pwc-archive/papers-with-abstracts huggingface dataset:
Inexact Coordinate Descent: Complexity and Preconditioning
Authors: Rachael Tappenden, Peter Richtárik, Jacek Gondzio
Date: 2013-04-19 00:00:00

In this paper we consider the problem of minimizing a convex function using a
randomized block coordinate descent method. One of the key steps at each
iteration of the algorithm is determining the update to a block of variables.
Existing algorithms assume that in order to compute the update, a particular
subproblem is solved exactly. In his work we relax this requirement, and allow
for the subproblem to be solved inexactly, leading to an inexact block
coordinate descent method. Our approach incorporates the best known results for
exact updates as a special case. Moreover, these theoretical guarantees are
complemented by practical considerations: the use of iterative techniques to
determine the update as well as the use of preconditioning for further
acceleration.
Candidate Constrained CRFs for Loss-Aware Structured Prediction
Authors: Faruk Ahmed, Daniel Tarlow, Dhruv Batra
Date: 2014-12-10 00:00:00
Tasks: Image Segmentation, Prediction, Semantic Segmentation, Structured Prediction

When evaluating computer vision systems, we are often concerned with
performance on a task-specific evaluation measure such as the
Intersection-Over-Union score used in the PASCAL VOC image segmentation
challenge. Ideally, our systems would be tuned specifically to these evaluation
measures. However, despite much work on loss-aware structured prediction, top
performing systems do not use these techniques. In this work, we seek to
address this problem, incorporating loss-aware prediction in a manner that is
amenable to the approaches taken by top performing systems. Our main idea is to
simultaneously leverage two systems: a highly tuned pipeline system as is found
on top of leaderboards, and a traditional CRF. We show how to combine high
quality candidate solutions from the pipeline with the probabilistic approach
of the CRF that is amenable to loss-aware prediction. The result is that we can
use loss-aware prediction methodology to improve performance of the highly
tuned pipeline system.
Neural Turing Machines
Authors: Alex Graves, Greg Wayne, Ivo Danihelka
Date: 2014-10-20 00:00:00

We extend the capabilities of neural networks by coupling them to external
memory resources, which they can interact with by attentional processes. The
combined system is analogous to a Turing Machine or Von Neumann architecture
but is differentiable end-to-end, allowing it to be efficiently trained with
gradient descent. Preliminary results demonstrate that Neural Turing Machines
can infer simple algorithms such as copying, sorting, and associative recall
from input and output examples.
Deep Multi-Instance Transfer Learning
Authors: Dimitrios Kotzias, Misha Denil, Phil Blunsom, Nando de Freitas
Date: 2014-11-12 00:00:00
Tasks: Transfer Learning

We present a new approach for transferring knowledge from groups to
individuals that comprise them. We evaluate our method in text, by inferring
the ratings of individual sentences using full-review ratings. This approach,
which combines ideas from transfer learning, deep learning and multi-instance
learning, reduces the need for laborious human labelling of fine-grained data
when abundant labels are available at the group level.
Convergence and rate of convergence of some greedy algorithms in convex optimization
Authors: Vladimir Temlyakov
Date: 2014-12-10 00:00:00
Tasks: Vocal Bursts Type Prediction

The paper gives a systematic study of the approximate versions of three
greedy-type algorithms that are widely used in convex optimization. By
approximate version we mean the one where some of evaluations are made with an
error. Importance of such versions of greedy-type algorithms in convex
optimization and in approximation theory was emphasized in previous literature.
The category of networks of ontologies
Authors: Jérôme Euzenat
Date: 2014-12-10 00:00:00

The semantic web has led to the deployment of ontologies on the web connected
through various relations and, in particular, alignments of their vocabularies.
There exists several semantics for alignments which make difficult
interoperation between different interpretation of networks of ontologies. Here
we present an abstraction of these semantics which allows for defining the
notions of closure and consistency for networks of ontologies independently
from the precise semantics. We also show that networks of ontologies with
specific notions of morphisms define categories of networks of ontologies.
Generalised Entropy MDPs and Minimax Regret
Authors: Emmanouil G. Androulakis, Christos Dimitrakakis
Date: 2014-12-10 00:00:00

