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9,701 | Multidimensional classification of hippocampal shape features
discriminates Alzheimer's disease and mild cognitive impairment from normal
aging | cs.CV | We describe a new method to automatically discriminate between patients with
Alzheimer's disease (AD) or mild cognitive impairment (MCI) and elderly
controls, based on multidimensional classification of hippocampal shape
features. This approach uses spherical harmonics (SPHARM) coefficients to model
the shape of the hi... | computer science |
9,702 | EnzyNet: enzyme classification using 3D convolutional neural networks on
spatial representation | cs.CV | During the past decade, with the significant progress of computational power
as well as ever-rising data availability, deep learning techniques became
increasingly popular due to their excellent performance on computer vision
problems. The size of the Protein Data Bank has increased more than 15 fold
since 1999, which ... | computer science |
9,703 | Estimating speech from lip dynamics | cs.CV | The goal of this project is to develop a limited lip reading algorithm for a
subset of the English language. We consider a scenario in which no audio
information is available. The raw video is processed and the position of the
lips in each frame is extracted. We then prepare the lip data for processing
and classify the... | computer science |
9,704 | Encoding Multi-Resolution Brain Networks Using Unsupervised Deep
Learning | stat.ML | The main goal of this study is to extract a set of brain networks in multiple
time-resolutions to analyze the connectivity patterns among the anatomic
regions for a given cognitive task. We suggest a deep architecture which learns
the natural groupings of the connectivity patterns of human brain in multiple
time-resolu... | computer science |
9,705 | Efficient training-image based geostatistical simulation and inversion
using a spatial generative adversarial neural network | stat.ML | Probabilistic inversion within a multiple-point statistics framework is still
computationally prohibitive for large-scale problems. To partly address this,
we introduce and evaluate a new training-image based simulation and inversion
approach for complex geologic media. Our approach relies on a deep neural
network of t... | computer science |
9,706 | Continual One-Shot Learning of Hidden Spike-Patterns with Neural Network
Simulation Expansion and STDP Convergence Predictions | cs.CV | This paper presents a constructive algorithm that achieves successful
one-shot learning of hidden spike-patterns in a competitive detection task. It
has previously been shown (Masquelier et al., 2008) that spike-timing-dependent
plasticity (STDP) and lateral inhibition can result in neurons competitively
tuned to repea... | computer science |
9,707 | Yet Another ADNI Machine Learning Paper? Paving The Way Towards
Fully-reproducible Research on Classification of Alzheimer's Disease | stat.ML | In recent years, the number of papers on Alzheimer's disease classification
has increased dramatically, generating interesting methodological ideas on the
use machine learning and feature extraction methods. However, practical impact
is much more limited and, eventually, one could not tell which of these
approaches are... | computer science |
9,708 | Statistical learning of spatiotemporal patterns from longitudinal
manifold-valued networks | stat.ML | We introduce a mixed-effects model to learn spatiotempo-ral patterns on a
network by considering longitudinal measures distributed on a fixed graph. The
data come from repeated observations of subjects at different time points which
take the form of measurement maps distributed on a graph such as an image or a
mesh. Th... | computer science |
9,709 | Smoothness-based Edge Detection using Low-SNR Camera for Robot
Navigation | cs.CV | In the emerging advancement in the branch of autonomous robotics, the ability
of a robot to efficiently localize and construct maps of its surrounding is
crucial. This paper deals with utilizing thermal-infrared cameras, as opposed
to conventional cameras as the primary sensor to capture images of the robot's
surroundi... | computer science |
9,710 | Anatomical Pattern Analysis for decoding visual stimuli in human brains | stat.ML | Background: A universal unanswered question in neuroscience and machine
learning is whether computers can decode the patterns of the human brain.
