pwc-archive/papers-with-abstracts huggingface dataset:
A General Multi-Graph Matching Approach via Graduated Consistency-regularized Boosting
Authors: Junchi Yan, Minsu Cho, Hongyuan Zha, Xiaokang Yang, Stephen Chu
Date: 2015-02-20 00:00:00
Tasks: Graph Matching

This paper addresses the problem of matching $N$ weighted graphs referring to
an identical object or category. More specifically, matching the common node
correspondences among graphs. This multi-graph matching problem involves two
ingredients affecting the overall accuracy: i) the local pairwise matching
affinity score among graphs; ii) the global matching consistency that measures
the uniqueness of the pairwise matching results by different chaining orders.
Previous studies typically either enforce the matching consistency constraints
in the beginning of iterative optimization, which may propagate matching error
both over iterations and across graph pairs; or separate affinity optimizing
and consistency regularization in two steps. This paper is motivated by the
observation that matching consistency can serve as a regularizer in the
affinity objective function when the function is biased due to noises or
inappropriate modeling. We propose multi-graph matching methods to incorporate
the two aspects by boosting the affinity score, meanwhile gradually infusing
the consistency as a regularizer. Furthermore, we propose a node-wise
consistency/affinity-driven mechanism to elicit the common inlier nodes out of
the irrelevant outliers. Extensive results on both synthetic and public image
datasets demonstrate the competency of the proposed algorithms.
Automated Reasoning for Robot Ethics
Authors: Ulrich Furbach, Claudia Schon, Frieder Stolzenburg
Date: 2015-02-20 00:00:00
Tasks: Ethics, Legal Reasoning, Philosophy

Deontic logic is a very well researched branch of mathematical logic and
philosophy. Various kinds of deontic logics are considered for different
application domains like argumentation theory, legal reasoning, and acts in
multi-agent systems. In this paper, we show how standard deontic logic can be
used to model ethical codes for multi-agent systems. Furthermore we show how
Hyper, a high performance theorem prover, can be used to prove properties of
these ethical codes.
A provably convergent alternating minimization method for mean field inference
Authors: Pierre Baqué, Jean-Hubert Hours, François Fleuret, Pascal Fua
Date: 2015-02-20 00:00:00

Mean-Field is an efficient way to approximate a posterior distribution in
complex graphical models and constitutes the most popular class of Bayesian
variational approximation methods. In most applications, the mean field
distribution parameters are computed using an alternate coordinate
minimization. However, the convergence properties of this algorithm remain
unclear. In this paper, we show how, by adding an appropriate penalization
term, we can guarantee convergence to a critical point, while keeping a closed
form update at each step. A convergence rate estimate can also be derived based
on recent results in non-convex optimization.
Spike Event Based Learning in Neural Networks
Authors: James A. Henderson, TingTing A. Gibson, Janet Wiles
Date: 2015-02-20 00:00:00
Tasks: General Classification

A scheme is derived for learning connectivity in spiking neural networks. The
scheme learns instantaneous firing rates that are conditional on the activity
in other parts of the network. The scheme is independent of the choice of
neuron dynamics or activation function, and network architecture. It involves
two simple, online, local learning rules that are applied only in response to
occurrences of spike events. This scheme provides a direct method for
transferring ideas between the fields of deep learning and computational
neuroscience. This learning scheme is demonstrated using a layered feedforward
spiking neural network trained self-supervised on a prediction and
classification task for moving MNIST images collected using a Dynamic Vision
Sensor.
Pairwise Constraint Propagation: A Survey
Authors: Zhenyong Fu, Zhiwu Lu
Date: 2015-02-19 00:00:00
Tasks: Survey

As one of the most important types of (weaker) supervised information in
machine learning and pattern recognition, pairwise constraint, which specifies
whether a pair of data points occur together, has recently received significant
attention, especially the problem of pairwise constraint propagation. At least
two reasons account for this trend: the first is that compared to the data
label, pairwise constraints are more general and easily to collect, and the
second is that since the available pairwise constraints are usually limited,
the constraint propagation problem is thus important.
  This paper provides an up-to-date critical survey of pairwise constraint
propagation research. There are two underlying motivations for us to write this
survey paper: the first is to provide an up-to-date review of the existing
literature, and the second is to offer some insights into the studies of
pairwise constraint propagation. To provide a comprehensive survey, we not only
categorize existing propagation techniques but also present detailed
descriptions of representative methods within each category.
A New Sampling Technique for Tensors
Authors: Srinadh Bhojanapalli, Sujay Sanghavi
Date: 2015-02-17 00:00:00