Bayesian methods suffer from the problem of how to specify prior beliefs. One
interesting idea is to consider worst-case priors. This requires solving a
stochastic zero-sum game. In this paper, we extend well-known results from
bandit theory in order to discover minimax-Bayes policies and discuss when they
are practical.
Object-centric Sampling for Fine-grained Image Classification
Authors: Xiaoyu Wang, Tianbao Yang, Guobin Chen, Yuanqing Lin
Date: 2014-12-10 00:00:00
Tasks: Classification, Fine-Grained Image Classification, General Classification, image-classification, Image Classification, Object, object-detection, Object Detection

This paper proposes to go beyond the state-of-the-art deep convolutional
neural network (CNN) by incorporating the information from object detection,
focusing on dealing with fine-grained image classification. Unfortunately, CNN
suffers from over-fiting when it is trained on existing fine-grained image
classification benchmarks, which typically only consist of less than a few tens
of thousands training images. Therefore, we first construct a large-scale
fine-grained car recognition dataset that consists of 333 car classes with more
than 150 thousand training images. With this large-scale dataset, we are able
to build a strong baseline for CNN with top-1 classification accuracy of 81.6%.
One major challenge in fine-grained image classification is that many classes
are very similar to each other while having large within-class variation. One
contributing factor to the within-class variation is cluttered image
background. However, the existing CNN training takes uniform window sampling
over the image, acting as blind on the location of the object of interest. In
contrast, this paper proposes an \emph{object-centric sampling} (OCS) scheme
that samples image windows based on the object location information. The
challenge in using the location information lies in how to design powerful
object detector and how to handle the imperfectness of detection results. To
that end, we design a saliency-aware object detection approach specific for the
setting of fine-grained image classification, and the uncertainty of detection
results are naturally handled in our OCS scheme. Our framework is demonstrated
to be very effective, improving top-1 accuracy to 89.3% (from 81.6%) on the
large-scale fine-grained car classification dataset.
Road Detection via On--line Label Transfer
Authors: José M. Álvarez, Ferran Diego, Joan Serrat, Antonio M. López
Date: 2014-12-10 00:00:00
Tasks: Pedestrian Detection, valid, Video Alignment

Vision-based road detection is an essential functionality for supporting
advanced driver assistance systems (ADAS) such as road following and vehicle
and pedestrian detection. The major challenges of road detection are dealing
with shadows and lighting variations and the presence of other objects in the
scene. Current road detection algorithms characterize road areas at pixel level
and group pixels accordingly. However, these algorithms fail in presence of
strong shadows and lighting variations. Therefore, we propose a road detection
algorithm based on video alignment. The key idea of the algorithm is to exploit
the similarities occurred when a vehicle follows the same trajectory more than
once. In this way, road areas are learned in a first ride and then, this road
knowledge is used to infer areas depicting drivable road surfaces in subsequent
rides. Two different experiments are conducted to validate the proposal on
different video sequences taken at different scenarios and different daytime.
The former aims to perform on-line road detection. The latter aims to perform
off-line road detection and is applied to automatically generate the
ground-truth necessary to validate road detection algorithms. Qualitative and
quantitative evaluations prove that the proposed algorithm is a valid road
detection approach.
Computoser - rule-based, probability-driven algorithmic music composition
Authors: Bozhidar Bozhanov
Date: 2014-12-09 00:00:00

This paper presents the Computoser hybrid probability/rule based algorithm
for music composition (http://computoser.com) and provides a reference
implementation. It addresses the issues of unpleasantness and lack of variation
exhibited by many existing approaches by combining the two methods (basing the
parameters of the rules on data obtained from preliminary analysis).
  A sample of 500+ musical pieces was analyzed to derive probabilities for
musical characteristics and events (e.g. scale, tempo, intervals). The
algorithm was constructed to produce musical pieces using the derived
probabilities combined with a large set of composition rules, which were
obtained and structured after studying established composition practices.
Generated pieces were published on the Computoser website where evaluation was
performed by listeners. The feedback was positive (58.4% approval), asserting
the merits of the undertaken approach.
  The paper compares this hybrid approach to other approaches to algorithmic
composition and presents a survey of the pleasantness of the resulting music.
Hierarchical Mixture-of-Experts Model for Large-Scale Gaussian Process Regression
Authors: Jun Wei Ng, Marc Peter Deisenroth
Date: 2014-12-09 00:00:00
Tasks: Mixture-of-Experts, regression

