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5,100 | A bio-inspired image coder with temporal scalability | cs.CV | We present a novel bio-inspired and dynamic coding scheme for static images.
Our coder aims at reproducing the main steps of the visual stimulus processing
in the mammalian retina taking into account its time behavior. The main novelty
of this work is to show how to exploit the time behavior of the retina cells to
ensu... | computer science |
5,101 | An Application of Backpropagation Artificial Neural Network Method for
Measuring The Severity of Osteoarthritis | cs.NE | The examination of Osteoarthritis disease through X-ray by rheumatology can
be classified into four grade of severity. This paper discusses about the
application of artificial neural network backpropagation method for measuring
the severity of the disease, where the observed X-ray range from wrist to
fingers. The main ... | computer science |
5,102 | Learning to See by Moving | cs.CV | The dominant paradigm for feature learning in computer vision relies on
training neural networks for the task of object recognition using millions of
hand labelled images. Is it possible to learn useful features for a diverse set
of visual tasks using any other form of supervision? In biology, living
organisms develope... | computer science |
5,103 | Efficient Large Scale Video Classification | cs.CV | Video classification has advanced tremendously over the recent years. A large
part of the improvements in video classification had to do with the work done
by the image classification community and the use of deep convolutional
networks (CNNs) which produce competitive results with hand- crafted motion
features. These ... | computer science |
5,104 | Texture Synthesis Using Convolutional Neural Networks | cs.CV | Here we introduce a new model of natural textures based on the feature spaces
of convolutional neural networks optimised for object recognition. Samples from
the model are of high perceptual quality demonstrating the generative power of
neural networks trained in a purely discriminative fashion. Within the model,
textu... | computer science |
5,105 | PoseNet: A Convolutional Network for Real-Time 6-DOF Camera
Relocalization | cs.CV | We present a robust and real-time monocular six degree of freedom
relocalization system. Our system trains a convolutional neural network to
regress the 6-DOF camera pose from a single RGB image in an end-to-end manner
with no need of additional engineering or graph optimisation. The algorithm can
operate indoors and o... | computer science |
5,106 | A robust autoassociative memory with coupled networks of Kuramoto-type
oscillators | nlin.AO | Uncertain recognition success, unfavorable scaling of connection complexity
or dependence on complex external input impair the usefulness of current
oscillatory neural networks for pattern recognition or restrict technical
realizations to small networks. We propose a new network architecture of
coupled oscillators for ... | computer science |
5,107 | Computing With Contextual Numbers | cs.CE | Self Organizing Map (SOM) has been applied into several classical modeling
tasks including clustering, classification, function approximation and
visualization of high dimensional spaces. The final products of a trained SOM
are a set of ordered (low dimensional) indices and their associated high
dimensional weight vect... | computer science |
5,108 | Automated Classification of L/R Hand Movement EEG Signals using Advanced
Feature Extraction and Machine Learning | cs.NE | In this paper, we propose an automated computer platform for the purpose of
classifying Electroencephalography (EEG) signals associated with left and right
hand movements using a hybrid system that uses advanced feature extraction
techniques and machine learning algorithms. It is known that EEG represents the
brain act... | computer science |
5,109 | Pixels to Voxels: Modeling Visual Representation in the Human Brain | cs.CV | The human brain is adept at solving difficult high-level visual processing
problems such as image interpretation and object recognition in natural scenes.
Over the past few years neuroscientists have made remarkable progress in
understanding how the human brain represents categories of objects and actions
in natural sc... | computer science |
5,110 | Spectral classification using convolutional neural networks | cs.CV | There is a great need for accurate and autonomous spectral classification
methods in astrophysics. This thesis is about training a convolutional neural
network (ConvNet) to recognize an object class (quasar, star or galaxy) from
one-dimension spectra only. Author developed several scripts and C programs for
datasets pr... | computer science |
5,111 | A Neural Algorithm of Artistic Style | cs.CV | In fine art, especially painting, humans have mastered the skill to create
unique visual experiences through composing a complex interplay between the
content and style of an image. Thus far the algorithmic basis of this process
is unknown and there exists no artificial system with similar capabilities.