In this paper we propose new techniques to sample arbitrary third-order
tensors, with an objective of speeding up tensor algorithms that have recently
gained popularity in machine learning. Our main contribution is a new way to
select, in a biased random way, only $O(n^{1.5}/\epsilon^2)$ of the possible
$n^3$ elements while still achieving each of the three goals: \\ {\em (a)
tensor sparsification}: for a tensor that has to be formed from arbitrary
samples, compute very few elements to get a good spectral approximation, and
for arbitrary orthogonal tensors {\em (b) tensor completion:} recover an
exactly low-rank tensor from a small number of samples via alternating least
squares, or {\em (c) tensor factorization:} approximating factors of a low-rank
tensor corrupted by noise. \\ Our sampling can be used along with existing
tensor-based algorithms to speed them up, removing the computational bottleneck
in these methods.
Gray-Level Image Transitions Driven by Tsallis Entropic Index
Authors: Amelia Carolina Sparavigna
Date: 2015-02-14 00:00:00

The maximum entropy principle is largely used in thresholding and
segmentation of images. Among the several formulations of this principle, the
most effectively applied is that based on Tsallis non-extensive entropy. Here,
we discuss the role of its entropic index in determining the thresholds. When
this index is spanning the interval (0,1), for some images, the values of
thresholds can have large leaps. In this manner, we observe abrupt transitions
in the appearance of corresponding bi-level or multi-level images. These
gray-level image transitions are analogous to order or texture transitions
observed in physical systems, transitions which are driven by the temperature
or by other physical quantities.
Forgetting and consolidation for incremental and cumulative knowledge acquisition systems
Authors: Fernando Martínez-Plumed, Cèsar Ferri, José Hernández-Orallo, María José Ramírez-Quintana
Date: 2015-02-19 00:00:00

The application of cognitive mechanisms to support knowledge acquisition is,
from our point of view, crucial for making the resulting models coherent,
efficient, credible, easy to use and understandable. In particular, there are
two characteristic features of intelligence that are essential for knowledge
development: forgetting and consolidation. Both plays an important role in
knowledge bases and learning systems to avoid possible information overflow and
redundancy, and in order to preserve and strengthen important or frequently
used rules and remove (or forget) useless ones. We present an incremental,
long-life view of knowledge acquisition which tries to improve task after task
by determining what to keep, what to consolidate and what to forget, overcoming
The Stability-Plasticity dilemma. In order to do that, we rate rules by
introducing several metrics through the first adaptation, to our knowledge, of
the Minimum Message Length (MML) principle to a coverage graph, a hierarchical
assessment structure which treats evidence and rules in a unified way. The
metrics are not only used to forget some of the worst rules, but also to set a
consolidation process to promote those selected rules to the knowledge base,
which is also mirrored by a demotion system. We evaluate the framework with a
series of tasks in a chess rule learning domain.
Learning to Execute
Authors: Wojciech Zaremba, Ilya Sutskever
Date: 2014-10-17 00:00:00
Tasks: Learning to Execute

Recurrent Neural Networks (RNNs) with Long Short-Term Memory units (LSTM) are
widely used because they are expressive and are easy to train. Our interest
lies in empirically evaluating the expressiveness and the learnability of LSTMs
in the sequence-to-sequence regime by training them to evaluate short computer
programs, a domain that has traditionally been seen as too complex for neural
networks. We consider a simple class of programs that can be evaluated with a
single left-to-right pass using constant memory. Our main result is that LSTMs
can learn to map the character-level representations of such programs to their
correct outputs. Notably, it was necessary to use curriculum learning, and
while conventional curriculum learning proved ineffective, we developed a new
variant of curriculum learning that improved our networks' performance in all
experimental conditions. The improved curriculum had a dramatic impact on an
addition problem, making it possible to train an LSTM to add two 9-digit
numbers with 99% accuracy.
Recurrent Neural Network Regularization
Authors: Wojciech Zaremba, Ilya Sutskever, Oriol Vinyals
Date: 2014-09-08 00:00:00
Tasks: Caption Generation, Image Captioning, Language Modeling, Language Modelling, Machine Translation, Speech Recognition, Translation

