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12,601 | Representation Learning and Recovery in the ReLU Model | stat.ML | Rectified linear units, or ReLUs, have become the preferred activation
function for artificial neural networks. In this paper we consider two basic
learning problems assuming that the underlying data follow a generative model
based on a ReLU-network -- a neural network with ReLU activations. As a
primarily theoretical ... | computer science |
12,602 | Optimal Rates of Sketched-regularized Algorithms for Least-Squares
Regression over Hilbert Spaces | stat.ML | We investigate regularized algorithms combining with projection for
least-squares regression problem over a Hilbert space, covering nonparametric
regression over a reproducing kernel Hilbert space. We prove convergence
results with respect to variants of norms, under a capacity assumption on the
hypothesis space and a ... | computer science |
12,603 | Bridge type classification: supervised learning on a modified NBI
dataset | stat.ML | A key phase in the bridge design process is the selection of the structural
system. Due to budget and time constraints, engineers typically rely on
engineering judgment and prior experience when selecting a structural system,
often considering a limited range of design alternatives. The objective of this
study was to e... | computer science |
12,604 | Gradient Augmented Information Retrieval with Autoencoders and Semantic
Hashing | cs.IR | This paper will explore the use of autoencoders for semantic hashing in the
context of Information Retrieval. This paper will summarize how to efficiently
train an autoencoder in order to create meaningful and low-dimensional
encodings of data. This paper will demonstrate how computing and storing the
closest encodings... | computer science |
12,605 | Automated software vulnerability detection with machine learning | cs.SE | Thousands of security vulnerabilities are discovered in production software
each year, either reported publicly to the Common Vulnerabilities and Exposures
database or discovered internally in proprietary code. Vulnerabilities often
manifest themselves in subtle ways that are not obvious to code reviewers or
the develo... | computer science |
12,606 | Compact Convolutional Neural Networks for Classification of Asynchronous
Steady-state Visual Evoked Potentials | cs.LG | Steady-State Visual Evoked Potentials (SSVEPs) are neural oscillations from
the parietal and occipital regions of the brain that are evoked from flickering
visual stimuli. SSVEPs are robust signals measurable in the
electroencephalogram (EEG) and are commonly used in brain-computer interfaces
(BCIs). However, methods f... | computer science |
12,607 | Hybrid Forecasting of Chaotic Processes: Using Machine Learning in
Conjunction with a Knowledge-Based Model | cs.LG | A model-based approach to forecasting chaotic dynamical systems utilizes
knowledge of the physical processes governing the dynamics to build an
approximate mathematical model of the system. In contrast, machine learning
techniques have demonstrated promising results for forecasting chaotic systems
purely from past time... | computer science |
12,608 | Low-Rank Boolean Matrix Approximation by Integer Programming | cs.LG | Low-rank approximations of data matrices are an important dimensionality
reduction tool in machine learning and regression analysis. We consider the
case of categorical variables, where it can be formulated as the problem of
finding low-rank approximations to Boolean matrices. In this paper we give what
is to the best ... | computer science |
12,609 | Multiplicative Updates for Elastic Net Regularized Convolutional NMF
Under $β$-Divergence | cs.LG | We generalize the convolutional NMF by taking the $\beta$-divergence as the
loss function, add a regularizer for sparsity in the form of an elastic net,
and provide multiplicative update rules for its factors in closed form. The new
update rules embed the $\beta$-NMF, the standard convolutional NMF, and sparse
coding a... | computer science |
12,610 | Redundancy Techniques for Straggler Mitigation in Distributed
Optimization and Learning | stat.ML | Performance of distributed optimization and learning systems is bottlenecked
by "straggler" nodes and slow communication links, which significantly delay
computation. We propose a distributed optimization framework where the dataset
is "encoded" to have an over-complete representation with built-in redundancy,
and the ... | computer science |
12,611 | A Hierarchical Latent Vector Model for Learning Long-Term Structure in
Music | cs.LG | The Variational Autoencoder (VAE) has proven to be an effective model for
producing semantically meaningful latent representations for natural data.