However, in oth... | computer science |
5,112 | Applying deep learning to classify pornographic images and videos | cs.CV | It is no secret that pornographic material is now a one-click-away from
everyone, including children and minors. General social media networks are
striving to isolate adult images and videos from normal ones. Intelligent image
analysis methods can help to automatically detect and isolate questionable
images in media. U... | computer science |
5,113 | YodaNN: An Architecture for Ultra-Low Power Binary-Weight CNN
Acceleration | cs.AR | Convolutional neural networks (CNNs) have revolutionized the world of
computer vision over the last few years, pushing image classification beyond
human accuracy. The computational effort of today's CNNs requires power-hungry
parallel processors or GP-GPUs. Recent developments in CNN accelerators for
system-on-chip int... | computer science |
5,114 | Artificial Neural Networks for Detection of Malaria in RBCs | cs.CV | Malaria is one of the most common diseases caused by mosquitoes and is a
great public health problem worldwide. Currently, for malaria diagnosis the
standard technique is microscopic examination of a stained blood film. We
propose use of Artificial Neural Networks (ANN) for the diagnosis of the
disease in the red blood... | computer science |
5,115 | A Large Contextual Dataset for Classification, Detection and Counting of
Cars with Deep Learning | cs.CV | We have created a large diverse set of cars from overhead images, which are
useful for training a deep learner to binary classify, detect and count them.
The dataset and all related material will be made publically available. The set
contains contextual matter to aid in identification of difficult targets. We
demonstra... | computer science |
5,116 | Deep Convolutional Networks as Models of Generalization and Blending
Within Visual Creativity | cs.NE | We examine two recent artificial intelligence (AI) based deep learning
algorithms for visual blending in convolutional neural networks (Mordvintsev et
al. 2015, Gatys et al. 2015). To investigate the potential value of these
algorithms as tools for computational creativity research, we explain and
schematize the essent... | computer science |
5,117 | Multiple Instance Fuzzy Inference Neural Networks | cs.NE | Fuzzy logic is a powerful tool to model knowledge uncertainty, measurements
imprecision, and vagueness. However, there is another type of vagueness that
arises when data have multiple forms of expression that fuzzy logic does not
address quite well. This is the case for multiple instance learning problems
(MIL). In MIL... | computer science |
5,118 | Sparsey: Event Recognition via Deep Hierarchical Spare Distributed Codes | cs.CV | Visual cortex's hierarchical, multi-level organization is captured in many
biologically inspired computational vision models, the general idea being that
progressively larger scale, more complex spatiotemporal features are
represented in progressively higher areas. However, most earlier models use
localist representati... | computer science |
5,119 | Deep Tensor Convolution on Multicores | cs.CV | Deep convolutional neural networks (ConvNets) of 3-dimensional kernels allow
joint modeling of spatiotemporal features. These networks have improved
performance of video and volumetric image analysis, but have been limited in
size due to the low memory ceiling of GPU hardware. Existing CPU
implementations overcome this... | computer science |
5,120 | A Radically New Theory of how the Brain Represents and Computes with
Probabilities | cs.CV | The brain is believed to implement probabilistic reasoning and to represent
information via population, or distributed, coding. Most previous
population-based probabilistic (PPC) theories share several basic properties:
1) continuous-valued neurons; 2) fully(densely)-distributed codes, i.e.,
all(most) units participate... | computer science |
5,121 | Stable and Controllable Neural Texture Synthesis and Style Transfer
Using Histogram Losses | cs.GR | Recently, methods have been proposed that perform texture synthesis and style
transfer by using convolutional neural networks (e.g. Gatys et al.
[2015,2016]). These methods are exciting because they can in some cases create
results with state-of-the-art quality. However, in this paper, we show these
methods also have l... | computer science |
5,122 | Large-scale image analysis using docker sandboxing | cs.CV | With the advent of specialized hardware such as Graphics Processing Units
(GPUs), large scale image localization, classification and retrieval have seen
increased prevalence. Designing scalable software architecture that co-evolves
with such specialized hardware is a challenge in the commercial setting. In
this paper, ... | computer science |
5,123 | Weakly supervised training of deep convolutional neural networks for
overhead pedestrian localization in depth fields | cs.CV | Overhead depth map measurements capture sufficient amount of information to
enable human experts to track pedestrians accurately. However, fully automating
this process using image analysis algorithms can be challenging. Even though
hand-crafted image analysis algorithms are successful in many common cases,
they fail f... | computer science |
5,124 | SideEye: A Generative Neural Network Based Simulator of Human Peripheral
Vision | cs.NE | Foveal vision makes up less than 1% of the visual field. The other 99% is
peripheral vision. Precisely what human beings see in the periphery is both
obvious and mysterious in that we see it with our own eyes but can't visualize
what we see, except in controlled lab experiments. Degradation of information
in the periph... | computer science |
5,125 | Learned Primal-dual Reconstruction | math.OC | We propose the Learned Primal-Dual algorithm for tomographic reconstruction.
