pwc-archive/papers-with-abstracts huggingface dataset:
Discovering beautiful attributes for aesthetic image analysis
Authors: Luca Marchesotti, Naila Murray, Florent Perronnin
Date: 2014-12-16 00:00:00
Tasks: Retrieval

Aesthetic image analysis is the study and assessment of the aesthetic
properties of images. Current computational approaches to aesthetic image
analysis either provide accurate or interpretable results. To obtain both
accuracy and interpretability by humans, we advocate the use of learned and
nameable visual attributes as mid-level features. For this purpose, we propose
to discover and learn the visual appearance of attributes automatically, using
a recently introduced database, called AVA, which contains more than 250,000
images together with their aesthetic scores and textual comments given by
photography enthusiasts. We provide a detailed analysis of these annotations as
well as the context in which they were given. We then describe how these three
key components of AVA - images, scores, and comments - can be effectively
leveraged to learn visual attributes. Lastly, we show that these learned
attributes can be successfully used in three applications: aesthetic quality
prediction, image tagging and retrieval.
SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives
Authors: Aaron Defazio, Francis Bach, Simon Lacoste-Julien
Date: 2014-07-01 00:00:00
Proceeding: NeurIPS 2014 12

In this work we introduce a new optimisation method called SAGA in the spirit
of SAG, SDCA, MISO and SVRG, a set of recently proposed incremental gradient
algorithms with fast linear convergence rates. SAGA improves on the theory
behind SAG and SVRG, with better theoretical convergence rates, and has support
for composite objectives where a proximal operator is used on the regulariser.
Unlike SDCA, SAGA supports non-strongly convex problems directly, and is
adaptive to any inherent strong convexity of the problem. We give experimental
results showing the effectiveness of our method.
Asynchronous Adaptation and Learning over Networks - Part III: Comparison Analysis
Authors: Xiaochuan Zhao, Ali H. Sayed
Date: 2013-12-19 00:00:00

In Part II [3] we carried out a detailed mean-square-error analysis of the
performance of asynchronous adaptation and learning over networks under a
fairly general model for asynchronous events including random topologies,
random link failures, random data arrival times, and agents turning on and off
randomly. In this Part III, we compare the performance of synchronous and
asynchronous networks. We also compare the performance of decentralized
adaptation against centralized stochastic-gradient (batch) solutions. Two
interesting conclusions stand out. First, the results establish that the
performance of adaptive networks is largely immune to the effect of
asynchronous events: the mean and mean-square convergence rates and the
asymptotic bias values are not degraded relative to synchronous or centralized
implementations. Only the steady-state mean-square-deviation suffers a
degradation in the order of $\nu$, which represents the small step-size
parameters used for adaptation. Second, the results show that the adaptive
distributed network matches the performance of the centralized solution. These
conclusions highlight another critical benefit of cooperation by networked
agents: cooperation does not only enhance performance in comparison to
stand-alone single-agent processing, but it also endows the network with
remarkable resilience to various forms of random failure events and is able to
deliver performance that is as powerful as batch solutions.
Asynchronous Adaptation and Learning over Networks - Part II: Performance Analysis
Authors: Xiaochuan Zhao, Ali H. Sayed
Date: 2013-12-19 00:00:00
Tasks: Distributed Optimization

In Part I \cite{Zhao13TSPasync1}, we introduced a fairly general model for
asynchronous events over adaptive networks including random topologies, random
link failures, random data arrival times, and agents turning on and off
randomly. We performed a stability analysis and established the notable fact
that the network is still able to converge in the mean-square-error sense to
the desired solution. Once stable behavior is guaranteed, it becomes important
to evaluate how fast the iterates converge and how close they get to the
optimal solution. This is a demanding task due to the various asynchronous
events and due to the fact that agents influence each other. In this Part II,
we carry out a detailed analysis of the mean-square-error performance of
asynchronous strategies for solving distributed optimization and adaptation
problems over networks. We derive analytical expressions for the mean-square
convergence rate and the steady-state mean-square-deviation. The expressions
reveal how the various parameters of the asynchronous behavior influence
network performance. In the process, we establish the interesting conclusion
that even under the influence of asynchronous events, all agents in the
adaptive network can still reach an $O(\nu^{1 + \gamma_o'})$ near-agreement
with some $\gamma_o' > 0$ while approaching the desired solution within
$O(\nu)$ accuracy, where $\nu$ is proportional to the small step-size parameter
for adaptation.
Asynchronous Adaptation and Learning over Networks --- Part I: Modeling and Stability Analysis
Authors: Xiaochuan Zhao, Ali H. Sayed
Date: 2013-12-19 00:00:00
Tasks: Distributed Optimization

