pwc-archive/papers-with-abstracts huggingface dataset:
Computabilities of Validity and Satisfiability in Probability Logics over Finite and Countable Models
Authors: Greg Yang
Date: 2014-10-12 00:00:00
Tasks: Learning Theory

The $\epsilon$-logic (which is called $\epsilon$E-logic in this paper) of
Kuyper and Terwijn is a variant of first order logic with the same syntax, in
which the models are equipped with probability measures and in which the
$\forall x$ quantifier is interpreted as "there exists a set $A$ of measure
$\ge 1 - \epsilon$ such that for each $x \in A$, ...." Previously, Kuyper and
Terwijn proved that the general satisfiability and validity problems for this
logic are, i) for rational $\epsilon \in (0, 1)$, respectively
$\Sigma^1_1$-complete and $\Pi^1_1$-hard, and ii) for $\epsilon = 0$,
respectively decidable and $\Sigma^0_1$-complete. The adjective "general" here
means "uniformly over all languages."
  We extend these results in the scenario of finite models. In particular, we
show that the problems of satisfiability by and validity over finite models in
$\epsilon$E-logic are, i) for rational $\epsilon \in (0, 1)$, respectively
$\Sigma^0_1$- and $\Pi^0_1$-complete, and ii) for $\epsilon = 0$, respectively
decidable and $\Pi^0_1$-complete. Although partial results toward the countable
case are also achieved, the computability of $\epsilon$E-logic over countable
models still remains largely unsolved. In addition, most of the results, of
this paper and of Kuyper and Terwijn, do not apply to individual languages with
a finite number of unary predicates. Reducing this requirement continues to be
a major point of research.
  On the positive side, we derive the decidability of the corresponding
problems for monadic relational languages --- equality- and function-free
languages with finitely many unary and zero other predicates. This result holds
for all three of the unrestricted, the countable, and the finite model cases.
  Applications in computational learning theory, weighted graphs, and neural
networks are discussed in the context of these decidability and undecidability
results.
Using Sentence Plausibility to Learn the Semantics of Transitive Verbs
Authors: Tamara Polajnar, Laura Rimell, Stephen Clark
Date: 2014-11-28 00:00:00
Tasks: regression, Sentence

The functional approach to compositional distributional semantics considers
transitive verbs to be linear maps that transform the distributional vectors
representing nouns into a vector representing a sentence. We conduct an initial
investigation that uses a matrix consisting of the parameters of a logistic
regression classifier trained on a plausibility task as a transitive verb
function. We compare our method to a commonly used corpus-based method for
constructing a verb matrix and find that the plausibility training may be more
effective for disambiguation tasks.
An Automatic Seeded Region Growing for 2D Biomedical Image Segmentation
Authors: Mohammed M. Abdelsamea
Date: 2014-12-12 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

In this paper, an automatic seeded region growing algorithm is proposed for
cellular image segmentation. First, the regions of interest (ROIs) extracted
from the preprocessed image. Second, the initial seeds are automatically
selected based on ROIs extracted from the image. Third, the most reprehensive
seeds are selected using a machine learning algorithm. Finally, the cellular
image is segmented into regions where each region corresponds to a seed. The
aim of the proposed is to automatically extract the Region of Interests (ROI)
from the cellular images in terms of overcoming the explosion, under
segmentation and over segmentation problems. Experimental results show that the
proposed algorithm can improve the segmented image and the segmented results
are less noisy as compared to some existing algorithms.
CITlab ARGUS for historical handwritten documents
Authors: Tobias Strauß, Tobias Grüning, Gundram Leifert, Roger Labahn, for the University of Rostock - CITlab
Date: 2014-12-12 00:00:00
Tasks: General Classification, Handwriting Recognition

We describe CITlab's recognition system for the HTRtS competition attached to
the 14. International Conference on Frontiers in Handwriting Recognition, ICFHR
2014. The task comprises the recognition of historical handwritten documents.
The core algorithms of our system are based on multi-dimensional recurrent
neural networks (MDRNN) and connectionist temporal classification (CTC). The
software modules behind that as well as the basic utility technologies are
essentially powered by PLANET's ARGUS framework for intelligent text
recognition and image processing.
Sparse Graph-based Transduction for Image Classification
Authors: Sheng Huang, Dan Yang, Jia Zhou, Luwen Huangfu, Xiaohong Zhang
Date: 2014-08-26 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