The algorithm accounts for a (possibly non-linear) forward operator in a deep
neural network by unrolling a proximal primal-dual optimization method, but
where the proximal operators have been replaced with convolutional neural
networks. The a... | computer science |
5,126 | Model based learning for accelerated, limited-view 3D photoacoustic
tomography | cs.CV | Recent advances in deep learning for tomographic reconstructions have shown
great potential to create accurate and high quality images with a considerable
speed-up. In this work we present a deep neural network that is specifically
designed to provide high resolution 3D images from restricted photoacoustic
measurements... | computer science |
5,127 | MUFold-SS: Protein Secondary Structure Prediction Using Deep
Inception-Inside-Inception Networks | cs.CV | Motivation: Protein secondary structure prediction can provide important
information for protein 3D structure prediction and protein functions. Deep
learning, which has been successfully applied to various research fields such
as image classification and voice recognition, provides a new opportunity to
significantly im... | computer science |
5,128 | A self-organizing neural network architecture for learning human-object
interactions | cs.NE | The visual recognition of transitive actions comprising human-object
interactions is a key component for artificial systems operating in natural
environments. This challenging task requires jointly the recognition of
articulated body actions as well as the extraction of semantic elements from
the scene such as the iden... | computer science |
5,129 | Vector Quantization using the Improved Differential Evolution Algorithm
for Image Compression | cs.CV | Vector Quantization, VQ is a popular image compression technique with a
simple decoding architecture and high compression ratio. Codebook designing is
the most essential part in Vector Quantization. LindeBuzoGray, LBG is a
traditional method of generation of VQ Codebook which results in lower PSNR
value. A Codebook aff... | computer science |
5,130 | Phase Transitions in Image Denoising via Sparsely Coding Convolutional
Neural Networks | cs.NE | Neural networks are analogous in many ways to spin glasses, systems which are
known for their rich set of dynamics and equally complex phase diagrams. We
apply well-known techniques in the study of spin glasses to a convolutional
sparsely encoding neural network and observe power law finite-size scaling
behavior in the... | computer science |
5,131 | Convolutional Drift Networks for Video Classification | cs.CV | Analyzing spatio-temporal data like video is a challenging task that requires
processing visual and temporal information effectively. Convolutional Neural
Networks have shown promise as baseline fixed feature extractors through
transfer learning, a technique that helps minimize the training cost on visual
information. ... | computer science |
5,132 | Compact Neural Networks based on the Multiscale Entanglement
Renormalization Ansatz | cs.NE | The goal of this paper is to demonstrate a method for tensorizing neural
networks based upon an efficient way of approximating scale invariant quantum
states, the Multi-scale Entanglement Renormalization Ansatz (MERA). We employ
MERA as a replacement for linear layers in a neural network and test this
implementation on... | computer science |
5,133 | A General Neural Network Hardware Architecture on FPGA | cs.CV | Field Programmable Gate Arrays (FPGAs) plays an increasingly important role
in data sampling and processing industries due to its highly parallel
architecture, low power consumption, and flexibility in custom algorithms.
Especially, in the artificial intelligence field, for training and implement
the neural networks an... | computer science |
5,134 | Neural Network Reinforcement Learning for Audio-Visual Gaze Control in
Human-Robot Interaction | cs.RO | This paper introduces a novel neural network-based reinforcement learning
approach for robot gaze control. Our approach enables a robot to learn and
adapt its gaze control strategy for human-robot interaction without the use of
external sensors or human supervision. The robot learns to focus its attention
on groups of ... | computer science |
5,135 | WSNet: Compact and Efficient Networks with Weight Sampling | cs.CV | We present a new approach and a novel architecture, termed WSNet, for
learning compact and efficient deep neural networks. Existing approaches
conventionally learn full model parameters independently at first and then
compress them via ad hoc processing like model pruning or filter factorization.