In this work and the supporting Parts II [2] and III [3], we provide a rather
detailed analysis of the stability and performance of asynchronous strategies
for solving distributed optimization and adaptation problems over networks. We
examine asynchronous networks that are subject to fairly general sources of
uncertainties, such as changing topologies, random link failures, random data
arrival times, and agents turning on and off randomly. Under this model, agents
in the network may stop updating their solutions or may stop sending or
receiving information in a random manner and without coordination with other
agents. We establish in Part I conditions on the first and second-order moments
of the relevant parameter distributions to ensure mean-square stable behavior.
We derive in Part II expressions that reveal how the various parameters of the
asynchronous behavior influence network performance. We compare in Part III the
performance of asynchronous networks to the performance of both centralized
solutions and synchronous networks. One notable conclusion is that the
mean-square-error performance of asynchronous networks shows a degradation only
of the order of $O(\nu)$, where $\nu$ is a small step-size parameter, while the
convergence rate remains largely unaltered. The results provide a solid
justification for the remarkable resilience of cooperative networks in the face
of random failures at multiple levels: agents, links, data arrivals, and
topology.
Combining the Best of Graphical Models and ConvNets for Semantic Segmentation
Authors: Michael Cogswell, Xiao Lin, Senthil Purushwalkam, Dhruv Batra
Date: 2014-12-14 00:00:00
Tasks: Segmentation, Semantic Segmentation

We present a two-module approach to semantic segmentation that incorporates
Convolutional Networks (CNNs) and Graphical Models. Graphical models are used
to generate a small (5-30) set of diverse segmentations proposals, such that
this set has high recall. Since the number of required proposals is so low, we
can extract fairly complex features to rank them. Our complex feature of choice
is a novel CNN called SegNet, which directly outputs a (coarse) semantic
segmentation. Importantly, SegNet is specifically trained to optimize the
corpus-level PASCAL IOU loss function. To the best of our knowledge, this is
the first CNN specifically designed for semantic segmentation. This two-module
approach achieves $52.5\%$ on the PASCAL 2012 segmentation challenge.
A Morphological Analyzer for Japanese Nouns, Verbs and Adjectives
Authors: Yanchuan Sim
Date: 2014-10-01 00:00:00
Tasks: General Classification

We present an open source morphological analyzer for Japanese nouns, verbs
and adjectives. The system builds upon the morphological analyzing capabilities
of MeCab to incorporate finer details of classification such as politeness,
tense, mood and voice attributes. We implemented our analyzer in the form of a
finite state transducer using the open source finite state compiler FOMA
toolkit. The source code and tool is available at
https://bitbucket.org/skylander/yc-nlplab/.
Learning with Pseudo-Ensembles
Authors: Philip Bachman, Ouais Alsharif, Doina Precup
Date: 2014-12-16 00:00:00
Proceeding: NeurIPS 2014 12
Tasks: Sentiment Analysis