Motivated by the remarkable successes of Graph-based Transduction (GT) and
Sparse Representation (SR), we present a novel Classifier named Sparse
Graph-based Classifier (SGC) for image classification. In SGC, SR is leveraged
to measure the correlation (similarity) of each two samples and a graph is
constructed for encoding these correlations. Then the Laplacian eigenmapping is
adopted for deriving the graph Laplacian of the graph. Finally, SGC can be
obtained by plugging the graph Laplacian into the conventional GT framework. In
the image classification procedure, SGC utilizes the correlations, which are
encoded in the learned graph Laplacian, to infer the labels of unlabeled
images. SGC inherits the merits of both GT and SR. Compared to SR, SGC improves
the robustness and the discriminating power of GT. Compared to GT, SGC
sufficiently exploits the whole data. Therefore it alleviates the undercomplete
dictionary issue suffered by SR. Four popular image databases are employed for
evaluation. The results demonstrate that SGC can achieve a promising
performance in comparison with the state-of-the-art classifiers, particularly
in the small training sample size case and the noisy sample case.
Region segmentation for sparse decompositions: better brain parcellations from rest fMRI
Authors: Alexandre Abraham, Elvis Dohmatob, Bertrand Thirion, Dimitris Samaras, Gael Varoquaux
Date: 2014-12-12 00:00:00

Functional Magnetic Resonance Images acquired during resting-state provide
information about the functional organization of the brain through measuring
correlations between brain areas. Independent components analysis is the
reference approach to estimate spatial components from weakly structured data
such as brain signal time courses; each of these components may be referred to
as a brain network and the whole set of components can be conceptualized as a
brain functional atlas. Recently, new methods using a sparsity prior have
emerged to deal with low signal-to-noise ratio data. However, even when using
sophisticated priors, the results may not be very sparse and most often do not
separate the spatial components into brain regions. This work presents
post-processing techniques that automatically sparsify brain maps and separate
regions properly using geometric operations, and compares these techniques
according to faithfulness to data and stability metrics. In particular, among
threshold-based approaches, hysteresis thresholding and random walker
segmentation, the latter improves significantly the stability of both dense and
sparse models.
Size sensitive packing number for Hamming cube and its consequences
Authors: Kunal Dutta, Arijit Ghosh
Date: 2014-12-12 00:00:00
Tasks: LEMMA

We prove a size-sensitive version of Haussler's Packing
lemma~\cite{Haussler92spherepacking} for set-systems with bounded primal
shatter dimension, which have an additional {\em size-sensitive property}. This
answers a question asked by Ezra~\cite{Ezra-sizesendisc-soda-14}. We also
partially address another point raised by Ezra regarding overcounting of sets
in her chaining procedure. As a consequence of these improvements, we get an
improvement on the size-sensitive discrepancy bounds for set systems with the
above property. Improved bounds on the discrepancy for these special set
systems also imply an improvement in the sizes of {\em relative $(\varepsilon,
\delta)$-approximations} and $(\nu, \alpha)$-samples.
Machine Learning for Neuroimaging with Scikit-Learn
Authors: Alexandre Abraham, Fabian Pedregosa, Michael Eickenberg, Philippe Gervais, Andreas Muller, Jean Kossaifi, Alexandre Gramfort, Bertrand Thirion, Gäel Varoquaux
Date: 2014-12-12 00:00:00
Tasks: BIG-bench Machine Learning, Time Series, Time Series Analysis