We present a simple regularization technique for Recurrent Neural Networks
(RNNs) with Long Short-Term Memory (LSTM) units. Dropout, the most successful
technique for regularizing neural networks, does not work well with RNNs and
LSTMs. In this paper, we show how to correctly apply dropout to LSTMs, and show
that it substantially reduces overfitting on a variety of tasks. These tasks
include language modeling, speech recognition, image caption generation, and
machine translation.
Finding Dantzig selectors with a proximity operator based fixed-point algorithm
Authors: Ashley Prater, Lixin Shen, Bruce W. Suter
Date: 2015-02-19 00:00:00
Tasks: regression

In this paper, we study a simple iterative method for finding the Dantzig
selector, which was designed for linear regression problems. The method
consists of two main stages. The first stage is to approximate the Dantzig
selector through a fixed-point formulation of solutions to the Dantzig selector
problem. The second stage is to construct a new estimator by regressing data
onto the support of the approximated Dantzig selector. We compare our method to
an alternating direction method, and present the results of numerical
simulations using both the proposed method and the alternating direction method
on synthetic and real data sets. The numerical simulations demonstrate that the
two methods produce results of similar quality, however the proposed method
tends to be significantly faster.
Multi-valued Color Representation Based on Frank t-norm Properties
Authors: Vasile Patrascu
Date: 2015-02-19 00:00:00

In this paper two knowledge representation models are proposed, FP4 and FP6.
Both combine ideas from fuzzy sets and four-valued and hexa-valued logics. Both
represent imprecise properties whose accomplished degree is unknown or
contradictory for some objects. A possible application in the color analysis
and color image processing is discussed.
A New Penta-valued Logic Based Knowledge Representation
Authors: Vasile Patrascu
Date: 2015-02-19 00:00:00
Tasks: Negation

In this paper a knowledge representation model are proposed, FP5, which
combine the ideas from fuzzy sets and penta-valued logic. FP5 represents
imprecise properties whose accomplished degree is undefined, contradictory or
indeterminate for some objects. Basic operations of conjunction, disjunction
and negation are introduced. Relations to other representation models like
fuzzy sets, intuitionistic, paraconsistent and bipolar fuzzy sets are
discussed.
NeuroSVM: A Graphical User Interface for Identification of Liver Patients
Authors: Kalyan Nagaraj, Amulyashree Sridhar
Date: 2015-02-19 00:00:00
Tasks: Classification, General Classification, Prediction

Diagnosis of liver infection at preliminary stage is important for better
treatment. In todays scenario devices like sensors are used for detection of
infections. Accurate classification techniques are required for automatic
identification of disease samples. In this context, this study utilizes data
mining approaches for classification of liver patients from healthy
individuals. Four algorithms (Naive Bayes, Bagging, Random forest and SVM) were
implemented for classification using R platform. Further to improve the
accuracy of classification a hybrid NeuroSVM model was developed using SVM and
feed-forward artificial neural network (ANN). The hybrid model was tested for
its performance using statistical parameters like root mean square error (RMSE)
and mean absolute percentage error (MAPE). The model resulted in a prediction
accuracy of 98.83%. The results suggested that development of hybrid model
improved the accuracy of prediction. To serve the medicinal community for
prediction of liver disease among patients, a graphical user interface (GUI)
has been developed using R. The GUI is deployed as a package in local
repository of R platform for users to perform prediction.
Classification and Bayesian Optimization for Likelihood-Free Inference
Authors: Michael U. Gutmann, Jukka Corander, Ritabrata Dutta, Samuel Kaski
Date: 2015-02-19 00:00:00
Tasks: Bayesian Optimization, Classification, General Classification