Multi-Voxels Pattern Analysis (MVPA) is a critical tool for addressing this
question. However, there are two challenges in the previous MVPA methods, which
include decreasing... | computer science |
9,711 | Iterative PET Image Reconstruction Using Convolutional Neural Network
Representation | cs.CV | PET image reconstruction is challenging due to the ill-poseness of the
inverse problem and limited number of detected photons. Recently deep neural
networks have been widely and successfully used in computer vision tasks and
attracted growing interests in medical imaging. In this work, we trained a deep
residual convol... | computer science |
9,712 | Deep Hyperalignment | cs.CV | This paper proposes Deep Hyperalignment (DHA) as a regularized, deep
extension, scalable Hyperalignment (HA) method, which is well-suited for
applying functional alignment to fMRI datasets with nonlinearity,
high-dimensionality (broad ROI), and a large number of subjects. Unlink
previous methods, DHA is not limited by ... | computer science |
9,713 | Multimodal Observation and Interpretation of Subjects Engaged in Problem
Solving | cs.HC | In this paper we present the first results of a pilot experiment in the
capture and interpretation of multimodal signals of human experts engaged in
solving challenging chess problems. Our goal is to investigate the extent to
which observations of eye-gaze, posture, emotion and other physiological
signals can be used t... | computer science |
9,714 | Densely Connected Convolutional Networks and Signal Quality Analysis to
Detect Atrial Fibrillation Using Short Single-Lead ECG Recordings | eess.SP | The development of new technology such as wearables that record high-quality
single channel ECG, provides an opportunity for ECG screening in a larger
population, especially for atrial fibrillation screening. The main goal of this
study is to develop an automatic classification algorithm for normal sinus
rhythm (NSR), ... | computer science |
9,715 | Linear-Time Algorithm in Bayesian Image Denoising based on Gaussian
Markov Random Field | stat.ML | In this paper, we consider Bayesian image denoising based on a Gaussian
Markov random field (GMRF) model, for which we propose an new algorithm. Our
method can solve Bayesian image denoising problems, including hyperparameter
estimation, in $O(n)$-time, where $n$ is the number of pixels in a given image.
From the persp... | computer science |
9,716 | Updating the VESICLE-CNN Synapse Detector | cs.CV | We present an updated version of the VESICLE-CNN algorithm presented by
Roncal et al. (2014). The original implementation makes use of a patch-based
approach. This methodology is known to be slow due to repeated computations. We
update this implementation to be fully convolutional through the use of dilated
convolution... | computer science |
9,717 | Revealing structure components of the retina by deep learning networks | stat.ML | Deep convolutional neural networks (CNNs) have demonstrated impressive
performance on visual object classification tasks. In addition, it is a useful
model for predication of neuronal responses recorded in visual system. However,
there is still no clear understanding of what CNNs learn in terms of visual
neuronal circu... | computer science |
9,718 | SolarisNet: A Deep Regression Network for Solar Radiation Prediction | cs.CV | Effective utilization of photovoltaic (PV) plants requires weather
variability robust global solar radiation (GSR) forecasting models. Random
weather turbulence phenomena coupled with assumptions of clear sky model as
suggested by Hottel pose significant challenges to parametric & non-parametric
models in GSR conversio... | computer science |
9,719 | Parallel transport in shape analysis: a scalable numerical scheme | cs.CV | The analysis of manifold-valued data requires efficient tools from Riemannian
geometry to cope with the computational complexity at stake. This complexity
arises from the always-increasing dimension of the data, and the absence of
closed-form expressions to basic operations such as the Riemannian logarithm.
In this pap... | computer science |
9,720 | Robust PCA, Subspace Learning, and Tracking | cs.IT | PCA is one of the most widely used dimension reduction techniques. A related
easier problem is "subspace learning" or "subspace estimation". Given
relatively clean data, both are easily solved via singular value decomposition
(SVD). The problem of subspace learning or PCA in the presence of outliers is
called robust su... | computer science |
9,721 | A fatal point concept and a low-sensitivity quantitative measure for
traffic safety analytics | cs.CV | The variability of the clusters generated by clustering techniques in the
domain of latitude and longitude variables of fatal crash data are
significantly unpredictable. This unpredictability, caused by the randomness of
fatal crash incidents, reduces the accuracy of crash frequency (i.e., counts of
fatal crashes per c... | computer science |
9,722 | Tighter Lifting-Free Convex Relaxations for Quadratic Matching Problems | math.OC | In this work we study convex relaxations of quadratic optimisation problems
over permutation matrices. While existing semidefinite programming approaches
can achieve remarkably tight relaxations, they have the strong disadvantage
that they lift the original $n {\times} n$-dimensional variable to an $n^2
{\times} n^2$-d... | computer science |
9,723 | MR image reconstruction using deep density priors | cs.CV | Purpose: MR image reconstruction exploits regularization to compensate for
missing k-space data. In this work, we propose to learn the probability
distribution of MR image patches with neural networks and use this distribution
as prior information constraining images during reconstruction, effectively
employing it as r... | computer science |
9,724 | Toward Multimodal Image-to-Image Translation | cs.CV | Many image-to-image translation problems are ambiguous, as a single input
image may correspond to multiple possible outputs. In this work, we aim to
model a \emph{distribution} of possible outputs in a conditional generative
modeling setting. The ambiguity of the mapping is distilled in a
low-dimensional latent vector,... | computer science |
9,725 | Stochastic reconstruction of an oolitic limestone by generative
adversarial networks | cs.CV | Stochastic image reconstruction is a key part of modern digital rock physics
and materials analysis that aims to create numerous representative samples of
material micro-structures for upscaling, numerical computation of effective
properties and uncertainty quantification. We present a method of
three-dimensional stoch... | computer science |
9,726 | Distributed Mapper | cs.CV | The construction of Mapper has emerged in the last decade as a powerful and
effective topological data analysis tool that approximates and generalizes
other topological summaries, such as the Reeb graph, the contour tree, split,
and joint trees. In this paper we study the parallel analysis of the
construction of Mapper... | computer science |
9,727 | Nearly Optimal Robust Subspace Tracking and Dynamic Robust PCA | cs.IT | We study the robust subspace tracking (RST) problem and obtain one of the
first provable guarantees for it. The goal of RST is to track data that lies in
a slowly changing low-dimensional subspace, and the subspaces themselves, while
being robust to corruption by (often large magnitude) sparse outliers. It can
be simpl... | computer science |
9,728 | Attenuation Correction for Brain PET imaging using Deep Neural Network
based on Dixon and ZTE MR images | cs.CV | Positron Emission Tomography (PET) is a functional imaging modality widely
used in neuroscience studies. To obtain meaningful quantitative results from
PET images, attenuation correction is necessary during image reconstruction.