We formalize the notion of a pseudo-ensemble, a (possibly infinite)
collection of child models spawned from a parent model by perturbing it
according to some noise process. E.g., dropout (Hinton et. al, 2012) in a deep
neural network trains a pseudo-ensemble of child subnetworks generated by
randomly masking nodes in the parent network. We present a novel regularizer
based on making the behavior of a pseudo-ensemble robust with respect to the
noise process generating it. In the fully-supervised setting, our regularizer
matches the performance of dropout. But, unlike dropout, our regularizer
naturally extends to the semi-supervised setting, where it produces
state-of-the-art results. We provide a case study in which we transform the
Recursive Neural Tensor Network of (Socher et. al, 2013) into a
pseudo-ensemble, which significantly improves its performance on a real-world
sentiment analysis benchmark.
Highly Efficient Forward and Backward Propagation of Convolutional Neural Networks for Pixelwise Classification
Authors: Hongsheng Li, Rui Zhao, Xiaogang Wang
Date: 2014-12-15 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Image Segmentation, object-detection, Object Detection, Semantic Segmentation

We present highly efficient algorithms for performing forward and backward
propagation of Convolutional Neural Network (CNN) for pixelwise classification
on images. For pixelwise classification tasks, such as image segmentation and
object detection, surrounding image patches are fed into CNN for predicting the
classes of centered pixels via forward propagation and for updating CNN
parameters via backward propagation. However, forward and backward propagation
was originally designed for whole-image classification. Directly applying it to
pixelwise classification in a patch-by-patch scanning manner is extremely
inefficient, because surrounding patches of pixels have large overlaps, which
lead to a lot of redundant computation.
  The proposed algorithms eliminate all the redundant computation in
convolution and pooling on images by introducing novel d-regularly sparse
kernels. It generates exactly the same results as those by patch-by-patch
scanning. Convolution and pooling operations with such kernels are able to
continuously access memory and can run efficiently on GPUs. A fraction of
patches of interest can be chosen from each training image for backward
propagation by applying a mask to the error map at the last CNN layer. Its
computation complexity is constant with respect to the number of patches
sampled from the image. Experiments have shown that our proposed algorithms
speed up commonly used patch-by-patch scanning over 1500 times in both forward
and backward propagation. The speedup increases with the sizes of images and
patches.
Cooperative learning in multi-agent systems from intermittent measurements
Authors: Naomi Ehrich Leonard, Alex Olshevsky
Date: 2012-09-11 00:00:00

Motivated by the problem of tracking a direction in a decentralized way, we
consider the general problem of cooperative learning in multi-agent systems
with time-varying connectivity and intermittent measurements. We propose a
distributed learning protocol capable of learning an unknown vector $\mu$ from
noisy measurements made independently by autonomous nodes. Our protocol is
completely distributed and able to cope with the time-varying, unpredictable,
and noisy nature of inter-agent communication, and intermittent noisy
measurements of $\mu$. Our main result bounds the learning speed of our
protocol in terms of the size and combinatorial features of the (time-varying)
networks connecting the nodes.
Rule-based Emotion Detection on Social Media: Putting Tweets on Plutchik's Wheel
Authors: Erik Tromp, Mykola Pechenizkiy
Date: 2014-12-15 00:00:00
Tasks: Sentiment Analysis

We study sentiment analysis beyond the typical granularity of polarity and
instead use Plutchik's wheel of emotions model. We introduce RBEM-Emo as an
extension to the Rule-Based Emission Model algorithm to deduce such emotions
from human-written messages. We evaluate our approach on two different datasets
and compare its performance with the current state-of-the-art techniques for
emotion detection, including a recursive auto-encoder. The results of the
experimental study suggest that RBEM-Emo is a promising approach advancing the
current state-of-the-art in emotion detection.
Kernel-based Information Criterion
Authors: Somayeh Danafar, Kenji Fukumizu, Faustino Gomez
Date: 2014-08-25 00:00:00
Tasks: GPR, Model Selection, regression