However, it has thus far seen limited application to sequential data, and, as
we demonstrate, existing recurrent VAE models have difficulty modeling
sequences with long-te... | computer science |
12,612 | Minimal I-MAP MCMC for Scalable Structure Discovery in Causal DAG Models | stat.CO | Learning a Bayesian network (BN) from data can be useful for decision-making
or discovering causal relationships. However, traditional methods often fail in
modern applications, which exhibit a larger number of observed variables than
data points. The resulting uncertainty about the underlying network as well as
the de... | computer science |
12,613 | On the insufficiency of existing momentum schemes for Stochastic
Optimization | cs.LG | Momentum based stochastic gradient methods such as heavy ball (HB) and
Nesterov's accelerated gradient descent (NAG) method are widely used in
practice for training deep networks and other supervised learning models, as
they often provide significant improvements over stochastic gradient descent
(SGD). Rigorously speak... | computer science |
12,614 | Gaussian Processes Over Graphs | stat.ML | We propose Gaussian processes for signals over graphs (GPG) using the apriori
knowledge that the target vectors lie over a graph. We incorporate this
information using a graph- Laplacian based regularization which enforces the
target vectors to have a specific profile in terms of graph Fourier transform
coeffcients, fo... | computer science |
12,615 | EEG machine learning with Higuchi fractal dimension and Sample Entropy
as features for successful detection of depression | stat.ML | Reliable diagnosis of depressive disorder is essential for both optimal
treatment and prevention of fatal outcomes. In this study, we aimed to
elucidate the effectiveness of two non-linear measures, Higuchi Fractal
Dimension (HFD) and Sample Entropy (SampEn), in detecting depressive disorders
when applied on EEG. HFD a... | computer science |
12,616 | Escaping Saddles with Stochastic Gradients | cs.LG | We analyze the variance of stochastic gradients along negative curvature
directions in certain non-convex machine learning models and show that
stochastic gradients exhibit a strong component along these directions.
Furthermore, we show that - contrary to the case of isotropic noise - this
variance is proportional to t... | computer science |
12,617 | Impacts of Dirty Data: and Experimental Evaluation | cs.DB | Data quality issues have attracted widespread attention due to the negative
impacts of dirty data on data mining and machine learning results. The
relationship between data quality and the accuracy of results could be applied
on the selection of the appropriate algorithm with the consideration of data
quality and the d... | computer science |
12,618 | Decentralization Meets Quantization | cs.LG | Optimizing distributed learning systems is an art of balancing between
computation and communication. There have been two lines of research that try
to deal with slower networks: {\em quantization} for low bandwidth networks,
and {\em decentralization} for high latency networks. In this paper, we explore
a natural ques... | computer science |
12,619 | Note: Variational Encoding of Protein Dynamics Benefits from Maximizing
Latent Autocorrelation | cs.LG | As deep Variational Auto-Encoder (VAE) frameworks become more widely used for
modeling biomolecular simulation data, we emphasize the capability of the VAE
architecture to concurrently maximize the timescale of the latent space while
inferring a reduced coordinate, which assists in finding slow processes as
according t... | computer science |
12,620 | Learning Mixtures of Product Distributions via Higher Multilinear
Moments | cs.LG | Learning mixtures of $k$ binary product distributions is a central problem in
computational learning theory, but one where there are wide gaps between the
best known algorithms and lower bounds (even for restricted families of
algorithms). We narrow many of these gaps by developing novel insights about
how to reason ab... | computer science |
12,621 | Multi-device, Multi-tenant Model Selection with GP-EI | cs.LG | Bayesian optimization is the core technique behind the emergence of AutoML,
which holds the promise of automatically searching for models and
hyperparameters to make machine learning techniques more accessible. As such
services are moving towards the cloud, we ask -- {\em When multiple AutoML
users share the same compu... | computer science |
12,622 | Comparing Dynamics: Deep Neural Networks versus Glassy Systems | stat.ML | We analyze numerically the training dynamics of deep neural networks (DNN) by
using methods developed in statistical physics of glassy systems. The two main
issues we address are the complexity of the loss-landscape and of the dynamics
within it, and to what extent DNNs share similarities with glassy systems. Our
findi... | computer science |
12,623 | What Doubling Tricks Can and Can't Do for Multi-Armed Bandits | stat.ML | An online reinforcement learning algorithm is anytime if it does not need to
know in advance the horizon T of the experiment. A well-known technique to
obtain an anytime algorithm from any non-anytime algorithm is the "Doubling
Trick". In the context of adversarial or stochastic multi-armed bandits, the
performance of ... | computer science |
12,624 | D$^2$: Decentralized Training over Decentralized Data | cs.DC | While training a machine learning model using multiple workers, each of which
collects data from their own data sources, it would be most useful when the
data collected from different workers can be {\em unique} and {\em different}.