For PET/MR hybrid systems, PET attenuation is challenging as Magnetic Resonance
(MR) images... | computer science |
9,729 | Incremental Adversarial Domain Adaptation for Continually Changing
Environments | stat.ML | Continuous appearance shifts such as changes in weather and lighting
conditions can impact the performance of deployed machine learning models.
While unsupervised domain adaptation aims to address this challenge, current
approaches do not utilise the continuity of the occurring shifts. In
particular, many robotics appl... | computer science |
9,730 | Towards dense object tracking in a 2D honeybee hive | cs.CV | From human crowds to cells in tissue, the detection and efficient tracking of
multiple objects in dense configurations is an important and unsolved problem.
In the past, limitations of image analysis have restricted studies of dense
groups to tracking a single or subset of marked individuals, or to
coarse-grained group... | computer science |
9,731 | Optimizing Channel Selection for Seizure Detection | eess.SP | Interpretation of electroencephalogram (EEG) signals can be complicated by
obfuscating artifacts. Artifact detection plays an important role in the
observation and analysis of EEG signals. Spatial information contained in the
placement of the electrodes can be exploited to accurately detect artifacts.
However, when few... | computer science |
9,732 | Improved EEG Event Classification Using Differential Energy | eess.SP | Feature extraction for automatic classification of EEG signals typically
relies on time frequency representations of the signal. Techniques such as
cepstral-based filter banks or wavelets are popular analysis techniques in many
signal processing applications including EEG classification. In this paper, we
present a com... | computer science |
9,733 | Generating Adversarial Examples with Adversarial Networks | cs.CR | Deep neural networks (DNNs) have been found to be vulnerable to adversarial
examples resulting from adding small-magnitude perturbations to inputs. Such
adversarial examples can mislead DNNs to produce adversary-selected results.
Different attack strategies have been proposed to generate adversarial
examples, but how t... | computer science |
9,734 | Spatially Transformed Adversarial Examples | cs.CR | Recent studies show that widely used deep neural networks (DNNs) are
vulnerable to carefully crafted adversarial examples. Many advanced algorithms
have been proposed to generate adversarial examples by leveraging the
$\mathcal{L}_p$ distance for penalizing perturbations. Researchers have
explored different defense met... | computer science |
9,735 | Feature Decomposition Based Saliency Detection in Electron
Cryo-Tomograms | cs.CV | Electron Cryo-Tomography (ECT) allows 3D visualization of subcellular
structures at the submolecular resolution in close to the native state.