Different from them, W... | computer science |
5,136 | Triagem virtual de imagens de imuno-histoquímica usando redes neurais
artificiais e espectro de padrões | cs.CV | The importance of organizing medical images according to their nature,
application and relevance is increasing. Furhermore, a previous selection of
medical images can be useful to accelerate the task of analysis by
pathologists. Herein this work we propose an image classifier to integrate a
CBIR (Content-Based Image Re... | computer science |
5,137 | Dialectical Multispectral Classification of Diffusion-Weighted Magnetic
Resonance Images as an Alternative to Apparent Diffusion Coefficients Maps to
Perform Anatomical Analysis | cs.CV | Multispectral image analysis is a relatively promising field of research with
applications in several areas, such as medical imaging and satellite
monitoring. A considerable number of current methods of analysis are based on
parametric statistics. Alternatively, some methods in Computational
Intelligence are inspired b... | computer science |
5,138 | Detection and classification of masses in mammographic images in a
multi-kernel approach | cs.CV | According to the World Health Organization, breast cancer is the main cause
of cancer death among adult women in the world. Although breast cancer occurs
indiscriminately in countries with several degrees of social and economic
development, among developing and underdevelopment countries mortality rates
are still high,... | computer science |
5,139 | An Incremental Self-Organizing Architecture for Sensorimotor Learning
and Prediction | cs.CV | During visuomotor tasks, robots must compensate for temporal delays inherent
in their sensorimotor processing systems. Delay compensation becomes crucial in
a dynamic environment where the visual input is constantly changing, e.g.,
during the interacting with a human demonstrator. For this purpose, the robot
must be eq... | computer science |
5,140 | A tutorial on conformal prediction | cs.LG | Conformal prediction uses past experience to determine precise levels of
confidence in new predictions. Given an error probability $\epsilon$, together
with a method that makes a prediction $\hat{y}$ of a label $y$, it produces a
set of labels, typically containing $\hat{y}$, that also contains $y$ with
probability $1-... | computer science |
5,141 | Learning Balanced Mixtures of Discrete Distributions with Small Sample | cs.LG | We study the problem of partitioning a small sample of $n$ individuals from a
mixture of $k$ product distributions over a Boolean cube $\{0, 1\}^K$ according
to their distributions. Each distribution is described by a vector of allele
frequencies in $\R^K$. Given two distributions, we use $\gamma$ to denote the
average... | computer science |
5,142 | Exploring Large Feature Spaces with Hierarchical Multiple Kernel
Learning | cs.LG | For supervised and unsupervised learning, positive definite kernels allow to
use large and potentially infinite dimensional feature spaces with a
computational cost that only depends on the number of observations. This is
usually done through the penalization of predictor functions by Euclidean or
Hilbertian norms. In ... | computer science |
5,143 | Matrix Completion from a Few Entries | cs.LG | Let M be a random (alpha n) x n matrix of rank r<<n, and assume that a
uniformly random subset E of its entries is observed. We describe an efficient
algorithm that reconstructs M from |E| = O(rn) observed entries with relative
root mean square error RMSE <= C(rn/|E|)^0.5 . Further, if r=O(1), M can be
reconstructed ex... | computer science |
5,144 | Model-Consistent Sparse Estimation through the Bootstrap | cs.LG | We consider the least-square linear regression problem with regularization by
the $\ell^1$-norm, a problem usually referred to as the Lasso. In this paper,
we first present a detailed asymptotic analysis of model consistency of the
Lasso in low-dimensional settings. For various decays of the regularization
parameter, w... | computer science |
5,145 | Tree Exploration for Bayesian RL Exploration | stat.ML | Research in reinforcement learning has produced algorithms for optimal
decision making under uncertainty that fall within two main types. The first
employs a Bayesian framework, where optimality improves with increased
computational time. This is because the resulting planning task takes the form
of a dynamic programmi... | computer science |
5,146 | Induction of High-level Behaviors from Problem-solving Traces using
Machine Learning Tools | stat.ML | This paper applies machine learning techniques to student modeling. It
presents a method for discovering high-level student behaviors from a very
large set of low-level traces corresponding to problem-solving actions in a
learning environment. Basic actions are encoded into sets of domain-dependent
attribute-value patt... | computer science |
5,147 | The Infinite Hierarchical Factor Regression Model | cs.LG | We propose a nonparametric Bayesian factor regression model that accounts for
uncertainty in the number of factors, and the relationship between factors. To
accomplish this, we propose a sparse variant of the Indian Buffet Process and
couple this with a hierarchical model over factors, based on Kingman's
coalescent. We... | computer science |
5,148 | Streamed Learning: One-Pass SVMs | cs.LG | We present a streaming model for large-scale classification (in the context
of $\ell_2$-SVM) by leveraging connections between learning and computational
geometry. The streaming model imposes the constraint that only a single pass
over the data is allowed. The $\ell_2$-SVM is known to have an equivalent
formulation in ... | computer science |
5,149 | Learning Exponential Families in High-Dimensions: Strong Convexity and
Sparsity | cs.LG | The versatility of exponential families, along with their attendant convexity
properties, make them a popular and effective statistical model. A central
issue is learning these models in high-dimensions, such as when there is some
sparsity pattern of the optimal parameter. This work characterizes a certain
strong conve... | computer science |
5,150 | How to Explain Individual Classification Decisions | stat.ML | After building a classifier with modern tools of machine learning we
typically have a black box at hand that is able to predict well for unseen
data. Thus, we get an answer to the question what is the most likely label of a
given unseen data point. However, most methods will provide no answer why the
model predicted th... | computer science |
5,151 | Gaussian Process Structural Equation Models with Latent Variables | cs.LG | In a variety of disciplines such as social sciences, psychology, medicine and
economics, the recorded data are considered to be noisy measurements of latent
variables connected by some causal structure. This corresponds to a family of
graphical models known as the structural equation model with latent variables.