Ironically, recent analysis of decentralized parallel stochastic gradient
descent (D-PS... | computer science |
12,625 | Training Recurrent Neural Networks as a Constraint Satisfaction Problem | cs.LG | This paper presents a new approach for training artificial neural networks
using techniques for solving the constraint satisfaction problem (CSP). The
quotient gradient system (QGS) is a trajectory based method for solving the
CSP. This study converts the training set of a neural network into a CSP and
uses the QGS to ... | computer science |
12,626 | Sparse Reduced Rank Regression With Nonconvex Regularization | stat.ML | In this paper, the estimation problem for sparse reduced rank regression
(SRRR) model is considered. The SRRR model is widely used for dimension
reduction and variable selection with applications in signal processing,
econometrics, etc. The problem is formulated to minimize the least squares loss
with a sparsity-induci... | computer science |
12,627 | Risk and parameter convergence of logistic regression | cs.LG | The logistic loss is strictly convex and does not attain its infimum;
consequently the solutions of logistic regression are in general off at
infinity. This work provides a convergence analysis of gradient descent applied
to logistic regression under no assumptions on the problem instance. Firstly,
the risk is shown to... | computer science |
12,628 | Frank-Wolfe with Subsampling Oracle | math.OC | We analyze two novel randomized variants of the Frank-Wolfe (FW) or
conditional gradient algorithm. While classical FW algorithms require solving a
linear minimization problem over the domain at each iteration, the proposed
method only requires to solve a linear minimization problem over a small
\emph{subset} of the or... | computer science |
12,629 | DeepGauge: Comprehensive and Multi-Granularity Testing Criteria for
Gauging the Robustness of Deep Learning Systems | cs.SE | Deep learning defines a new data-driven programming paradigm that constructs
the internal system logic of a crafted neuron network through a set of training
data. Deep learning (DL) has been widely adopted in many safety-critical
scenarios. However, a plethora of studies have shown that the state-of-the-art
DL systems ... | computer science |
12,630 | Online Learning: Sufficient Statistics and the Burkholder Method | cs.LG | We uncover a fairly general principle in online learning: If regret can be
(approximately) expressed as a function of certain "sufficient statistics" for
the data sequence, then there exists a special Burkholder function that 1) can
be used algorithmically to achieve the regret bound and 2) only depends on
these suffic... | computer science |
12,631 | Efficient Structure Learning and Sampling of Bayesian Networks | stat.ML | Bayesian networks are probabilistic graphical models widely employed to
understand dependencies in high dimensional data, and even to facilitate causal
discovery. Learning the underlying network structure, which is encoded as a
directed acyclic graph (DAG) is highly challenging mainly due to the vast
number of possible... | computer science |
12,632 | Resilient Monotone Sequential Maximization | stat.ML | Applications in machine learning, optimization, and control require the
sequential selection of a few system elements, such as sensors, data, or
actuators, to optimize the system performance across multiple time steps.
However, in failure-prone and adversarial environments, sensors get attacked,
data get deleted, and a... | computer science |
12,633 | Stochastic Learning under Random Reshuffling | cs.LG | In empirical risk optimization, it has been observed that stochastic gradient
implementations that rely on random reshuffling of the data achieve better
performance than implementations that rely on sampling the data uniformly.
Recent works have pursued justifications for this behavior by examining the
convergence rate... | computer science |
12,634 | A Survey on Application of Machine Learning Techniques in Optical
Networks | cs.NI | Today, the amount of data that can be retrieved from communications networks
is extremely high and diverse (e.g., data regarding users behavior, traffic
traces, network alarms, signal quality indicators, etc.). Advanced mathematical
tools are required to extract useful information from this large set of network
data. I... | computer science |
12,635 | Boosting Random Forests to Reduce Bias; One-Step Boosted Forest and its
Variance Estimate | stat.ML | In this paper we propose using the principle of boosting to reduce the bias
of a random forest prediction in the regression setting. From the original
random forest fit we extract the residuals and then fit another random forest
to these residuals. We call the sum of these two random forests a
\textit{one-step boosted ... | computer science |
12,636 | Error Estimation for Randomized Least-Squares Algorithms via the
Bootstrap | stat.ML | Over the course of the past decade, a variety of randomized algorithms have
been proposed for computing approximate least-squares (LS) solutions in
large-scale settings. A longstanding practical issue is that, for any given
input, the user rarely knows the actual error of an approximate solution
(relative to the exact ... | computer science |
12,637 | Learning the Localization Function: Machine Learning Approach to
Fingerprinting Localization | cs.NI | Considered as a data-driven approach, Fingerprinting Localization Solutions
(FPSs) enjoy huge popularity due to their good performance and minimal
environment information requirement. This papers addresses applications of
artificial intelligence to solve two problems in Received Signal Strength
Indicator (RSSI) based F... | computer science |
12,638 | Boosted Density Estimation Remastered | cs.LG | There has recently been a steadily increase in the iterative approaches to
boosted density estimation and sampling, usually proceeding by adding candidate
"iterate" densities to a model that gets more accurate with iterations. The
relative accompanying burst of formal convergence results has not yet changed a
striking ... | computer science |
12,639 | Enforcing constraints for interpolation and extrapolation in Generative
Adversarial Networks | cs.LG | Generative Adversarial Networks (GANs) are becoming popular choices for
unsupervised learning. At the same time there is a concerted effort in the
machine learning community to expand the range of tasks in which learning can
be applied as well as to utilize methods from other disciplines to accelerate
learning. With th... | computer science |
12,640 | Contextual Normalization Applied to Aircraft Gas Turbine Engine
Diagnosis | cs.LG | Diagnosing faults in aircraft gas turbine engines is a complex problem. It
involves several tasks, including rapid and accurate interpretation of patterns
in engine sensor data. We have investigated contextual normalization for the
development of a software tool to help engine repair technicians with
interpretation of ... | computer science |
12,641 | Data Engineering for the Analysis of Semiconductor Manufacturing Data | cs.LG | We have analyzed manufacturing data from several different semiconductor
manufacturing plants, using decision tree induction software called Q-YIELD.