Statistical machine learning methods are increasingly used for neuroimaging
data analysis. Their main virtue is their ability to model high-dimensional
datasets, e.g. multivariate analysis of activation images or resting-state time
series. Supervised learning is typically used in decoding or encoding settings
to relate brain images to behavioral or clinical observations, while
unsupervised learning can uncover hidden structures in sets of images (e.g.
resting state functional MRI) or find sub-populations in large cohorts. By
considering different functional neuroimaging applications, we illustrate how
scikit-learn, a Python machine learning library, can be used to perform some
key analysis steps. Scikit-learn contains a very large set of statistical
learning algorithms, both supervised and unsupervised, and its application to
neuroimaging data provides a versatile tool to study the brain.
Edge Preserving Multi-Modal Registration Based On Gradient Intensity Self-Similarity
Authors: Tamar Rott, Dorin Shriki, Tamir Bendory
Date: 2014-12-12 00:00:00
Tasks: Image Registration

Image registration is a challenging task in the world of medical imaging.
Particularly, accurate edge registration plays a central role in a variety of
clinical conditions. The Modality Independent Neighbourhood Descriptor (MIND)
demonstrates state of the art alignment, based on the image self-similarity.
However, this method appears to be less accurate regarding edge registration.
In this work, we propose a new registration method, incorporating gradient
intensity and MIND self-similarity metric. Experimental results show the
superiority of this method in edge registration tasks, while preserving the
original MIND performance for other image features and textures.
Belief revision in the propositional closure of a qualitative algebra
Authors: Valmi Dufour-Lussier, Alice Hermann, Florence Le Ber, Jean Lieber
Date: 2014-12-12 00:00:00

Belief revision is an operation that aims at modifying old be-liefs so that
they become consistent with new ones. The issue of belief revision has been
studied in various formalisms, in particular, in qualitative algebras (QAs) in
which the result is a disjunction of belief bases that is not necessarily
repre-sentable in a QA. This motivates the study of belief revision in
formalisms extending QAs, namely, their propositional clo-sures: in such a
closure, the result of belief revision belongs to the formalism. Moreover, this
makes it possible to define a contraction operator thanks to the Harper
identity. Belief revision in the propositional closure of QAs is studied, an
al-gorithm for a family of revision operators is designed, and an open-source
implementation is made freely available on the web.
Score Function Features for Discriminative Learning: Matrix and Tensor Framework
Authors: Majid Janzamin, Hanie Sedghi, Anima Anandkumar
Date: 2014-12-09 00:00:00

Feature learning forms the cornerstone for tackling challenging learning
problems in domains such as speech, computer vision and natural language
processing. In this paper, we consider a novel class of matrix and
tensor-valued features, which can be pre-trained using unlabeled samples. We
present efficient algorithms for extracting discriminative information, given
these pre-trained features and labeled samples for any related task. Our class
of features are based on higher-order score functions, which capture local
variations in the probability density function of the input. We establish a
theoretical framework to characterize the nature of discriminative information
that can be extracted from score-function features, when used in conjunction
with labeled samples. We employ efficient spectral decomposition algorithms (on
matrices and tensors) for extracting discriminative components. The advantage
of employing tensor-valued features is that we can extract richer
discriminative information in the form of an overcomplete representations.
Thus, we present a novel framework for employing generative models of the input
for discriminative learning.
Simulating a perceptron on a quantum computer
Authors: Maria Schuld, Ilya Sinayskiy, Francesco Petruccione
Date: 2014-12-11 00:00:00
Tasks: BIG-bench Machine Learning, Quantum Machine Learning

Perceptrons are the basic computational unit of artificial neural networks,
as they model the activation mechanism of an output neuron due to incoming
signals from its neighbours. As linear classifiers, they play an important role
in the foundations of machine learning. In the context of the emerging field of
quantum machine learning, several attempts have been made to develop a
corresponding unit using quantum information theory. Based on the quantum phase
estimation algorithm, this paper introduces a quantum perceptron model
imitating the step-activation function of a classical perceptron. This scheme
requires resources in $\mathcal{O}(n)$ (where $n$ is the size of the input) and
promises efficient applications for more complex structures such as trainable
quantum neural networks.
Efficient penalty search for multiple changepoint problems
Authors: Kaylea Haynes, Idris A. Eckley, Paul Fearnhead
Date: 2014-12-11 00:00:00