However, due to the high degree of structural complexity and imaging limits,
the automatic segmentation of cellular components from ECT images is very
difficult. To complement an... | computer science |
9,736 | Model compression for faster structural separation of macromolecules
captured by Cellular Electron Cryo-Tomography | cs.CV | Electron Cryo-Tomography (ECT) enables 3D visualization of macromolecule
structure inside single cells. Macromolecule classification approaches based on
convolutional neural networks (CNN) were developed to separate millions of
macromolecules captured from ECT systematically. However, given the fast
accumulation of ECT... | computer science |
9,737 | Image restoration with generalized Gaussian mixture model patch priors | eess.IV | Patch priors have became an important component of image restoration. A
powerful approach in this category of restoration algorithms is the popular
Expected Patch Log-likelihood (EPLL) algorithm. EPLL uses a Gaussian mixture
model (GMM) prior learned on clean image patches as a way to regularize
degraded patches. In th... | computer science |
9,738 | Highly accurate model for prediction of lung nodule malignancy with CT
scans | cs.CV | Computed tomography (CT) examinations are commonly used to predict lung
nodule malignancy in patients, which are shown to improve noninvasive early
diagnosis of lung cancer. It remains challenging for computational approaches
to achieve performance comparable to experienced radiologists. Here we present
NoduleX, a syst... | computer science |
9,739 | Convolutional Hashing for Automated Scene Matching | cs.CV | We present a powerful new loss function and training scheme for learning
binary hash functions. In particular, we demonstrate our method by creating for
the first time a neural network that outperforms state-of-the-art Haar wavelets
and color layout descriptors at the task of automated scene matching. By
accurately rel... | computer science |
9,740 | Deep learning based supervised semantic segmentation of Electron
Cryo-Subtomograms | cs.CV | Cellular Electron Cryo-Tomography (CECT) is a powerful imaging technique for
the 3D visualization of cellular structure and organization at submolecular
resolution. It enables analyzing the native structures of macromolecular
complexes and their spatial organization inside single cells. However, due to
the high degree ... | computer science |
9,741 | Conditioning of three-dimensional generative adversarial networks for
pore and reservoir-scale models | stat.ML | Geostatistical modeling of petrophysical properties is a key step in modern
integrated oil and gas reservoir studies. Recently, generative adversarial
networks (GAN) have been shown to be a successful method for generating
unconditional simulations of pore- and reservoir-scale models. This
contribution leverages the di... | computer science |
9,742 | DropLasso: A robust variant of Lasso for single cell RNA-seq data | cs.CV | Single-cell RNA sequencing (scRNA-seq) is a fast growing approach to measure
the genome-wide transcriptome of many individual cells in parallel, but results
in noisy data with many dropout events. Existing methods to learn molecular
signatures from bulk transcriptomic data may therefore not be adapted to
scRNA-seq data... | computer science |
9,743 | A General Pipeline for 3D Detection of Vehicles | cs.CV | Autonomous driving requires 3D perception of vehicles and other objects in
the in environment. Much of the current methods support 2D vehicle detection.
This paper proposes a flexible pipeline to adopt any 2D detection network and
fuse it with a 3D point cloud to generate 3D information with minimum changes
of the 2D d... | computer science |
9,744 | Applicability and interpretation of the deterministic weighted cepstral
distance | cs.SY | Quantifying similarity between data objects is an important part of modern
data science. Deciding what similarity measure to use is very application
dependent. In this paper, we combine insights from systems theory and machine
learning, and investigate the weighted cepstral distance, which was previously
defined for si... | computer science |
9,745 | Classifying Online Dating Profiles on Tinder using FaceNet Facial
Embeddings | cs.CV | A method to produce personalized classification models to automatically
review online dating profiles on Tinder is proposed, based on the user's
historical preference. The method takes advantage of a FaceNet facial
classification model to extract features which may be related to facial
attractiveness. The embeddings fr... | computer science |
9,746 | Synchronisation of Partial Multi-Matchings via Non-negative
Factorisations | cs.CV | In this work we study permutation synchronisation for the challenging case of
partial permutations, which plays an important role for the problem of matching
multiple objects (e.g. images or shapes). The term synchronisation refers to
the property that the set of pairwise matchings is cycle-consistent, i.e. in
the full... | computer science |
9,747 | Positive-unlabeled convolutional neural networks for particle picking in
cryo-electron micrographs | cs.CV | Cryo-electron microscopy (cryoEM) is fast becoming the preferred method for
protein structure determination. Particle picking is a significant bottleneck
in the solving of protein structures from single particle cryoEM. Hand labeling
sufficient numbers of particles can take months of effort and current
computationally ... | computer science |
9,748 | Distributed Real-Time Sentiment Analysis for Big Data Social Streams | stat.ML | Big data trend has enforced the data-centric systems to have continuous fast
data streams. In recent years, real-time analytics on stream data has formed
into a new research field, which aims to answer queries about
what-is-happening-now with a negligible delay. The real challenge with
real-time stream data processing ... | computer science |
9,749 | Topic Similarity Networks: Visual Analytics for Large Document Sets | cs.CL | We investigate ways in which to improve the interpretability of LDA topic
models by better analyzing and visualizing their outputs. We focus on examining
what we refer to as topic similarity networks: graphs in which nodes represent
latent topics in text collections and links represent similarity among topics.