The software generates rules for predicting when a given product should be
rejected. The rules are intended to help the process engineers improve the
yield of the product... | computer science |
12,642 | Semi-Supervised Learning -- A Statistical Physics Approach | cs.LG | We present a novel approach to semi-supervised learning which is based on
statistical physics. Most of the former work in the field of semi-supervised
learning classifies the points by minimizing a certain energy function, which
corresponds to a minimal k-way cut solution. In contrast to these methods, we
estimate the ... | computer science |
12,643 | Feature Level Clustering of Large Biometric Database | cs.CV | This paper proposes an efficient technique for partitioning large biometric
database during identification. In this technique feature vector which
comprises of global and local descriptors extracted from offline signature are
used by fuzzy clustering technique to partition the database. As biometric
features posses no ... | computer science |
12,644 | Segmentation of Natural Images by Texture and Boundary Compression | cs.CV | We present a novel algorithm for segmentation of natural images that
harnesses the principle of minimum description length (MDL). Our method is
based on observations that a homogeneously textured region of a natural image
can be well modeled by a Gaussian distribution and the region boundary can be
effectively coded by... | computer science |
12,645 | Robust Low-Rank Subspace Segmentation with Semidefinite Guarantees | cs.CV | Recently there is a line of research work proposing to employ Spectral
Clustering (SC) to segment (group){Throughout the paper, we use segmentation,
clustering, and grouping, and their verb forms, interchangeably.}
high-dimensional structural data such as those (approximately) lying on
subspaces {We follow {liu2010robu... | computer science |
12,646 | A Hajj And Umrah Location Classification System For Video Crowded Scenes | cs.CV | In this paper, a new automatic system for classifying ritual locations in
diverse Hajj and Umrah video scenes is investigated. This challenging subject
has mostly been ignored in the past due to several problems one of which is the
lack of realistic annotated video datasets. HUER Dataset is defined to model
six differe... | computer science |
12,647 | Robust Text Detection in Natural Scene Images | cs.CV | Text detection in natural scene images is an important prerequisite for many
content-based image analysis tasks. In this paper, we propose an accurate and
robust method for detecting texts in natural scene images. A fast and effective
pruning algorithm is designed to extract Maximally Stable Extremal Regions
(MSERs) as... | computer science |
12,648 | Factorized Topic Models | cs.LG | In this paper we present a modification to a latent topic model, which makes
the model exploit supervision to produce a factorized representation of the
observed data. The structured parameterization separately encodes variance that
is shared between classes from variance that is private to each class by the
introducti... | computer science |
12,649 | Deep Learning for Detecting Robotic Grasps | cs.LG | We consider the problem of detecting robotic grasps in an RGB-D view of a
scene containing objects. In this work, we apply a deep learning approach to
solve this problem, which avoids time-consuming hand-design of features. This
presents two main challenges. First, we need to evaluate a huge number of
candidate grasps.... | computer science |
12,650 | Guarantees of Total Variation Minimization for Signal Recovery | cs.IT | In this paper, we consider using total variation minimization to recover
signals whose gradients have a sparse support, from a small number of
measurements. We establish the proof for the performance guarantee of total
variation (TV) minimization in recovering \emph{one-dimensional} signal with
sparse gradient support.... | computer science |
12,651 | Translation-Invariant Shrinkage/Thresholding of Group Sparse Signals | cs.CV | This paper addresses signal denoising when large-amplitude coefficients form
clusters (groups). The L1-norm and other separable sparsity models do not
capture the tendency of coefficients to cluster (group sparsity). This work
develops an algorithm, called 'overlapping group shrinkage' (OGS), based on the
minimization ... | computer science |
12,652 | Image Retrieval using Histogram Factorization and Contextual Similarity
Learning | cs.CV | Image retrieval has been a top topic in the field of both computer vision and
machine learning for a long time. Content based image retrieval, which tries to
retrieve images from a database visually similar to a query image, has
attracted much attention. Two most important issues of image retrieval are the
representati... | computer science |
12,653 | A new Bayesian ensemble of trees classifier for identifying multi-class
labels in satellite images | stat.ME | Classification of satellite images is a key component of many remote sensing
applications. One of the most important products of a raw satellite image is
the classified map which labels the image pixels into meaningful classes.