While ... | computer science |
5,152 | Exploratory Analysis of Functional Data via Clustering and Optimal
Segmentation | stat.ML | We propose in this paper an exploratory analysis algorithm for functional
data. The method partitions a set of functions into $K$ clusters and represents
each cluster by a simple prototype (e.g., piecewise constant). The total number
of segments in the prototypes, $P$, is chosen by the user and optimally
distributed am... | computer science |
5,153 | Generative and Latent Mean Map Kernels | cs.LG | We introduce two kernels that extend the mean map, which embeds probability
measures in Hilbert spaces. The generative mean map kernel (GMMK) is a smooth
similarity measure between probabilistic models. The latent mean map kernel
(LMMK) generalizes the non-iid formulation of Hilbert space embeddings of
empirical distri... | computer science |
5,154 | A Unifying View of Multiple Kernel Learning | stat.ML | Recent research on multiple kernel learning has lead to a number of
approaches for combining kernels in regularized risk minimization. The proposed
approaches include different formulations of objectives and varying
regularization strategies. In this paper we present a unifying general
optimization criterion for multip... | computer science |
5,155 | Context models on sequences of covers | stat.ML | We present a class of models that, via a simple construction, enables exact,
incremental, non-parametric, polynomial-time, Bayesian inference of conditional
measures. The approach relies upon creating a sequence of covers on the
conditioning variable and maintaining a different model for each set within a
cover. Infere... | computer science |
5,156 | Using a Kernel Adatron for Object Classification with RCS Data | cs.LG | Rapid identification of object from radar cross section (RCS) signals is
important for many space and military applications. This identification is a
problem in pattern recognition which either neural networks or support vector
machines should prove to be high-speed. Bayesian networks would also provide
value but requi... | computer science |
5,157 | Online Learning via Sequential Complexities | cs.LG | We consider the problem of sequential prediction and provide tools to study
the minimax value of the associated game. Classical statistical learning theory
provides several useful complexity measures to study learning with i.i.d. data.
Our proposed sequential complexities can be seen as extensions of these
measures to ... | computer science |
5,158 | Fast ABC-Boost for Multi-Class Classification | cs.LG | Abc-boost is a new line of boosting algorithms for multi-class
classification, by utilizing the commonly used sum-to-zero constraint. To
implement abc-boost, a base class must be identified at each boosting step.
Prior studies used a very expensive procedure based on exhaustive search for
determining the base class at ... | computer science |
5,159 | Approximate Inference and Stochastic Optimal Control | cs.LG | We propose a novel reformulation of the stochastic optimal control problem as
an approximate inference problem, demonstrating, that such a interpretation
leads to new practical methods for the original problem. In particular we
characterise a novel class of iterative solutions to the stochastic optimal
control problem ... | computer science |
5,160 | Regularization Strategies and Empirical Bayesian Learning for MKL | stat.ML | Multiple kernel learning (MKL), structured sparsity, and multi-task learning
have recently received considerable attention. In this paper, we show how
different MKL algorithms can be understood as applications of either
regularization on the kernel weights or block-norm-based regularization, which
is more common in str... | computer science |
5,161 | Classifying Clustering Schemes | stat.ML | Many clustering schemes are defined by optimizing an objective function
defined on the partitions of the underlying set of a finite metric space. In
this paper, we construct a framework for studying what happens when we instead
impose various structural conditions on the clustering schemes, under the
general heading of... | computer science |
5,162 | The Sample Complexity of Dictionary Learning | stat.ML | A large set of signals can sometimes be described sparsely using a
dictionary, that is, every element can be represented as a linear combination
of few elements from the dictionary. Algorithms for various signal processing
applications, including classification, denoising and signal separation, learn
a dictionary from ... | computer science |
5,163 | In All Likelihood, Deep Belief Is Not Enough | stat.ML | Statistical models of natural stimuli provide an important tool for
researchers in the fields of machine learning and computational neuroscience. A
canonical way to quantitatively assess and compare the performance of
statistical models is given by the likelihood. One class of statistical models
which has recently gain... | computer science |
5,164 | Split Bregman Method for Sparse Inverse Covariance Estimation with
Matrix Iteration Acceleration | stat.ML | We consider the problem of estimating the inverse covariance matrix by
maximizing the likelihood function with a penalty added to encourage the
sparsity of the resulting matrix. We propose a new approach based on the split
Bregman method to solve the regularized maximum likelihood estimation problem.