In the multiple changepoint setting, various search methods have been
proposed which involve optimising either a constrained or penalised cost
function over possible numbers and locations of changepoints using dynamic
programming. Such methods are typically computationally intensive. Recent work
in the penalised optimisation setting has focussed on developing a
pruning-based approach which gives an improved computational cost that, under
certain conditions, is linear in the number of data points. Such an approach
naturally requires the specification of a penalty to avoid under/over-fitting.
Work has been undertaken to identify the appropriate penalty choice for data
generating processes with known distributional form, but in many applications
the model assumed for the data is not correct and these penalty choices are not
always appropriate. Consequently it is desirable to have an approach that
enables us to compare segmentations for different choices of penalty. To this
end we present a method to obtain optimal changepoint segmentations of data
sequences for all penalty values across a continuous range. This permits an
evaluation of the various segmentations to identify a suitably parsimonious
penalty choice. The computational complexity of this approach can be linear in
the number of data points and linear in the difference between the number of
changepoints in the optimal segmentations for the smallest and largest penalty
values. This can be orders of magnitude faster than alternative approaches that
find optimal segmentations for a range of the number of changepoints.
High-level numerical simulations of noise in CCD and CMOS photosensors: review and tutorial
Authors: Mikhail Konnik, James Welsh
Date: 2014-12-11 00:00:00

In many applications, such as development and testing of image processing
algorithms, it is often necessary to simulate images containing realistic noise
from solid-state photosensors. A high-level model of CCD and CMOS photosensors
based on a literature review is formulated in this paper. The model includes
photo-response non-uniformity, photon shot noise, dark current Fixed Pattern
Noise, dark current shot noise, offset Fixed Pattern Noise, source follower
noise, sense node reset noise, and quantisation noise. The model also includes
voltage-to-voltage, voltage-to-electrons, and analogue-to-digital converter
non-linearities. The formulated model can be used to create synthetic images
for testing and validation of image processing algorithms in the presence of
realistic images noise. An example of the simulated CMOS photosensor and a
comparison with a custom-made CMOS hardware sensor is presented. Procedures for
characterisation from both light and dark noises are described. Experimental
results that confirm the validity of the numerical model are provided. The
paper addresses the issue of the lack of comprehensive high-level photosensor
models that enable engineers to simulate realistic effects of noise on the
images obtained from solid-state photosensors.
Turing Test for the Internet of Things
Authors: Neil Rubens
Date: 2014-12-11 00:00:00

How smart is your kettle? How smart are things in your kitchen, your house,
your neighborhood, on the internet? With the advent of Internet of Things, and
the move of making devices `smart' by utilizing AI, a natural question arrises,
how can we evaluate the progress. The standard way of evaluating AI is through
the Turing Test. While Turing Test was designed for AI; the device that it was
tailored to was a computer. Applying the test to variety of devices that
constitute Internet of Things poses a number of challenges which could be
addressed through a number of adaptations.
Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
Authors: Junyoung Chung, Caglar Gulcehre, Kyunghyun Cho, Yoshua Bengio
Date: 2014-12-11 00:00:00
Tasks: Music Modeling

In this paper we compare different types of recurrent units in recurrent
neural networks (RNNs). Especially, we focus on more sophisticated units that
implement a gating mechanism, such as a long short-term memory (LSTM) unit and
a recently proposed gated recurrent unit (GRU). We evaluate these recurrent
units on the tasks of polyphonic music modeling and speech signal modeling. Our
experiments revealed that these advanced recurrent units are indeed better than
more traditional recurrent units such as tanh units. Also, we found GRU to be
comparable to LSTM.
Reinforcement Learning and Nonparametric Detection of Game-Theoretic Equilibrium Play in Social Networks
Authors: Omid Namvar Gharehshiran, William Hoiles, Vikram Krishnamurthy
Date: 2014-12-11 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper studies two important signal processing aspects of equilibrium
behavior in non-cooperative games arising in social networks, namely,
reinforcement learning and detection of equilibrium play. The first part of the
paper presents a reinforcement learning (adaptive filtering) algorithm that
facilitates learning an equilibrium by resorting to diffusion cooperation
strategies in a social network. Agents form homophilic social groups, within
which they exchange past experiences over an undirected graph. It is shown
that, if all agents follow the proposed algorithm, their global behavior is
attracted to the correlated equilibria set of the game. The second part of the
paper provides a test to detect if the actions of agents are consistent with
play from the equilibrium of a concave potential game. The theory of revealed
preference from microeconomics is used to construct a non-parametric decision
test and statistical test which only require the probe and associated actions
of agents. A stochastic gradient algorithm is given to optimize the probe in
real time to minimize the Type-II error probabilities of the detection test
subject to specified Type-I error probability. We provide a real-world example
using the energy market, and a numerical example to detect malicious agents in
an online social network.
The ROMES method for statistical modeling of reduced-order-model error
Authors: Martin Drohmann, Kevin Carlberg
Date: 2014-05-20 00:00:00