Though several parametric and non-parametric classifiers have been developed
thus far, accur... | computer science |
12,654 | Combinaison d'information visuelle, conceptuelle, et contextuelle pour
la construction automatique de hierarchies semantiques adaptees a
l'annotation d'images | cs.CV | This paper proposes a new methodology to automatically build semantic
hierarchies suitable for image annotation and classification. The building of
the hierarchy is based on a new measure of semantic similarity. The proposed
measure incorporates several sources of information: visual, conceptual and
contextual as we de... | computer science |
12,655 | An implementation of the relational k-means algorithm | cs.LG | A C# implementation of a generalized k-means variant called relational
k-means is described here. Relational k-means is a generalization of the
well-known k-means clustering method which works for non-Euclidean scenarios as
well. The input is an arbitrary distance matrix, as opposed to the traditional
k-means method, w... | computer science |
12,656 | A branch-and-bound feature selection algorithm for U-shaped cost
functions | cs.CV | This paper presents the formulation of a combinatorial optimization problem
with the following characteristics: i.the search space is the power set of a
finite set structured as a Boolean lattice; ii.the cost function forms a
U-shaped curve when applied to any lattice chain. This formulation applies for
feature selecti... | computer science |
12,657 | Multi Layer Analysis | cs.CV | This thesis presents a new methodology to analyze one-dimensional signals
trough a new approach called Multi Layer Analysis, for short MLA. It also
provides some new insights on the relationship between one-dimensional signals
processed by MLA and tree kernels, test of randomness and signal processing
techniques. The M... | computer science |
12,658 | A Novel Clustering Algorithm Based Upon Games on Evolving Network | cs.LG | This paper introduces a model based upon games on an evolving network, and
develops three clustering algorithms according to it. In the clustering
algorithms, data points for clustering are regarded as players who can make
decisions in games. On the network describing relationships among data points,
an edge-removing-a... | computer science |
12,659 | Metric and Kernel Learning using a Linear Transformation | cs.LG | Metric and kernel learning are important in several machine learning
applications. However, most existing metric learning algorithms are limited to
learning metrics over low-dimensional data, while existing kernel learning
algorithms are often limited to the transductive setting and do not generalize
to new data points... | computer science |
12,660 | Comparing Distributions and Shapes using the Kernel Distance | cs.CG | Starting with a similarity function between objects, it is possible to define
a distance metric on pairs of objects, and more generally on probability
distributions over them. These distance metrics have a deep basis in functional
analysis, measure theory and geometric measure theory, and have a rich
structure that inc... | computer science |
12,661 | Robust Recovery of Subspace Structures by Low-Rank Representation | cs.IT | In this work we address the subspace recovery problem. Given a set of data
samples (vectors) approximately drawn from a union of multiple subspaces, our
goal is to segment the samples into their respective subspaces and correct the
possible errors as well. To this end, we propose a novel method termed Low-Rank
Represen... | computer science |
12,662 | Hypothesize and Bound: A Computational Focus of Attention Mechanism for
Simultaneous N-D Segmentation, Pose Estimation and Classification Using Shape
Priors | cs.CV | Given the ever increasing bandwidth of the visual information available to
many intelligent systems, it is becoming essential to endow them with a sense
of what is worthwhile their attention and what can be safely disregarded. This
article presents a general mathematical framework to efficiently allocate the
available ... | computer science |
12,663 | Intent Inference and Syntactic Tracking with GMTI Measurements | stat.ME | In conventional target tracking systems, human operators use the estimated
target tracks to make higher level inference of the target behaviour/intent.