We descr... | computer science |
9,750 | Learning about Spanish dialects through Twitter | stat.ML | This paper maps the large-scale variation of the Spanish language by
employing a corpus based on geographically tagged Twitter messages. Lexical
dialects are extracted from an analysis of variants of tens of concepts. The
resulting maps show linguistic variation on an unprecedented scale across the
globe. We discuss th... | computer science |
9,751 | Anchored Correlation Explanation: Topic Modeling with Minimal Domain
Knowledge | cs.CL | While generative models such as Latent Dirichlet Allocation (LDA) have proven
fruitful in topic modeling, they often require detailed assumptions and careful
specification of hyperparameters. Such model complexity issues only compound
when trying to generalize generative models to incorporate human input. We
introduce ... | computer science |
9,752 | Computational Content Analysis of Negative Tweets for Obesity, Diet,
Diabetes, and Exercise | cs.SI | Social media based digital epidemiology has the potential to support faster
response and deeper understanding of public health related threats. This study
proposes a new framework to analyze unstructured health related textual data
via Twitter users' post (tweets) to characterize the negative health sentiments
and non-... | computer science |
9,753 | Characterizing Diabetes, Diet, Exercise, and Obesity Comments on Twitter | cs.SI | Social media provide a platform for users to express their opinions and share
information. Understanding public health opinions on social media, such as
Twitter, offers a unique approach to characterizing common health issues such
as diabetes, diet, exercise, and obesity (DDEO), however, collecting and
analyzing a larg... | computer science |
9,754 | $A^{4}NT$: Author Attribute Anonymity by Adversarial Training of Neural
Machine Translation | cs.CR | Text-based analysis methods allow to reveal privacy relevant author
attributes such as gender, age and identify of the text's author. Such methods
can compromise the privacy of an anonymous author even when the author tries to
remove privacy sensitive content. In this paper, we propose an automatic
method, called Adver... | computer science |
9,755 | Mining Public Opinion about Economic Issues: Twitter and the U.S.
Presidential Election | cs.SI | Opinion polls have been the bridge between public opinion and politicians in
elections. However, developing surveys to disclose people's feedback with
respect to economic issues is limited, expensive, and time-consuming. In recent
years, social media such as Twitter has enabled people to share their opinions
regarding ... | computer science |
9,756 | A Theory of Universal Artificial Intelligence based on Algorithmic
Complexity | cs.AI | Decision theory formally solves the problem of rational agents in uncertain
worlds if the true environmental prior probability distribution is known.
Solomonoff's theory of universal induction formally solves the problem of
sequence prediction for unknown prior distribution. We combine both ideas and
get a parameterles... | computer science |
9,757 | Noise-Tolerant Learning, the Parity Problem, and the Statistical Query
Model | cs.LG | We describe a slightly sub-exponential time algorithm for learning parity
functions in the presence of random classification noise. This results in a
polynomial-time algorithm for the case of parity functions that depend on only
the first O(log n log log n) bits of input. This is the first known instance of
an efficien... | computer science |
9,758 | General Loss Bounds for Universal Sequence Prediction | cs.AI | The Bayesian framework is ideally suited for induction problems. The
probability of observing $x_t$ at time $t$, given past observations
$x_1...x_{t-1}$ can be computed with Bayes' rule if the true distribution $\mu$
of the sequences $x_1x_2x_3...$ is known. The problem, however, is that in many
cases one does not even... | computer science |
9,759 | An effective Procedure for Speeding up Algorithms | cs.CC | The provably asymptotically fastest algorithm within a factor of 5 for
formally described problems will be constructed. The main idea is to enumerate
all programs provably equivalent to the original problem by enumerating all
proofs. The algorithm could be interpreted as a generalization and improvement
of Levin search... | computer science |
9,760 | Fitness Uniform Selection to Preserve Genetic Diversity | cs.AI | In evolutionary algorithms, the fitness of a population increases with time
by mutating and recombining individuals and by a biased selection of more fit
individuals. The right selection pressure is critical in ensuring sufficient
optimization progress on the one hand and in preserving genetic diversity to be
able to e... | computer science |
9,761 | Bounds on sample size for policy evaluation in Markov environments | cs.LG | Reinforcement learning means finding the optimal course of action in
Markovian environments without knowledge of the environment's dynamics.