We show that our m... | computer science |
5,165 | Shaping Level Sets with Submodular Functions | cs.LG | We consider a class of sparsity-inducing regularization terms based on
submodular functions. While previous work has focused on non-decreasing
functions, we explore symmetric submodular functions and their \lova
extensions. We show that the Lovasz extension may be seen as the convex
envelope of a function that depends ... | computer science |
5,166 | Concentration-Based Guarantees for Low-Rank Matrix Reconstruction | cs.LG | We consider the problem of approximately reconstructing a partially-observed,
approximately low-rank matrix. This problem has received much attention lately,
mostly using the trace-norm as a surrogate to the rank. Here we study low-rank
matrix reconstruction using both the trace-norm, as well as the less-studied
max-no... | computer science |
5,167 | Sparse Signal Recovery with Temporally Correlated Source Vectors Using
Sparse Bayesian Learning | stat.ML | We address the sparse signal recovery problem in the context of multiple
measurement vectors (MMV) when elements in each nonzero row of the solution
matrix are temporally correlated. Existing algorithms do not consider such
temporal correlations and thus their performance degrades significantly with
the correlations. I... | computer science |
5,168 | Multi-label Learning via Structured Decomposition and Group Sparsity | cs.LG | In multi-label learning, each sample is associated with several labels.
Existing works indicate that exploring correlations between labels improve the
prediction performance. However, embedding the label correlations into the
training process significantly increases the problem size. Moreover, the
mapping of the label ... | computer science |
5,169 | Generalization error bounds for stationary autoregressive models | stat.ML | We derive generalization error bounds for stationary univariate
autoregressive (AR) models. We show that imposing stationarity is enough to
control the Gaussian complexity without further regularization. This lets us
use structural risk minimization for model selection. We demonstrate our
methods by predicting interest... | computer science |
5,170 | A note on active learning for smooth problems | cs.LG | We show that the disagreement coefficient of certain smooth hypothesis
classes is $O(m)$, where $m$ is the dimension of the hypothesis space, thereby
answering a question posed in \cite{friedman09}. | computer science |
5,171 | Clustered regression with unknown clusters | cs.LG | We consider a collection of prediction experiments, which are clustered in
the sense that groups of experiments ex- hibit similar relationship between the
predictor and response variables. The experiment clusters as well as the
regres- sion relationships are unknown. The regression relation- ships define
the experiment... | computer science |
5,172 | Classification of Sets using Restricted Boltzmann Machines | cs.LG | We consider the problem of classification when inputs correspond to sets of
vectors. This setting occurs in many problems such as the classification of
pieces of mail containing several pages, of web sites with several sections or
of images that have been pre-segmented into smaller regions. We propose
generalizations o... | computer science |
5,173 | Rapid Learning with Stochastic Focus of Attention | cs.LG | We present a method to stop the evaluation of a decision making process when
the result of the full evaluation is obvious. This trait is highly desirable
for online margin-based machine learning algorithms where a classifier
traditionally evaluates all the features for every example. We observe that
some examples are e... | computer science |
5,174 | Pruning nearest neighbor cluster trees | stat.ML | Nearest neighbor (k-NN) graphs are widely used in machine learning and data
mining applications, and our aim is to better understand what they reveal about
the cluster structure of the unknown underlying distribution of points.