We propose a practical and scalable Gaussian process model for large-scale
nonlinear probabilistic regression. Our mixture-of-experts model is
conceptually simple and hierarchically recombines computations for an overall
approximation of a full Gaussian process. Closed-form and distributed
computations allow for efficient and massive parallelisation while keeping the
memory consumption small. Given sufficient computing resources, our model can
handle arbitrarily large data sets, without explicit sparse approximations. We
provide strong experimental evidence that our model can be applied to large
data sets of sizes far beyond millions. Hence, our model has the potential to
lay the foundation for general large-scale Gaussian process research.
The Computational Complexity of Structure-Based Causality
Authors: Gadi Aleksandrowicz, Hana Chockler, Joseph Y. Halpern, Alexander Ivrii
Date: 2014-12-09 00:00:00

Halpern and Pearl introduced a definition of actual causality; Eiter and
Lukasiewicz showed that computing whether X=x is a cause of Y=y is NP-complete
in binary models (where all variables can take on only two values) and\
Sigma_2^P-complete in general models. In the final version of their paper,
Halpern and Pearl slightly modified the definition of actual cause, in order to
deal with problems pointed by Hopkins and Pearl. As we show, this modification
has a nontrivial impact on the complexity of computing actual cause. To
characterize the complexity, a new family D_k^P, k= 1, 2, 3, ..., of complexity
classes is introduced, which generalizes the class DP introduced by
Papadimitriou and Yannakakis (DP is just D_1^P). %joe2 %We show that the
complexity of computing causality is $\D_2$-complete %under the new definition.
Chockler and Halpern \citeyear{CH04} extended the We show that the complexity
of computing causality under the updated definition is $D_2^P$-complete.
  Chockler and Halpern extended the definition of causality by introducing
notions of responsibility and blame. The complexity of determining the degree
of responsibility and blame using the original definition of causality was
completely characterized. Again, we show that changing the definition of
causality affects the complexity, and completely characterize it using the
updated definition.
POPE: Post Optimization Posterior Evaluation of Likelihood Free Models
Authors: Edward Meeds, Michael Chiang, Mary Lee, Olivier Cinquin, John Lowengrub, Max Welling
Date: 2014-12-09 00:00:00

In many domains, scientists build complex simulators of natural phenomena
that encode their hypotheses about the underlying processes. These simulators
can be deterministic or stochastic, fast or slow, constrained or unconstrained,
and so on. Optimizing the simulators with respect to a set of parameter values
is common practice, resulting in a single parameter setting that minimizes an
objective subject to constraints. We propose a post optimization posterior
analysis that computes and visualizes all the models that can generate equally
good or better simulation results, subject to constraints. These optimization
posteriors are desirable for a number of reasons among which easy
interpretability, automatic parameter sensitivity and correlation analysis and
posterior predictive analysis. We develop a new sampling framework based on
approximate Bayesian computation (ABC) with one-sided kernels. In collaboration
with two groups of scientists we applied POPE to two important biological
simulators: a fast and stochastic simulator of stem-cell cycling and a slow and
deterministic simulator of tumor growth patterns.
A Unified Semantic Embedding: Relating Taxonomies and Attributes
Authors: Sung Ju Hwang, Leonid Sigal
Date: 2014-11-18 00:00:00
Proceeding: NeurIPS 2014 12
Tasks: Object Categorization

We propose a method that learns a discriminative yet semantic space for
object categorization, where we also embed auxiliary semantic entities such as
supercategories and attributes. Contrary to prior work which only utilized them
as side information, we explicitly embed the semantic entities into the same
space where we embed categories, which enables us to represent a category as
their linear combination. By exploiting such a unified model for semantics, we
enforce each category to be represented by a supercategory + sparse combination
of attributes, with an additional exclusive regularization to learn
discriminative composition.
Brain Tumor Detection Based on Bilateral Symmetry Information
Authors: Narkhede Sachin, Deven Shah, Vaishali Khairnar, Sujata Kadu
Date: 2014-12-09 00:00:00
Tasks: Image Segmentation, MRI segmentation, Segmentation, Semantic Segmentation