This paper introduces Kernel-based Information Criterion (KIC) for model
selection in regression analysis. The novel kernel-based complexity measure in
KIC efficiently computes the interdependency between parameters of the model
using a variable-wise variance and yields selection of better, more robust
regressors. Experimental results show superior performance on both simulated
and real data sets compared to Leave-One-Out Cross-Validation (LOOCV),
kernel-based Information Complexity (ICOMP), and maximum log of marginal
likelihood in Gaussian Process Regression (GPR).
A Broadcast News Corpus for Evaluation and Tuning of German LVCSR Systems
Authors: Felix Weninger, Björn Schuller, Florian Eyben, Martin Wöllmer, Gerhard Rigoll
Date: 2014-12-15 00:00:00
Tasks: Decoder, speech-recognition, Speech Recognition

Transcription of broadcast news is an interesting and challenging application
for large-vocabulary continuous speech recognition (LVCSR). We present in
detail the structure of a manually segmented and annotated corpus including
over 160 hours of German broadcast news, and propose it as an evaluation
framework of LVCSR systems. We show our own experimental results on the corpus,
achieved with a state-of-the-art LVCSR decoder, measuring the effect of
different feature sets and decoding parameters, and thereby demonstrate that
real-time decoding of our test set is feasible on a desktop PC at 9.2% word
error rate.
Challenges of Big Data Analysis
Authors: Jianqing Fan, Fang Han, Han Liu
Date: 2013-08-07 00:00:00

Big Data bring new opportunities to modern society and challenges to data
scientists. On one hand, Big Data hold great promises for discovering subtle
population patterns and heterogeneities that are not possible with small-scale
data. On the other hand, the massive sample size and high dimensionality of Big
Data introduce unique computational and statistical challenges, including
scalability and storage bottleneck, noise accumulation, spurious correlation,
incidental endogeneity, and measurement errors. These challenges are
distinguished and require new computational and statistical paradigm. This
article give overviews on the salient features of Big Data and how these
features impact on paradigm change on statistical and computational methods as
well as computing architectures. We also provide various new perspectives on
the Big Data analysis and computation. In particular, we emphasis on the
viability of the sparsest solution in high-confidence set and point out that
exogeneous assumptions in most statistical methods for Big Data can not be
validated due to incidental endogeneity. They can lead to wrong statistical
inferences and consequently wrong scientific conclusions.
Worst-case Optimal Query Answering for Greedy Sets of Existential Rules and Their Subclasses
Authors: Sebastian Rudolph, Michaël Thomazo, Jean-François Baget, Marie-Laure Mugnier
Date: 2014-12-15 00:00:00

The need for an ontological layer on top of data, associated with advanced
reasoning mechanisms able to exploit the semantics encoded in ontologies, has
been acknowledged both in the database and knowledge representation
communities. We focus in this paper on the ontological query answering problem,
which consists of querying data while taking ontological knowledge into
account. More specifically, we establish complexities of the conjunctive query
entailment problem for classes of existential rules (also called
tuple-generating dependencies, Datalog+/- rules, or forall-exists-rules. Our
contribution is twofold. First, we introduce the class of greedy
bounded-treewidth sets (gbts) of rules, which covers guarded rules, and their
most well-known generalizations. We provide a generic algorithm for query
entailment under gbts, which is worst-case optimal for combined complexity with
or without bounded predicate arity, as well as for data complexity and query
complexity. Secondly, we classify several gbts classes, whose complexity was
unknown, with respect to combined complexity (with both unbounded and bounded
predicate arity) and data complexity to obtain a comprehensive picture of the
complexity of existential rule fragments that are based on diverse guardedness
notions. Upper bounds are provided by showing that the proposed algorithm is
optimal for all of them.
CITlab ARGUS for Arabic Handwriting
Authors: Gundram Leifert, Roger Labahn, Tobias Strauß
Date: 2014-12-15 00:00:00
Tasks: Handwriting Recognition

In the recent years it turned out that multidimensional recurrent neural
networks (MDRNN) perform very well for offline handwriting recognition tasks
like the OpenHaRT 2013 evaluation DIR. With suitable writing preprocessing and
dictionary lookup, our ARGUS software completed this task with an error rate of
26.27% in its primary setup.
CITlab ARGUS for historical data tables
Authors: Gundram Leifert, Tobias Grüning, Tobias Strauß, Roger Labahn, for the University of Rostock - CITlab
Date: 2014-12-15 00:00:00
Tasks: General Classification, Handwriting Recognition