Stochastic optimization algorithms used in the field rely on estimates of the
value of a policy. Typically, the value of a policy is estimated from results
of simulating that very... | computer science |
9,762 | Convergence and Error Bounds for Universal Prediction of Nonbinary
Sequences | cs.LG | Solomonoff's uncomputable universal prediction scheme $\xi$ allows to predict
the next symbol $x_k$ of a sequence $x_1...x_{k-1}$ for any Turing computable,
but otherwise unknown, probabilistic environment $\mu$. This scheme will be
generalized to arbitrary environmental classes, which, among others, allows the
constru... | computer science |
9,763 | Self-Optimizing and Pareto-Optimal Policies in General Environments
based on Bayes-Mixtures | cs.AI | The problem of making sequential decisions in unknown probabilistic
environments is studied. In cycle $t$ action $y_t$ results in perception $x_t$
and reward $r_t$, where all quantities in general may depend on the complete
history. The perception $x_t$ and reward $r_t$ are sampled from the (reactive)
environmental pro... | computer science |
9,764 | Optimal Ordered Problem Solver | cs.AI | We present a novel, general, optimally fast, incremental way of searching for
a universal algorithm that solves each task in a sequence of tasks. The Optimal
Ordered Problem Solver (OOPS) continually organizes and exploits previously
found solutions to earlier tasks, efficiently searching not only the space of
domain-s... | computer science |
9,765 | Convergence and Loss Bounds for Bayesian Sequence Prediction | cs.LG | The probability of observing $x_t$ at time $t$, given past observations
$x_1...x_{t-1}$ can be computed with Bayes' rule if the true generating
distribution $\mu$ of the sequences $x_1x_2x_3...$ is known. If $\mu$ is
unknown, but known to belong to a class $M$ one can base ones prediction on the
Bayes mix $\xi$ defined... | computer science |
9,766 | The New AI: General & Sound & Relevant for Physics | cs.AI | Most traditional artificial intelligence (AI) systems of the past 50 years
are either very limited, or based on heuristics, or both. The new millennium,
however, has brought substantial progress in the field of theoretically optimal
and practically feasible algorithms for prediction, search, inductive inference
based o... | computer science |
9,767 | Universal Sequential Decisions in Unknown Environments | cs.AI | We give a brief introduction to the AIXI model, which unifies and overcomes
the limitations of sequential decision theory and universal Solomonoff
induction. While the former theory is suited for active agents in known
environments, the latter is suited for passive prediction of unknown
environments. | computer science |
9,768 | Bayesian Treatment of Incomplete Discrete Data applied to Mutual
Information and Feature Selection | cs.LG | Given the joint chances of a pair of random variables one can compute
quantities of interest, like the mutual information. The Bayesian treatment of
unknown chances involves computing, from a second order prior distribution and
the data likelihood, a posterior distribution of the chances. A common
treatment of incomple... | computer science |
9,769 | Optimality of Universal Bayesian Sequence Prediction for General Loss
and Alphabet | cs.LG | Various optimality properties of universal sequence predictors based on
Bayes-mixtures in general, and Solomonoff's prediction scheme in particular,
will be studied. The probability of observing $x_t$ at time $t$, given past
observations $x_1...x_{t-1}$ can be computed with the chain rule if the true
generating distrib... | computer science |
9,770 | Convergence of Discrete MDL for Sequential Prediction | cs.LG | We study the properties of the Minimum Description Length principle for
sequence prediction, considering a two-part MDL estimator which is chosen from
a countable class of models. This applies in particular to the important case
of universal sequence prediction, where the model class corresponds to all
algorithms for s... | computer science |
9,771 | Deriving a Stationary Dynamic Bayesian Network from a Logic Program with
Recursive Loops | cs.AI | Recursive loops in a logic program present a challenging problem to the PLP
framework. On the one hand, they loop forever so that the PLP backward-chaining
inferences would never stop. On the other hand, they generate cyclic
influences, which are disallowed in Bayesian networks. Therefore, in existing
PLP approaches lo... | computer science |
9,772 | Monotone Conditional Complexity Bounds on Future Prediction Errors | cs.LG | We bound the future loss when predicting any (computably) stochastic sequence
online. Solomonoff finitely bounded the total deviation of his universal
predictor M from the true distribution m by the algorithmic complexity of m.
Here we assume we are at a time t>1 and already observed x=x_1...x_t. We bound
the future pr... | computer science |
9,773 | Robust Inference of Trees | cs.LG | This paper is concerned with the reliable inference of optimal
tree-approximations to the dependency structure of an unknown distribution
generating data. The traditional approach to the problem measures the
dependency strength between random variables by the index called mutual
information. In this paper reliability i... | computer science |
9,774 | Low-rank matrix factorization with attributes | cs.LG | We develop a new collaborative filtering (CF) method that combines both
previously known users' preferences, i.e. standard CF, as well as product/user
attributes, i.e. classical function approximation, to predict a given user's
interest in a particular product. Our method is a generalized low rank matrix
completion pro... | computer science |
9,775 | A Novel Bayesian Classifier using Copula Functions | cs.LG | A useful method for representing Bayesian classifiers is through
\emph{discriminant functions}. Here, using copula functions, we propose a new
model for discriminants. This model provides a rich and generalized class of
decision boundaries. These decision boundaries significantly boost the
classification accuracy espec... | computer science |
9,776 | Algorithmic Complexity Bounds on Future Prediction Errors | cs.LG | We bound the future loss when predicting any (computably) stochastic sequence
online. Solomonoff finitely bounded the total deviation of his universal
predictor $M$ from the true distribution $mu$ by the algorithmic complexity of
$mu$. Here we assume we are at a time $t>1$ and already observed $x=x_1...x_t$.