Advances in computing technology have allowed researchers across many fields
of endeavor to collect and maintain vast amounts of observational statistical
data such as clinical data,biological patient data,data regarding access of web
sites,financial data,and the like.Brain Magnetic Resonance
Imaging(MRI)segmentation is a complex problem in the field of medical imaging
despite various presented methods.MR image of human brain can be divided into
several sub regions especially soft tissues such as gray matter,white matter
and cerebrospinal fluid.Although edge information is the main clue in image
segmentation,it can not get a better result in analysis the content of images
without combining other information.The segmentation of brain tissue in the
magnetic resonance imaging(MRI)is very important for detecting the existence
and outlines of tumors.In this paper,an algorithm about segmentation based on
the symmetry character of brain MRI image is presented.Our goal is to detect
the position and boundary of tumors automatically.Experiments were conducted on
real pictures,and the results show that the algorithm is flexible and
convenient.
An Approach for Reducing Outliers of Non Local Means Image Denoising Filter
Authors: Raka Kundu, Amlan Chakrabarti, Prasanna Lenka
Date: 2014-12-08 00:00:00
Tasks: Denoising, Image Denoising, Image Restoration

We propose an adaptive approach for non local means (NLM) image filtering
termed as non local adaptive clipped means (NLACM), which reduces the effect of
outliers and improves the denoising quality as compared to traditional NLM.
Common method to neglect outliers from a data population is computation of mean
in a range defined by mean and standard deviation. In NLACM we perform the
median within the defined range based on statistical estimation of the
neighbourhood region of a pixel to be denoised. As parameters of the range are
independent of any additional input and is based on local intensity values,
hence the approach is adaptive. Experimental results for NLACM show better
estimation of true intensity from noisy neighbourhood observation as compared
to NLM at high noise levels. We have verified the technique for speckle noise
reduction and we have tested it on ultrasound (US) image of lumbar spine. These
ultrasound images act as guidance for injection therapy for treatment of lumbar
radiculopathy. We believe that the proposed approach for image denoising is
first of its kind and its efficiency can be well justified as it shows better
performance in image restoration.
Cause, Responsibility, and Blame: oA Structural-Model Approach
Authors: Joseph Y. Halpern
Date: 2014-12-09 00:00:00

A definition of causality introduced by Halpern and Pearl, which uses
structural equations, is reviewed. A more refined definition is then
considered, which takes into account issues of normality and typicality, which
are well known to affect causal ascriptions. Causality is typically an
all-or-nothing notion: either A is a cause of B or it is not. An extension of
the definition of causality to capture notions of degree of responsibility and
degree of blame, due to Chockler and Halpern, is reviewed. For example, if
someone wins an election 11-0, then each person who votes for him is less
responsible for the victory than if he had won 6-5. Degree of blame takes into
account an agent's epistemic state. Roughly speaking, the degree of blame of A
for B is the expected degree of responsibility of A for B, taken over the
epistemic state of an agent. Finally, the structural-equations definition of
causality is compared to Wright's NESS test.
Sparsity and adaptivity for the blind separation of partially correlated sources
Authors: Jerome Bobin, Jeremy Rapin, Anthony Larue, Jean-Luc Starck
Date: 2014-12-09 00:00:00
Tasks: blind source separation, Diversity, valid

Blind source separation (BSS) is a very popular technique to analyze
multichannel data. In this context, the data are modeled as the linear
combination of sources to be retrieved. For that purpose, standard BSS methods
all rely on some discrimination principle, whether it is statistical
independence or morphological diversity, to distinguish between the sources.
However, dealing with real-world data reveals that such assumptions are rarely
valid in practice: the signals of interest are more likely partially
correlated, which generally hampers the performances of standard BSS methods.
In this article, we introduce a novel sparsity-enforcing BSS method coined
Adaptive Morphological Component Analysis (AMCA), which is designed to retrieve
sparse and partially correlated sources. More precisely, it makes profit of an
adaptive re-weighting scheme to favor/penalize samples based on their level of
correlation. Extensive numerical experiments have been carried out which show
that the proposed method is robust to the partial correlation of sources while
standard BSS techniques fail. The AMCA algorithm is evaluated in the field of
astrophysics for the separation of physical components from microwave data.
Bayesian Fisher's Discriminant for Functional Data
Authors: Yao-Hsiang Yang, Lu-Hung Chen, Chieh-Chih Wang, Chu-Song Chen
Date: 2014-12-09 00:00:00