We describe CITlab's recognition system for the ANWRESH-2014 competition
attached to the 14. International Conference on Frontiers in Handwriting
Recognition, ICFHR 2014. The task comprises word recognition from segmented
historical documents. The core components 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.
Automatic video scene segmentation based on spatial-temporal clues and rhythm
Authors: Walid Mahdi, Liming Chen, Mohsen Ardebilian
Date: 2014-12-15 00:00:00
Tasks: Retrieval, Rhythm, Scene Segmentation, Segmentation, Video Segmentation, Video Semantic Segmentation

With ever increasing computing power and data storage capacity, the potential
for large digital video libraries is growing rapidly.However, the massive use
of video for the moment is limited by its opaque characteristics. Indeed, a
user who has to handle and retrieve sequentially needs too much time in order
to find out segments of interest within a video. Therefore, providing an
environment both convenient and efficient for video storing and retrieval,
especially for content-based searching as this exists in traditional textbased
database systems, has been the focus of recent and important efforts of a large
research community
  In this paper, we propose a new automatic video scene segmentation method
that explores two main video features; these are spatial-temporal relationship
and rhythm of shots. The experimental evidence we obtained from a 80
minutevideo showed that our prototype provides very high accuracy for video
segmentation.
Generating Graphical Chain by Mutual Matching of Bayesian Network and Extracted Rules of Bayesian Network Using Genetic Algorithm
Authors: Mostafa Sepahvand, Ghasem Alikhajeh, Meysam Ghaffari, Abdolreza Mirzaei
Date: 2014-12-15 00:00:00
Tasks: Decision Making

With the technology development, the need of analyze and extraction of useful
information is increasing. Bayesian networks contain knowledge from data and
experts that could be used for decision making processes But they are not
easily understandable thus the rule extraction methods have been used but they
have high computation costs. To overcome this problem we extract rules from
Bayesian network using genetic algorithm. Then we generate the graphical chain
by mutually matching the extracted rules and Bayesian network. This graphical
chain could shows the sequence of events that lead to the target which could
help the decision making process. The experimental results on small networks
show that the proposed method has comparable results with brute force method
which has a significantly higher computation cost.
Fixed Point Algorithm Based on Quasi-Newton Method for Convex Minimization Problem with Application to Image Deblurring
Authors: Dai-Qiang Chen
Date: 2014-12-15 00:00:00
Tasks: Computational Efficiency, Deblurring, Image Deblurring

Solving an optimization problem whose objective function is the sum of two
convex functions has received considerable interests in the context of image
processing recently. In particular, we are interested in the scenario when a
non-differentiable convex function such as the total variation (TV) norm is
included in the objective function due to many variational models established
in image processing have this nature. In this paper, we propose a fast fixed
point algorithm based on the quasi-Newton method for solving this class of
problem, and apply it in the field of TV-based image deblurring. The novel
method is derived from the idea of the quasi-Newton method, and the fixed-point
algorithms based on the proximity operator, which were widely investigated very
recently. Utilizing the non-expansion property of the proximity operator we
further investigate the global convergence of the proposed algorithm. Numerical
experiments on image deblurring problem with additive or multiplicative noise
are presented to demonstrate that the proposed algorithm is superior to the
recently developed fixed-point algorithm in the computational efficiency.
The Computational Theory of Intelligence: Applications to Genetic Programming and Turing Machines
Authors: Daniel Kovach
Date: 2014-12-14 00:00:00

In this paper, we continue the efforts of the Computational Theory of
Intelligence (CTI) by extending concepts to include computational processes in
terms of Genetic Algorithms (GA's) and Turing Machines (TM's). Active, Passive,
and Hybrid Computational Intelligence processes are also introduced and
discussed. We consider the ramifications of the assumptions of CTI with regard
to the qualities of reproduction and virility. Applications to Biology,
Computer Science and Cyber Security are also discussed.