We bound t... | computer science |
9,777 | Reinforcement Learning for Adaptive Routing | cs.LG | Reinforcement learning means learning a policy--a mapping of observations
into actions--based on feedback from the environment. The learning can be
viewed as browsing a set of policies while evaluating them by trial through
interaction with the environment. We present an application of gradient ascent
algorithm for rei... | computer science |
9,778 | Learning a Machine for the Decision in a Partially Observable Markov
Universe | math.GM | In this paper, we are interested in optimal decisions in a partially
observable Markov universe. Our viewpoint departs from the dynamic programming
viewpoint: we are directly approximating an optimal strategic tree depending on
the observation. This approximation is made by means of a parameterized
probabilistic law. I... | computer science |
9,779 | The Role of Time in the Creation of Knowledge | cs.LG | This paper I assume that in humans the creation of knowledge depends on a
discrete time, or stage, sequential decision-making process subjected to a
stochastic, information transmitting environment. For each time-stage, this
environment randomly transmits Shannon type information-packets to the
decision-maker, who exam... | computer science |
9,780 | Clustering and Feature Selection using Sparse Principal Component
Analysis | cs.AI | In this paper, we study the application of sparse principal component
analysis (PCA) to clustering and feature selection problems. Sparse PCA seeks
sparse factors, or linear combinations of the data variables, explaining a
maximum amount of variance in the data while having only a limited number of
nonzero coefficients... | computer science |
9,781 | A Reactive Tabu Search Algorithm for Stimuli Generation in
Psycholinguistics | cs.AI | The generation of meaningless "words" matching certain statistical and/or
linguistic criteria is frequently needed for experimental purposes in
Psycholinguistics. Such stimuli receive the name of pseudowords or nonwords in
the Cognitive Neuroscience literatue. The process for building nonwords
sometimes has to be based... | computer science |
9,782 | On the Complexity of Binary Samples | cs.DM | Consider a class $\mH$ of binary functions $h: X\to\{-1, +1\}$ on a finite
interval $X=[0, B]\subset \Real$. Define the {\em sample width} of $h$ on a
finite subset (a sample) $S\subset X$ as $\w_S(h) \equiv \min_{x\in S}
|\w_h(x)|$, where $\w_h(x) = h(x) \max\{a\geq 0: h(z)=h(x), x-a\leq z\leq
x+a\}$. Let $\mathbb{S}_... | computer science |
9,783 | Knowledge Technologies | cs.CY | Several technologies are emerging that provide new ways to capture, store,
present and use knowledge. This book is the first to provide a comprehensive
introduction to five of the most important of these technologies: Knowledge
Engineering, Knowledge Based Engineering, Knowledge Webs, Ontologies and
Semantic Webs. For ... | computer science |
9,784 | Rollout Sampling Approximate Policy Iteration | cs.LG | Several researchers have recently investigated the connection between
reinforcement learning and classification. We are motivated by proposals of
approximate policy iteration schemes without value functions which focus on
policy representation using classifiers and address policy learning as a
supervised learning probl... | computer science |
9,785 | Algorithm Selection as a Bandit Problem with Unbounded Losses | cs.AI | Algorithm selection is typically based on models of algorithm performance,
learned during a separate offline training sequence, which can be prohibitively
expensive. In recent work, we adopted an online approach, in which a
performance model is iteratively updated and used to guide selection on a
sequence of problem in... | computer science |
9,786 | Learning Hidden Markov Models using Non-Negative Matrix Factorization | cs.LG | The Baum-Welsh algorithm together with its derivatives and variations has
been the main technique for learning Hidden Markov Models (HMM) from
observational data. We present an HMM learning algorithm based on the
non-negative matrix factorization (NMF) of higher order Markovian statistics
that is structurally different... | computer science |
9,787 | Time Hopping technique for faster reinforcement learning in simulations | cs.AI | This preprint has been withdrawn by the author for revision | computer science |
9,788 | Eligibility Propagation to Speed up Time Hopping for Reinforcement
Learning | cs.AI | A mechanism called Eligibility Propagation is proposed to speed up the Time
Hopping technique used for faster Reinforcement Learning in simulations.
