is a widely used inference technique, which
often produces good empirical results. Theoretical guarantees for its
performance are weak: even for tree structured graphs, the mixing time of Gibbs
may be exponential in the number of variables. To help understand the behavior
of Gibbs sampli... | computer science |
32,873 | Machine Learning for Machine Data from a CATI Network | cs.LG | This is a machine learning application paper involving big data. We present
high-accuracy prediction methods of rare events in semi-structured machine log
files, which are produced at high velocity and high volume by NORC's
computer-assisted telephone interviewing (CATI) network for conducting surveys.
We judiciously a... | computer science |
32,874 | Tight Variational Bounds via Random Projections and I-Projections | cs.LG | Information projections are the key building block of variational inference
algorithms and are used to approximate a target probabilistic model by
projecting it onto a family of tractable distributions. In general, there is no
guarantee on the quality of the approximation obtained. To overcome this issue,
we introduce ... | computer science |
32,875 | Stochastic Optimization for Deep CCA via Nonlinear Orthogonal Iterations | cs.LG | Deep CCA is a recently proposed deep neural network extension to the
traditional canonical correlation analysis (CCA), and has been successful for
multi-view representation learning in several domains. However, stochastic
optimization of the deep CCA objective is not straightforward, because it does
not decouple over t... | computer science |
32,876 | Uniform Learning in a Deep Neural Network via "Oddball" Stochastic
Gradient Descent | cs.LG | When training deep neural networks, it is typically assumed that the training
examples are uniformly difficult to learn. Or, to restate, it is assumed that
the training error will be uniformly distributed across the training examples.
Based on these assumptions, each training example is used an equal number of
times. H... | computer science |
32,877 | Technical Report of Participation in Higgs Boson Machine Learning
Challenge | cs.LG | This report entails the detailed description of the approach and
methodologies taken as part of competing in the Higgs Boson Machine Learning
Competition hosted by Kaggle Inc. and organized by CERN et al. It briefly
describes the theoretical background of the problem and the motivation for
taking part in the competitio... | computer science |
32,878 | Early Inference in Energy-Based Models Approximates Back-Propagation | cs.LG | We show that Langevin MCMC inference in an energy-based model with latent
variables has the property that the early steps of inference, starting from a
stationary point, correspond to propagating error gradients into internal
layers, similarly to back-propagation. The error that is back-propagated is
with respect to vi... | computer science |
32,879 | TSEB: More Efficient Thompson Sampling for Policy Learning | cs.LG | In model-based solution approaches to the problem of learning in an unknown
environment, exploring to learn the model parameters takes a toll on the
regret. The optimal performance with respect to regret or PAC bounds is
achievable, if the algorithm exploits with respect to reward or explores with
respect to the model ... | computer science |
32,880 | Survey on Feature Selection | cs.LG | Feature selection plays an important role in the data mining process. It is
needed to deal with the excessive number of features, which can become a
computational burden on the learning algorithms. It is also necessary, even
when computational resources are not scarce, since it improves the accuracy of
the machine lear... | computer science |
32,881 | On Correcting Inputs: Inverse Optimization for Online Structured
Prediction | cs.LG | Algorithm designers typically assume that the input data is correct, and then
proceed to find "optimal" or "sub-optimal" solutions using this input data.
However this assumption of correct data does not always hold in practice,
especially in the context of online learning systems where the objective is to
learn appropr... | computer science |
32,882 | $\ell_1$-regularized Neural Networks are Improperly Learnable in
Polynomial Time | cs.LG | We study the improper learning of multi-layer neural networks. Suppose that
the neural network to be learned has $k$ hidden layers and that the
$\ell_1$-norm of the incoming weights of any neuron is bounded by $L$. We
present a kernel-based method, such that with probability at least $1 -
\delta$, it learns a predictor... | computer science |
32,883 | Elastic regularization in restricted Boltzmann machines: Dealing with
$p\gg N$ | cs.LG | Restricted Boltzmann machines (RBMs) are endowed with the universal power of
modeling (binary) joint distributions. Meanwhile, as a result of their
confining network structure, training RBMs confronts less difficulties
(compared with more complicated models, e.g., Boltzmann machines) when dealing
with approximation and... | computer science |
32,884 | Online Markov decision processes with policy iteration | cs.LG | The online Markov decision process (MDP) is a generalization of the classical
Markov decision process that incorporates changing reward functions. In this
paper, we propose practical online MDP algorithms with policy iteration and
theoretically establish a sublinear regret bound. A notable advantage of the
proposed alg... | computer science |
32,885 | Quantification in-the-wild: data-sets and baselines | cs.LG | Quantification is the task of estimating the class-distribution of a
data-set. While typically considered as a parameter estimation problem with
strict assumptions on the data-set shift, we consider quantification
in-the-wild, on two large scale data-sets from marine ecology: a survey of
Caribbean coral reefs, and a pl... | computer science |
32,886 | Improving the Speed of Response of Learning Algorithms Using Multiple
Models | cs.LG | This is the first of a series of papers that the authors propose to write on
the subject of improving the speed of response of learning systems using
multiple models. During the past two decades, the first author has worked on
numerous methods for improving the stability, robustness, and performance of
adaptive systems... | computer science |
32,887 | How Important is Weight Symmetry in Backpropagation? | cs.LG | Gradient backpropagation (BP) requires symmetric feedforward and feedback
connections -- the same weights must be used for forward and backward passes.