Some statistical models are specified via a data generating process for which
the likelihood function cannot be computed in closed form. Standard
likelihood-based inference is then not feasible but the model parameters can be
inferred by finding the values which yield simulated data that resemble the
observed data. This approach faces at least two major difficulties: The first
difficulty is the choice of the discrepancy measure which is used to judge
whether the simulated data resemble the observed data. The second difficulty is
the computationally efficient identification of regions in the parameter space
where the discrepancy is low. We give here an introduction to our recent work
where we tackle the two difficulties through classification and Bayesian
optimization.
Screen Content Image Segmentation Using Least Absolute Deviation Fitting
Authors: Shervin Minaee, Yao Wang
Date: 2015-01-15 00:00:00
Tasks: Clustering, Foreground Segmentation, Image Segmentation, Segmentation, Semantic Segmentation

We propose an algorithm for separating the foreground (mainly text and line
graphics) from the smoothly varying background in screen content images. The
proposed method is designed based on the assumption that the background part of
the image is smoothly varying and can be represented by a linear combination of
a few smoothly varying basis functions, while the foreground text and graphics
create sharp discontinuity and cannot be modeled by this smooth representation.
The algorithm separates the background and foreground using a least absolute
deviation method to fit the smooth model to the image pixels. This algorithm
has been tested on several images from HEVC standard test sequences for screen
content coding, and is shown to have superior performance over other popular
methods, such as k-means clustering based segmentation in DjVu and shape
primitive extraction and coding (SPEC) algorithm. Such background/foreground
segmentation are important pre-processing steps for text extraction and
separate coding of background and foreground for compression of screen content
images.
Visualizing Object Detection Features
Authors: Carl Vondrick, Aditya Khosla, Hamed Pirsiavash, Tomasz Malisiewicz, Antonio Torralba
Date: 2015-02-19 00:00:00
Tasks: Object, object-detection, Object Detection

We introduce algorithms to visualize feature spaces used by object detectors.
Our method works by inverting a visual feature back to multiple natural images.
We found that these visualizations allow us to analyze object detection systems
in new ways and gain new insight into the detector's failures. For example,
when we visualize the features for high scoring false alarms, we discovered
that, although they are clearly wrong in image space, they do look deceptively
similar to true positives in feature space. This result suggests that many of
these false alarms are caused by our choice of feature space, and supports that
creating a better learning algorithm or building bigger datasets is unlikely to
correct these errors. By visualizing feature spaces, we can gain a more
intuitive understanding of recognition systems.
Does Learning Imply a Decrease in the Entropy of Behavior?
Authors: Paul E. Smaldino
Date: 2015-01-18 00:00:00
Tasks: Navigate, valid

Shannon's information entropy measures of the uncertainty of an event's
outcome. If learning about a system reflects a decrease in uncertainty, then a
plausible intuition is that learning should be accompanied by a decrease in the
entropy of the organism's actions and/or perceptual states. To address whether
this intuition is valid, I examined an artificial organism -- a simple robot --
that learned to navigate in an arena and analyzed the entropy of the outcome
variables action, state, and reward. Entropy did indeed decrease in the initial
stages of learning, but two factors complicated the scenario: (1) the
introduction of new options discovered during the learning process and (2) the
shifting patterns of perceptual and environmental states resulting from changes
to the robot's learned movement strategies. These factors lead to a subsequent
increase in entropy as the agent learned. I end with a discussion of the
utility of information-based characterizations of learning.
The (Final) countdown
Authors: Jean-Marc Alliot
Date: 2015-02-19 00:00:00

The Countdown game is one of the oldest TV show running in the world. It
started broadcasting in 1972 on the french television and in 1982 on British
channel 4, and it has been running since in both countries. The game, while
extremely popular, never received any serious scientific attention, probably
because it seems too simple at first sight. We present in this article an
in-depth analysis of the numbers round of the countdown game. This includes a
complexity analysis of the game, an analysis of existing algorithms, the
presentation of a new algorithm that increases resolution speed by a factor of
20. It also includes some leads on how to turn the game into a more difficult
one, both for a human player and for a computer, and even to transform it into
a probably undecidable problem.
Rule-and Dictionary-based Solution for Variations in Written Arabic Names in Social Networks, Big Data, Accounting Systems and Large Databases
Authors: Ahmad B. A. Hassanat, Ghada Awad Altarawneh
Date: 2015-02-18 00:00:00