Eligibility Propagation provides for Time Hopping similar abilities to what
eligibility traces provide for conventional Reinforcement Learning. It
propagates values from o... | computer science |
9,789 | Continuous Strategy Replicator Dynamics for Multi--Agent Learning | cs.LG | The problem of multi-agent learning and adaptation has attracted a great deal
of attention in recent years. It has been suggested that the dynamics of multi
agent learning can be studied using replicator equations from population
biology. Most existing studies so far have been limited to discrete strategy
spaces with a... | computer science |
9,790 | Differentially Private Empirical Risk Minimization | cs.LG | Privacy-preserving machine learning algorithms are crucial for the
increasingly common setting in which personal data, such as medical or
financial records, are analyzed. We provide general techniques to produce
privacy-preserving approximations of classifiers learned via (regularized)
empirical risk minimization (ERM)... | computer science |
9,791 | A Minimum Relative Entropy Controller for Undiscounted Markov Decision
Processes | cs.AI | Adaptive control problems are notoriously difficult to solve even in the
presence of plant-specific controllers. One way to by-pass the intractable
computation of the optimal policy is to restate the adaptive control as the
minimization of the relative entropy of a controller that ignores the true
plant dynamics from a... | computer science |
9,792 | A Generalization of the Chow-Liu Algorithm and its Application to
Statistical Learning | cs.IT | We extend the Chow-Liu algorithm for general random variables while the
previous versions only considered finite cases. In particular, this paper
applies the generalization to Suzuki's learning algorithm that generates from
data forests rather than trees based on the minimum description length by
balancing the fitness ... | computer science |
9,793 | Designing neural networks that process mean values of random variables | cs.AI | We introduce a class of neural networks derived from probabilistic models in
the form of Bayesian networks. By imposing additional assumptions about the
nature of the probabilistic models represented in the networks, we derive
neural networks with standard dynamics that require no training to determine
the synaptic wei... | computer science |
9,794 | Distantly Labeling Data for Large Scale Cross-Document Coreference | cs.AI | Cross-document coreference, the problem of resolving entity mentions across
multi-document collections, is crucial to automated knowledge base construction
and data mining tasks. However, the scarcity of large labeled data sets has
hindered supervised machine learning research for this task. In this paper we
develop an... | computer science |
9,795 | A Probabilistic Approach for Learning Folksonomies from Structured Data | cs.AI | Learning structured representations has emerged as an important problem in
many domains, including document and Web data mining, bioinformatics, and image
analysis. One approach to learning complex structures is to integrate many
smaller, incomplete and noisy structure fragments. In this work, we present an
unsupervise... | computer science |
9,796 | Agnostic Learning of Monomials by Halfspaces is Hard | cs.CC | We prove the following strong hardness result for learning: Given a
distribution of labeled examples from the hypercube such that there exists a
monomial consistent with $(1-\eps)$ of the examples, it is NP-hard to find a
halfspace that is correct on $(1/2+\eps)$ of the examples, for arbitrary
constants $\eps > 0$. In ... | computer science |
9,797 | Closed-set-based Discovery of Bases of Association Rules | cs.LG | The output of an association rule miner is often huge in practice. This is
why several concise lossless representations have been proposed, such as the
"essential" or "representative" rules. We revisit the algorithm given by
Kryszkiewicz (Int. Symp. Intelligent Data Analysis 2001, Springer-Verlag LNCS
2189, 350-359) fo... | computer science |
9,798 | Border Algorithms for Computing Hasse Diagrams of Arbitrary Lattices | cs.AI | The Border algorithm and the iPred algorithm find the Hasse diagrams of FCA
lattices. We show that they can be generalized to arbitrary lattices. In the
case of iPred, this requires the identification of a join-semilattice
homomorphism into a distributive lattice. | computer science |
9,799 | Bridging the Gap between Reinforcement Learning and Knowledge
Representation: A Logical Off- and On-Policy Framework | cs.AI | Knowledge Representation is important issue in reinforcement learning. In
this paper, we bridge the gap between reinforcement learning and knowledge
representation, by providing a rich knowledge representation framework, based
on normal logic programs with answer set semantics, that is capable of solving
model-free rei... | computer science |
9,800 | Inferring Disease and Gene Set Associations with Rank Coherence in
Networks | cs.AI | A computational challenge to validate the candidate disease genes identified
in a high-throughput genomic study is to elucidate the associations between the
set of candidate genes and disease phenotypes. The conventional gene set
enrichment analysis often fails to reveal associations between disease
phenotypes and the ... | computer science |
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