This work presents a technique for statistically modeling errors introduced
by reduced-order models. The method employs Gaussian-process regression to
construct a mapping from a small number of computationally inexpensive `error
indicators' to a distribution over the true error. The variance of this
distribution can be interpreted as the (epistemic) uncertainty introduced by
the reduced-order model. To model normed errors, the method employs existing
rigorous error bounds and residual norms as indicators; numerical experiments
show that the method leads to a near-optimal expected effectivity in contrast
to typical error bounds. To model errors in general outputs, the method uses
dual-weighted residuals---which are amenable to uncertainty control---as
indicators. Experiments illustrate that correcting the reduced-order-model
output with this surrogate can improve prediction accuracy by an order of
magnitude; this contrasts with existing `multifidelity correction' approaches,
which often fail for reduced-order models and suffer from the curse of
dimensionality. The proposed error surrogates also lead to a notion of
`probabilistic rigor', i.e., the surrogate bounds the error with specified
probability.
Deep Domain Confusion: Maximizing for Domain Invariance
Authors: Eric Tzeng, Judy Hoffman, Ning Zhang, Kate Saenko, Trevor Darrell
Date: 2014-12-10 00:00:00
Tasks: Domain Adaptation, Model Selection, Position

Recent reports suggest that a generic supervised deep CNN model trained on a
large-scale dataset reduces, but does not remove, dataset bias on a standard
benchmark. Fine-tuning deep models in a new domain can require a significant
amount of data, which for many applications is simply not available. We propose
a new CNN architecture which introduces an adaptation layer and an additional
domain confusion loss, to learn a representation that is both semantically
meaningful and domain invariant. We additionally show that a domain confusion
metric can be used for model selection to determine the dimension of an
adaptation layer and the best position for the layer in the CNN architecture.
Our proposed adaptation method offers empirical performance which exceeds
previously published results on a standard benchmark visual domain adaptation
task.
Complex support vector machines regression for robust channel estimation in LTE downlink system
Authors: Anis Charrada, Abdelaziz Samet
Date: 2014-12-10 00:00:00
Tasks: regression

In this paper, the problem of channel estimation for LTE Downlink system in
the environment of high mobility presenting non-Gaussian impulse noise
interfering with reference signals is faced. The estimation of the frequency
selective time varying multipath fading channel is performed by using a channel
estimator based on a nonlinear complex Support Vector Machine Regression (SVR)
which is applied to Long Term Evolution (LTE) downlink. The estimation
algorithm makes use of the pilot signals to estimate the total frequency
response of the highly selective fading multipath channel. Thus, the algorithm
maps trained data into a high dimensional feature space and uses the structural
risk minimization principle to carry out the regression estimation for the
frequency response function of the fading channel. The obtained results show
the effectiveness of the proposed method which has better performance than the
conventional Least Squares (LS) and Decision Feedback methods to track the
variations of the fading multipath channel.
Object Recognition Using Deep Neural Networks: A Survey
Authors: Soren Goyal, Paul Benjamin
Date: 2014-12-10 00:00:00
Tasks: Object, Object Recognition, Survey

Recognition of objects using Deep Neural Networks is an active area of
research and many breakthroughs have been made in the last few years. The paper
attempts to indicate how far this field has progressed. The paper briefly
describes the history of research in Neural Networks and describe several of
the recent advances in this field. The performances of recently developed
Neural Network Algorithm over benchmark datasets have been tabulated. Finally,
some the applications of this field have been provided.