This paper develops syntactic filtering algorithms that assist human operators
by extracting spatial patterns from target tracks to identify
suspicious/anomalous spatia... | computer science |
12,664 | Epitome for Automatic Image Colorization | cs.CV | Image colorization adds color to grayscale images. It not only increases the
visual appeal of grayscale images, but also enriches the information contained
in scientific images that lack color information. Most existing methods of
colorization require laborious user interaction for scribbles or image
segmentation. To e... | computer science |
12,665 | Optimal Computational Trade-Off of Inexact Proximal Methods | cs.LG | In this paper, we investigate the trade-off between convergence rate and
computational cost when minimizing a composite functional with
proximal-gradient methods, which are popular optimisation tools in machine
learning. We consider the case when the proximity operator is computed via an
iterative procedure, which prov... | computer science |
12,666 | MLPACK: A Scalable C++ Machine Learning Library | cs.MS | MLPACK is a state-of-the-art, scalable, multi-platform C++ machine learning
library released in late 2011 offering both a simple, consistent API accessible
to novice users and high performance and flexibility to expert users by
leveraging modern features of C++. MLPACK provides cutting-edge algorithms
whose benchmarks ... | computer science |
12,667 | Identification of Orchid Species Using Content-Based Flower Image
Retrieval | cs.CV | In this paper, we developed the system for recognizing the orchid species by
using the images of flower. We used MSRM (Maximal Similarity based on Region
Merging) method for segmenting the flower object from the background and
extracting the shape feature such as the distance from the edge to the centroid
point of the ... | computer science |
12,668 | Playing with Duality: An Overview of Recent Primal-Dual Approaches for
Solving Large-Scale Optimization Problems | cs.NA | Optimization methods are at the core of many problems in signal/image
processing, computer vision, and machine learning. For a long time, it has been
recognized that looking at the dual of an optimization problem may drastically
simplify its solution. Deriving efficient strategies which jointly brings into
play the pri... | computer science |
12,669 | Support vector machine classification of dimensionally reduced
structural MRI images for dementia | cs.CV | We classify very-mild to moderate dementia in patients (CDR ranging from 0 to
2) using a support vector machine classifier acting on dimensionally reduced
feature set derived from MRI brain scans of the 416 subjects available in the
OASIS-Brains dataset. We use image segmentation and principal component
analysis to red... | computer science |
12,670 | A survey on sensing methods and feature extraction algorithms for SLAM
problem | cs.RO | This paper is a survey work for a bigger project for designing a Visual SLAM
robot to generate 3D dense map of an unknown unstructured environment. A lot of
factors have to be considered while designing a SLAM robot. Sensing method of
the SLAM robot should be determined by considering the kind of environment to
be mode... | computer science |
12,671 | The Cyborg Astrobiologist: Matching of Prior Textures by Image
Compression for Geological Mapping and Novelty Detection | cs.CV | (abridged) We describe an image-comparison technique of Heidemann and Ritter
that uses image compression, and is capable of: (i) detecting novel textures in
a series of images, as well as of: (ii) alerting the user to the similarity of
a new image to a previously-observed texture. This image-comparison technique
has be... | computer science |
12,672 | Exploration and Exploitation in Visuomotor Prediction of Autonomous
Agents | cs.LG | This paper discusses various techniques to let an agent learn how to predict
the effects of its own actions on its sensor data autonomously, and their
usefulness to apply them to visual sensors. An Extreme Learning Machine is used
for visuomotor prediction, while various autonomous control techniques that can
aid the p... | computer science |
12,673 | Large-scale Classification of Fine-Art Paintings: Learning The Right
Metric on The Right Feature | cs.CV | In the past few years, the number of fine-art collections that are digitized
and publicly available has been growing rapidly. With the availability of such
large collections of digitized artworks comes the need to develop multimedia
systems to archive and retrieve this pool of data. Measuring the visual
similarity betw... | computer science |
12,674 | Using Ensemble Models in the Histological Examination of Tissue
Abnormalities | cs.CV | Classification models for the automatic detection of abnormalities on
histological samples do exists, with an active debate on the cost associated
with false negative diagnosis (underdiagnosis) and false positive diagnosis
(overdiagnosis). Current models tend to underdiagnose, failing to recognize a
potentially fatal d... | computer science |
12,675 | Affine and Regional Dynamic Time Warpng | cs.CV | Pointwise matches between two time series are of great importance in time
series analysis, and dynamic time warping (DTW) is known to provide generally
reasonable matches. There are situations where time series alignment should be
invariant to scaling and offset in amplitude or where local regions of the
considered tim... | computer science |
12,676 | Training a Convolutional Neural Network for Appearance-Invariant Place
Recognition | cs.CV | Place recognition is one of the most challenging problems in computer vision,