Moreover, is it possible to identify spurious structures that might arise due
to sampling va... | computer science |
5,175 | Generalized Boosting Algorithms for Convex Optimization | cs.LG | Boosting is a popular way to derive powerful learners from simpler hypothesis
classes. Following previous work (Mason et al., 1999; Friedman, 2000) on
general boosting frameworks, we analyze gradient-based descent algorithms for
boosting with respect to any convex objective and introduce a new measure of
weak learner p... | computer science |
5,176 | PAC-Bayesian Analysis of Martingales and Multiarmed Bandits | cs.LG | We present two alternative ways to apply PAC-Bayesian analysis to sequences
of dependent random variables. The first is based on a new lemma that enables
to bound expectations of convex functions of certain dependent random variables
by expectations of the same functions of independent Bernoulli random
variables. This ... | computer science |
5,177 | PAC-Bayesian Analysis of the Exploration-Exploitation Trade-off | cs.LG | We develop a coherent framework for integrative simultaneous analysis of the
exploration-exploitation and model order selection trade-offs. We improve over
our preceding results on the same subject (Seldin et al., 2011) by combining
PAC-Bayesian analysis with Bernstein-type inequality for martingales. Such a
combinatio... | computer science |
5,178 | Rademacher complexity of stationary sequences | stat.ML | We show how to control the generalization error of time series models wherein
past values of the outcome are used to predict future values. The results are
based on a generalization of standard i.i.d. concentration inequalities to
dependent data without the mixing assumptions common in the time series
setting. Our proo... | computer science |
5,179 | Optimal Reinforcement Learning for Gaussian Systems | stat.ML | The exploration-exploitation trade-off is among the central challenges of
reinforcement learning. The optimal Bayesian solution is intractable in
general. This paper studies to what extent analytic statements about optimal
learning are possible if all beliefs are Gaussian processes. A first order
approximation of learn... | computer science |
5,180 | Hashing Algorithms for Large-Scale Learning | stat.ML | In this paper, we first demonstrate that b-bit minwise hashing, whose
estimators are positive definite kernels, can be naturally integrated with
learning algorithms such as SVM and logistic regression. We adopt a simple
scheme to transform the nonlinear (resemblance) kernel into linear (inner
product) kernel; and hence... | computer science |
5,181 | Using More Data to Speed-up Training Time | cs.LG | In many recent applications, data is plentiful. By now, we have a rather
clear understanding of how more data can be used to improve the accuracy of
learning algorithms. Recently, there has been a growing interest in
understanding how more data can be leveraged to reduce the required training
runtime. In this paper, we... | computer science |
5,182 | Large-Scale Convex Minimization with a Low-Rank Constraint | cs.LG | We address the problem of minimizing a convex function over the space of
large matrices with low rank. While this optimization problem is hard in
general, we propose an efficient greedy algorithm and derive its formal
approximation guarantees. Each iteration of the algorithm involves
(approximately) finding the left an... | computer science |
5,183 | Efficient Transductive Online Learning via Randomized Rounding | cs.LG | Most traditional online learning algorithms are based on variants of mirror
descent or follow-the-leader. In this paper, we present an online algorithm
based on a completely different approach, tailored for transductive settings,
which combines "random playout" and randomized rounding of loss subgradients.
As an applic... | computer science |
5,184 | From Bandits to Experts: On the Value of Side-Observations | cs.LG | We consider an adversarial online learning setting where a decision maker can
choose an action in every stage of the game. In addition to observing the
reward of the chosen action, the decision maker gets side observations on the
reward he would have obtained had he chosen some of the other actions. The
observation str... | computer science |
5,185 | Robust Bayesian reinforcement learning through tight lower bounds | cs.LG | In the Bayesian approach to sequential decision making, exact calculation of
the (subjective) utility is intractable. This extends to most special cases of
interest, such as reinforcement learning problems. While utility bounds are
known to exist for this problem, so far none of them were particularly tight.
In this pa... | computer science |
5,186 | Learning with the Weighted Trace-norm under Arbitrary Sampling
Distributions | cs.LG | We provide rigorous guarantees on learning with the weighted trace-norm under
arbitrary sampling distributions. We show that the standard weighted trace-norm
might fail when the sampling distribution is not a product distribution (i.e.