We propose a Bayesian framework of Gaussian process in order to extend
Fisher's discriminant to classify functional data such as spectra and images.
The probability structure for our extended Fisher's discriminant is explicitly
formulated, and we utilize the smoothness assumptions of functional data as
prior probabilities. Existing methods which directly employ the smoothness
assumption of functional data can be shown as special cases within this
framework given corresponding priors while their estimates of the unknowns are
one-step approximations to the proposed MAP estimates. Empirical results on
various simulation studies and different real applications show that the
proposed method significantly outperforms the other Fisher's discriminant
methods for functional data.
Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition
Authors: Max Jaderberg, Karen Simonyan, Andrea Vedaldi, Andrew Zisserman
Date: 2014-06-09 00:00:00
Tasks: Scene Text Recognition, Text Generation

In this work we present a framework for the recognition of natural scene
text. Our framework does not require any human-labelled data, and performs word
recognition on the whole image holistically, departing from the character based
recognition systems of the past. The deep neural network models at the centre
of this framework are trained solely on data produced by a synthetic text
generation engine -- synthetic data that is highly realistic and sufficient to
replace real data, giving us infinite amounts of training data. This excess of
data exposes new possibilities for word recognition models, and here we
consider three models, each one "reading" words in a different way: via 90k-way
dictionary encoding, character sequence encoding, and bag-of-N-grams encoding.
In the scenarios of language based and completely unconstrained text
recognition we greatly improve upon state-of-the-art performance on standard
datasets, using our fast, simple machinery and requiring zero data-acquisition
costs.
ROP: Matrix recovery via rank-one projections
Authors: T. Tony Cai, Anru Zhang
Date: 2013-10-22 00:00:00

Estimation of low-rank matrices is of significant interest in a range of
contemporary applications. In this paper, we introduce a rank-one projection
model for low-rank matrix recovery and propose a constrained nuclear norm
minimization method for stable recovery of low-rank matrices in the noisy case.
The procedure is adaptive to the rank and robust against small perturbations.
Both upper and lower bounds for the estimation accuracy under the Frobenius
norm loss are obtained. The proposed estimator is shown to be rate-optimal
under certain conditions. The estimator is easy to implement via convex
programming and performs well numerically. The techniques and main results
developed in the paper also have implications to other related statistical
problems. An application to estimation of spiked covariance matrices from
one-dimensional random projections is considered. The results demonstrate that
it is still possible to accurately estimate the covariance matrix of a
high-dimensional distribution based only on one-dimensional projections.
Cancer Detection with Multiple Radiologists via Soft Multiple Instance Logistic Regression and $L_1$ Regularization
Authors: Inna Stainvas, Alexandra Manevitch, Isaac Leichter
Date: 2014-12-09 00:00:00
Tasks: Multiple Instance Learning, regression

This paper deals with the multiple annotation problem in medical application
of cancer detection in digital images. The main assumption is that though
images are labeled by many experts, the number of images read by the same
expert is not large. Thus differing with the existing work on modeling each
expert and ground truth simultaneously, the multi annotation information is
used in a soft manner. The multiple labels from different experts are used to
estimate the probability of the findings to be marked as malignant. The
learning algorithm minimizes the Kullback Leibler (KL) divergence between the
modeled probabilities and desired ones constraining the model to be compact.
The probabilities are modeled by logit regression and multiple instance
learning concept is used by us.
  Experiments on a real-life computer aided diagnosis (CAD) problem for CXR CAD
lung cancer detection demonstrate that the proposed algorithm leads to similar
results as learning with a binary RVMMIL classifier or a mixture of binary
RVMMIL models per annotator. However, this model achieves a smaller complexity
and is more preferable in practice.