and has become a key part in mobile robotics and autonomous driving
applications for performing loop closure in visual SLAM systems. Moreover, the
difficulty of recognizing a revisited location increases with appearance
changes caused, for in... | computer science |
12,677 | One-class classifiers based on entropic spanning graphs | cs.LG | One-class classifiers offer valuable tools to assess the presence of outliers
in data. In this paper, we propose a design methodology for one-class
classifiers based on entropic spanning graphs. Our approach takes into account
the possibility to process also non-numeric data by means of an embedding
procedure. The span... | computer science |
12,678 | Poisson noise reduction with non-local PCA | cs.CV | Photon-limited imaging arises when the number of photons collected by a
sensor array is small relative to the number of detector elements. Photon
limitations are an important concern for many applications such as spectral
imaging, night vision, nuclear medicine, and astronomy. Typically a Poisson
distribution is used t... | computer science |
12,679 | Dimension Reduction by Mutual Information Discriminant Analysis | cs.CV | In the past few decades, researchers have proposed many discriminant analysis
(DA) algorithms for the study of high-dimensional data in a variety of
problems. Most DA algorithms for feature extraction are based on
transformations that simultaneously maximize the between-class scatter and
minimize the withinclass scatte... | computer science |
12,680 | A Self-Supervised Terrain Roughness Estimator for Off-Road Autonomous
Driving | cs.CV | We present a machine learning approach for estimating the second derivative
of a drivable surface, its roughness. Robot perception generally focuses on the
first derivative, obstacle detection. However, the second derivative is also
important due to its direct relation (with speed) to the shock the vehicle
experiences.... | computer science |
12,681 | A Multi-View Embedding Space for Modeling Internet Images, Tags, and
their Semantics | cs.CV | This paper investigates the problem of modeling Internet images and
associated text or tags for tasks such as image-to-image search, tag-to-image
search, and image-to-tag search (image annotation). We start with canonical
correlation analysis (CCA), a popular and successful approach for mapping
visual and textual featu... | computer science |
12,682 | An ANN-based Method for Detecting Vocal Fold Pathology | cs.LG | There are different algorithms for vocal fold pathology diagnosis. These
algorithms usually have three stages which are Feature Extraction, Feature
Reduction and Classification. While the third stage implies a choice of a
variety of machine learning methods, the first and second stages play a
critical role in performan... | computer science |
12,683 | Classification Tree Diagrams in Health Informatics Applications | cs.IR | Health informatics deal with the methods used to optimize the acquisition,
storage and retrieval of medical data, and classify information in healthcare
applications. Healthcare analysts are particularly interested in various
computer informatics areas such as; knowledge representation from data, anomaly
detection, out... | computer science |
12,684 | Scalable Kernel Clustering: Approximate Kernel k-means | cs.CV | Kernel-based clustering algorithms have the ability to capture the non-linear
structure in real world data. Among various kernel-based clustering algorithms,
kernel k-means has gained popularity due to its simple iterative nature and
ease of implementation. However, its run-time complexity and memory footprint
increase... | computer science |
12,685 | Multi-view Metric Learning for Multi-view Video Summarization | cs.CV | Traditional methods on video summarization are designed to generate summaries
for single-view video records; and thus they cannot fully exploit the
redundancy in multi-view video records. In this paper, we present a multi-view
metric learning framework for multi-view video summarization that combines the
advantages of ... | computer science |
12,686 | Robust Temporally Coherent Laplacian Protrusion Segmentation of 3D
Articulated Bodies | cs.CV | In motion analysis and understanding it is important to be able to fit a
suitable model or structure to the temporal series of observed data, in order
to describe motion patterns in a compact way, and to discriminate between them.
In an unsupervised context, i.e., no prior model of the moving object(s) is
available, su... | computer science |
12,687 | Convergence of gradient based pre-training in Denoising autoencoders | cs.LG | The success of deep architectures is at least in part attributed to the
layer-by-layer unsupervised pre-training that initializes the network. Various
papers have reported extensive empirical analysis focusing on the design and
implementation of good pre-training procedures. However, an understanding
pertaining to the ... | computer science |
12,688 | Matrix Product State for Feature Extraction of Higher-Order Tensors | cs.CV | This paper introduces matrix product state (MPS) decomposition as a
computational tool for extracting features of multidimensional data represented
by higher-order tensors. Regardless of tensor order, MPS extracts its relevant
features to the so-called core tensor of maximum order three which can be used
for classifica... | computer science |
12,689 | Separable and non-separable data representation for pattern
discrimination | cs.CV | We provide a complete work-flow, based on the language of quantum information
theory, suitable for processing data for the purpose of pattern recognition.