when row and column indexes are not selected independently), present a
corrected var... | computer science |
5,187 | Tight Measurement Bounds for Exact Recovery of Structured Sparse Signals | stat.ML | Standard compressive sensing results state that to exactly recover an s
sparse signal in R^p, one requires O(s. log(p)) measurements. While this bound
is extremely useful in practice, often real world signals are not only sparse,
but also exhibit structure in the sparsity pattern. We focus on
group-structured patterns ... | computer science |
5,188 | A General Framework for Structured Sparsity via Proximal Optimization | cs.LG | We study a generalized framework for structured sparsity. It extends the
well-known methods of Lasso and Group Lasso by incorporating additional
constraints on the variables as part of a convex optimization problem. This
framework provides a straightforward way of favouring prescribed sparsity
patterns, such as orderin... | computer science |
5,189 | Ensemble Risk Modeling Method for Robust Learning on Scarce Data | cs.LG | In medical risk modeling, typical data are "scarce": they have relatively
small number of training instances (N), censoring, and high dimensionality (M).
We show that the problem may be effectively simplified by reducing it to
bipartite ranking, and introduce new bipartite ranking algorithm, Smooth Rank,
for robust lea... | computer science |
5,190 | Stability Conditions for Online Learnability | cs.LG | Stability is a general notion that quantifies the sensitivity of a learning
algorithm's output to small change in the training dataset (e.g. deletion or
replacement of a single training sample). Such conditions have recently been
shown to be more powerful to characterize learnability in the general learning
setting und... | computer science |
5,191 | Structured Sparsity and Generalization | cs.LG | We present a data dependent generalization bound for a large class of
regularized algorithms which implement structured sparsity constraints. The
bound can be applied to standard squared-norm regularization, the Lasso, the
group Lasso, some versions of the group Lasso with overlapping groups, multiple
kernel learning a... | computer science |
5,192 | Group Lasso with Overlaps: the Latent Group Lasso approach | stat.ML | We study a norm for structured sparsity which leads to sparse linear
predictors whose supports are unions of prede ned overlapping groups of
variables. We call the obtained formulation latent group Lasso, since it is
based on applying the usual group Lasso penalty on a set of latent variables. A
detailed analysis of th... | computer science |
5,193 | Efficient Latent Variable Graphical Model Selection via Split Bregman
Method | stat.ML | We consider the problem of covariance matrix estimation in the presence of
latent variables. Under suitable conditions, it is possible to learn the
marginal covariance matrix of the observed variables via a tractable convex
program, where the concentration matrix of the observed variables is decomposed
into a sparse ma... | computer science |
5,194 | A Reliable Effective Terascale Linear Learning System | cs.LG | We present a system and a set of techniques for learning linear predictors
with convex losses on terascale datasets, with trillions of features, {The
number of features here refers to the number of non-zero entries in the data
matrix.} billions of training examples and millions of parameters in an hour
using a cluster ... | computer science |
5,195 | An Optimal Algorithm for Linear Bandits | cs.LG | We provide the first algorithm for online bandit linear optimization whose
regret after T rounds is of order sqrt{Td ln N} on any finite class X of N
actions in d dimensions, and of order d*sqrt{T} (up to log factors) when X is
infinite. These bounds are not improvable in general. The basic idea utilizes
tools from con... | computer science |
5,196 | Kernel Topic Models | cs.LG | Latent Dirichlet Allocation models discrete data as a mixture of discrete
distributions, using Dirichlet beliefs over the mixture weights. We study a
variation of this concept, in which the documents' mixture weight beliefs are
replaced with squashed Gaussian distributions. This allows documents to be
associated with e... | computer science |
5,197 | Revisiting k-means: New Algorithms via Bayesian Nonparametrics | cs.LG | Bayesian models offer great flexibility for clustering
applications---Bayesian nonparametrics can be used for modeling infinite
mixtures, and hierarchical Bayesian models can be utilized for sharing clusters
across multiple data sets. For the most part, such flexibility is lacking in
classical clustering methods such a... | computer science |
5,198 | The Graphical Lasso: New Insights and Alternatives | stat.ML | The graphical lasso \citep{FHT2007a} is an algorithm for learning the
structure in an undirected Gaussian graphical model, using $\ell_1$
regularization to control the number of zeros in the precision matrix
${\B\Theta}={\B\Sigma}^{-1}$ \citep{BGA2008,yuan_lin_07}. The {\texttt R}
package \GL\ \citep{FHT2007a} is popul... | computer science |
5,199 | Learning a Factor Model via Regularized PCA | cs.LG | We consider the problem of learning a linear factor model. We propose a
regularized form of principal component analysis (PCA) and demonstrate through
experiments with synthetic and real data the superiority of resulting estimates
to those produced by pre-existing factor analysis approaches. We also establish
theoretic... | computer science |
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