This "weight transport problem" (Grossberg 1987) is thought to be one of the
main reasons to doubt BP's biologically plausibility. Using 15 different
classification dat... | computer science |
32,888 | AdaCluster : Adaptive Clustering for Heterogeneous Data | cs.LG | Clustering algorithms start with a fixed divergence, which captures the
possibly asymmetric distance between a sample and a centroid. In the mixture
model setting, the sample distribution plays the same role. When all attributes
have the same topology and dispersion, the data are said to be homogeneous. If
the prior kn... | computer science |
32,889 | Application of Machine Learning Techniques in Human Activity Recognition | cs.LG | Human activity detection has seen a tremendous growth in the last decade
playing a major role in the field of pervasive computing. This emerging
popularity can be attributed to its myriad of real-life applications primarily
dealing with human-centric problems like healthcare and elder care. Many
research attempts with ... | computer science |
32,890 | Transductive Optimization of Top k Precision | cs.LG | Consider a binary classification problem in which the learner is given a
labeled training set, an unlabeled test set, and is restricted to choosing
exactly $k$ test points to output as positive predictions. Problems of this
kind---{\it transductive precision@$k$}---arise in information retrieval,
digital advertising, a... | computer science |
32,891 | Fast and Scalable Structural SVM with Slack Rescaling | cs.LG | We present an efficient method for training slack-rescaled structural SVM.
Although finding the most violating label in a margin-rescaled formulation is
often easy since the target function decomposes with respect to the structure,
this is not the case for a slack-rescaled formulation, and finding the most
violated lab... | computer science |
32,892 | Robust Semi-Supervised Classification for Multi-Relational Graphs | cs.LG | Graph-regularized semi-supervised learning has been used effectively for
classification when (i) instances are connected through a graph, and (ii)
labeled data is scarce. If available, using multiple relations (or graphs)
between the instances can improve the prediction performance. On the other
hand, when these relati... | computer science |
32,893 | A Framework for Distributed Deep Learning Layer Design in Python | cs.LG | In this paper, a framework for testing Deep Neural Network (DNN) design in
Python is presented. First, big data, machine learning (ML), and Artificial
Neural Networks (ANNs) are discussed to familiarize the reader with the
importance of such a system. Next, the benefits and detriments of implementing
such a system in P... | computer science |
32,894 | Phenotyping of Clinical Time Series with LSTM Recurrent Neural Networks | cs.LG | We present a novel application of LSTM recurrent neural networks to
multilabel classification of diagnoses given variable-length time series of
clinical measurements. Our method outperforms a strong baseline on a variety of
metrics. | computer science |
32,895 | The Singular Value Decomposition, Applications and Beyond | cs.LG | The singular value decomposition (SVD) is not only a classical theory in
matrix computation and analysis, but also is a powerful tool in machine
learning and modern data analysis. In this tutorial we first study the basic
notion of SVD and then show the central role of SVD in matrices. Using
majorization theory, we con... | computer science |
32,896 | RATM: Recurrent Attentive Tracking Model | cs.LG | We present an attention-based modular neural framework for computer vision.
The framework uses a soft attention mechanism allowing models to be trained
with gradient descent. It consists of three modules: a recurrent attention
module controlling where to look in an image or video frame, a
feature-extraction module prov... | computer science |
32,897 | How good is good enough? Re-evaluating the bar for energy disaggregation | cs.LG | Since the early 1980s, the research community has developed ever more
sophisticated algorithms for the problem of energy disaggregation, but despite
decades of research, there is still a dearth of applications with demonstrated
value. In this work, we explore a question that is highly pertinent to this
research communi... | computer science |
32,898 | Testing Visual Attention in Dynamic Environments | cs.LG | We investigate attention as the active pursuit of useful information. This
contrasts with attention as a mechanism for the attenuation of irrelevant
information. We also consider the role of short-term memory, whose use is
critical to any model incapable of simultaneously perceiving all information on
which its output ... | computer science |
32,899 | The Pareto Regret Frontier for Bandits | cs.LG | Given a multi-armed bandit problem it may be desirable to achieve a
smaller-than-usual worst-case regret for some special actions. I show that the
price for such unbalanced worst-case regret guarantees is rather high.
Specifically, if an algorithm enjoys a worst-case regret of B with respect to
some action, then there ... | computer science |
32,900 | Large-scale probabilistic predictors with and without guarantees of
validity | cs.LG | This paper studies theoretically and empirically a method of turning
machine-learning algorithms into probabilistic predictors that automatically
enjoys a property of validity (perfect calibration) and is computationally
efficient. The price to pay for perfect calibration is that these probabilistic
predictors produce ... | computer science |
32,901 | Fast Collaborative Filtering from Implicit Feedback with Provable
Guarantees | cs.LG | Building recommendation algorithms is one of the most challenging tasks in
Machine Learning. Although most of the recommendation systems are built on
explicit feedback available from the users in terms of rating or text, a
majority of the applications do not receive such feedback. Here we consider the
recommendation ta... | computer science |
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