The main advantage of the introduced scheme is that it can be easily
implemented and applied to process real-world data using modest computation
resources. At the sa... | computer science |
12,690 | A Theoretical Framework for Robustness of (Deep) Classifiers against
Adversarial Examples | cs.LG | Most machine learning classifiers, including deep neural networks, are
vulnerable to adversarial examples. Such inputs are typically generated by
adding small but purposeful modifications that lead to incorrect outputs while
imperceptible to human eyes. The goal of this paper is not to introduce a
single method, but to... | computer science |
12,691 | Perspective Transformer Nets: Learning Single-View 3D Object
Reconstruction without 3D Supervision | cs.CV | Understanding the 3D world is a fundamental problem in computer vision.
However, learning a good representation of 3D objects is still an open problem
due to the high dimensionality of the data and many factors of variation
involved. In this work, we investigate the task of single-view 3D object
reconstruction from a l... | computer science |
12,692 | A series of maximum entropy upper bounds of the differential entropy | cs.IT | We present a series of closed-form maximum entropy upper bounds for the
differential entropy of a continuous univariate random variable and study the
properties of that series. We then show how to use those generic bounds for
upper bounding the differential entropy of Gaussian mixture models. This
requires to calculate... | computer science |
12,693 | Fast Patch-based Style Transfer of Arbitrary Style | cs.CV | Artistic style transfer is an image synthesis problem where the content of an
image is reproduced with the style of another. Recent works show that a
visually appealing style transfer can be achieved by using the hidden
activations of a pretrained convolutional neural network. However, existing
methods either apply (i)... | computer science |
12,694 | FINN: A Framework for Fast, Scalable Binarized Neural Network Inference | cs.CV | Research has shown that convolutional neural networks contain significant
redundancy, and high classification accuracy can be obtained even when weights
and activations are reduced from floating point to binary values. In this
paper, we present FINN, a framework for building fast and flexible FPGA
accelerators using a ... | computer science |
12,695 | A Unified Algorithmic Framework for Multi-Dimensional Scaling | cs.LG | In this paper, we propose a unified algorithmic framework for solving many
known variants of \mds. Our algorithm is a simple iterative scheme with
guaranteed convergence, and is \emph{modular}; by changing the internals of a
single subroutine in the algorithm, we can switch cost functions and target
spaces easily. In a... | computer science |
12,696 | Handwritten digit recognition by bio-inspired hierarchical networks | cs.LG | The human brain processes information showing learning and prediction
abilities but the underlying neuronal mechanisms still remain unknown.
Recently, many studies prove that neuronal networks are able of both
generalizations and associations of sensory inputs. In this paper, following a
set of neurophysiological evide... | computer science |
12,697 | Learning Monocular Reactive UAV Control in Cluttered Natural
Environments | cs.RO | Autonomous navigation for large Unmanned Aerial Vehicles (UAVs) is fairly
straight-forward, as expensive sensors and monitoring devices can be employed.
In contrast, obstacle avoidance remains a challenging task for Micro Aerial
Vehicles (MAVs) which operate at low altitude in cluttered environments. Unlike
large vehic... | computer science |
12,698 | TOP-SPIN: TOPic discovery via Sparse Principal component INterference | cs.CV | We propose a novel topic discovery algorithm for unlabeled images based on
the bag-of-words (BoW) framework. We first extract a dictionary of visual words
and subsequently for each image compute a visual word occurrence histogram. We
view these histograms as rows of a large matrix from which we extract sparse
principal... | computer science |
12,699 | Fast Training of Effective Multi-class Boosting Using Coordinate Descent
Optimization | cs.CV | Wepresentanovelcolumngenerationbasedboostingmethod for multi-class
classification. Our multi-class boosting is formulated in a single optimization
problem as in Shen and Hao (2011). Different from most existing multi-class
boosting methods, which use the same set of weak learners for all the classes,
we train class spe... | computer science |
12,700 | The Power of Asymmetry in Binary Hashing | cs.LG | When approximating binary similarity using the hamming distance between short
binary hashes, we show that even if the similarity is symmetric, we can have
shorter and more accurate hashes by using two distinct code maps. I.e. by
approximating the similarity between $x$ and $x'$ as the hamming distance
between $f(x)$ an... | computer science |
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