This paper investigates the problem that some Arabic names can be written in
multiple ways. When someone searches for only one form of a name, neither exact
nor approximate matching is appropriate for returning the multiple variants of
the name. Exact matching requires the user to enter all forms of the name for
the search, and approximate matching yields names not among the variations of
the one being sought. In this paper, we attempt to solve the problem with a
dictionary of all Arabic names mapped to their different (alternative) writing
forms. We generated alternatives based on rules we derived from reviewing the
first names of 9.9 million citizens and former citizens of Jordan. This
dictionary can be used for both standardizing the written form when inserting a
new name into a database and for searching for the name and all its alternative
written forms. Creating the dictionary automatically based on rules resulted in
at least 7% erroneous acceptance errors and 7.9% erroneous rejection errors. We
addressed the errors by manually editing the dictionary. The dictionary can be
of help to real world-databases, with the qualification that manual editing
does not guarantee 100% correctness.
Fusion of Image Segmentation Algorithms using Consensus Clustering
Authors: Mete Ozay, Fatos T. Yarman Vural, Sanjeev R. Kulkarni, H. Vincent Poor
Date: 2015-02-18 00:00:00
Tasks: Clustering, Image Segmentation, Segmentation, Semantic Segmentation, Stochastic Optimization

A new segmentation fusion method is proposed that ensembles the output of
several segmentation algorithms applied on a remotely sensed image. The
candidate segmentation sets are processed to achieve a consensus segmentation
using a stochastic optimization algorithm based on the Filtered Stochastic BOEM
(Best One Element Move) method. For this purpose, Filtered Stochastic BOEM is
reformulated as a segmentation fusion problem by designing a new distance
learning approach. The proposed algorithm also embeds the computation of the
optimum number of clusters into the segmentation fusion problem.
On learning k-parities with and without noise
Authors: Arnab Bhattacharyya, Ameet Gadekar, Ninad Rajgopal
Date: 2015-02-18 00:00:00
Tasks: Learning Theory

We first consider the problem of learning $k$-parities in the on-line
mistake-bound model: given a hidden vector $x \in \{0,1\}^n$ with $|x|=k$ and a
sequence of "questions" $a_1, a_2, ...\in \{0,1\}^n$, where the algorithm must
reply to each question with $< a_i, x> \pmod 2$, what is the best tradeoff
between the number of mistakes made by the algorithm and its time complexity?
We improve the previous best result of Buhrman et al. by an $\exp(k)$ factor in
the time complexity.
  Second, we consider the problem of learning $k$-parities in the presence of
classification noise of rate $\eta \in (0,1/2)$. A polynomial time algorithm
for this problem (when $\eta > 0$ and $k = \omega(1)$) is a longstanding
challenge in learning theory. Grigorescu et al. showed an algorithm running in
time ${n \choose k/2}^{1 + 4\eta^2 +o(1)}$. Note that this algorithm inherently
requires time ${n \choose k/2}$ even when the noise rate $\eta$ is polynomially
small. We observe that for sufficiently small noise rate, it is possible to
break the $n \choose k/2$ barrier. In particular, if for some function $f(n) =
\omega(1)$ and $\alpha \in [1/2, 1)$, $k = n/f(n)$ and $\eta = o(f(n)^{-
\alpha}/\log n)$, then there is an algorithm for the problem with running time
$poly(n)\cdot {n \choose k}^{1-\alpha} \cdot e^{-k/4.01}$.
A Clustering Analysis of Tweet Length and its Relation to Sentiment
Authors: Matthew Mayo
Date: 2014-06-12 00:00:00
Tasks: Clustering, Relation, Sentiment Analysis

Sentiment analysis of Twitter data is performed. The researcher has made the
following contributions via this paper: (1) an innovative method for deriving
sentiment score dictionaries using an existing sentiment dictionary as seed
words is explored, and (2) an analysis of clustered tweet sentiment scores
based on tweet length is performed.