pwc-archive/papers-with-abstracts huggingface dataset:
Category-Epitomes : Discriminatively Minimalist Representations for Object Categories
Authors: Ravi Kiran Sarvadevabhatla, R. Venkatesh Babu
Date: 2015-01-31 00:00:00
Tasks: Object

Freehand line sketches are an interesting and unique form of visual
representation. Typically, such sketches are studied and utilized as an end
product of the sketching process. However, we have found it instructive to
study the sketches as sequentially accumulated composition of drawing strokes
added over time. Studying sketches in this manner has enabled us to create
novel sparse yet discriminative sketch-based representations for object
categories which we term category-epitomes. Our procedure for obtaining these
epitomes concurrently provides a natural measure for quantifying the sparseness
underlying the original sketch, which we term epitome-score. We construct and
analyze category-epitomes and epitome-scores for freehand sketches belonging to
various object categories. Our analysis provides a novel viewpoint for studying
the semantic nature of object categories.
A Batchwise Monotone Algorithm for Dictionary Learning
Authors: Huan Wang, John Wright, Daniel Spielman
Date: 2015-01-31 00:00:00
Tasks: Dictionary Learning

We propose a batchwise monotone algorithm for dictionary learning. Unlike the
state-of-the-art dictionary learning algorithms which impose sparsity
constraints on a sample-by-sample basis, we instead treat the samples as a
batch, and impose the sparsity constraint on the whole. The benefit of
batchwise optimization is that the non-zeros can be better allocated across the
samples, leading to a better approximation of the whole. To accomplish this, we
propose procedures to switch non-zeros in both rows and columns in the support
of the coefficient matrix to reduce the reconstruction error. We prove in the
proposed support switching procedure the objective of the algorithm, i.e., the
reconstruction error, decreases monotonically and converges. Furthermore, we
introduce a block orthogonal matching pursuit algorithm that also operates on
sample batches to provide a warm start. Experiments on both natural image
patches and UCI data sets show that the proposed algorithm produces a better
approximation with the same sparsity levels compared to the state-of-the-art
algorithms.
Novel Approaches for Predicting Risk Factors of Atherosclerosis
Authors: V. Sree Hari Rao, M. Naresh Kumar
Date: 2015-01-28 00:00:00
Tasks: BIG-bench Machine Learning, Imputation

Coronary heart disease (CHD) caused by hardening of artery walls due to
cholesterol known as atherosclerosis is responsible for large number of deaths
world-wide. The disease progression is slow, asymptomatic and may lead to
sudden cardiac arrest, stroke or myocardial infraction. Presently, imaging
techniques are being employed to understand the molecular and metabolic
activity of atherosclerotic plaques to estimate the risk. Though imaging
methods are able to provide some information on plaque metabolism they lack the
required resolution and sensitivity for detection. In this paper we consider
the clinical observations and habits of individuals for predicting the risk
factors of CHD. The identification of risk factors helps in stratifying
patients for further intensive tests such as nuclear imaging or coronary
angiography. We present a novel approach for predicting the risk factors of
atherosclerosis with an in-built imputation algorithm and particle swarm
optimization (PSO). We compare the performance of our methodology with other
machine learning techniques on STULONG dataset which is based on longitudinal
study of middle aged individuals lasting for twenty years. Our methodology
powered by PSO search has identified physical inactivity as one of the risk
factor for the onset of atherosclerosis in addition to other already known
factors. The decision rules extracted by our methodology are able to predict
the risk factors with an accuracy of $99.73%$ which is higher than the
accuracies obtained by application of the state-of-the-art machine learning
techniques presently being employed in the identification of atherosclerosis
risk studies.
A New Intelligence Based Approach for Computer-Aided Diagnosis of Dengue Fever
Authors: Vadrevu Sree Hari Rao, Mallenahalli Naresh Kumar
Date: 2015-01-31 00:00:00
Tasks: Imputation, Management

Identification of the influential clinical symptoms and laboratory features
that help in the diagnosis of dengue fever in early phase of the illness would
aid in designing effective public health management and virological
surveillance strategies. Keeping this as our main objective we develop in this
paper, a new computational intelligence based methodology that predicts the
diagnosis in real time, minimizing the number of false positives and false
negatives. Our methodology consists of three major components (i) a novel
missing value imputation procedure that can be applied on any data set
consisting of categorical (nominal) and/or numeric (real or integer) (ii) a
wrapper based features selection method with genetic search for extracting a
subset of most influential symptoms that can diagnose the illness and (iii) an
alternating decision tree method that employs boosting for generating highly
accurate decision rules. The predictive models developed using our methodology
are found to be more accurate than the state-of-the-art methodologies used in
the diagnosis of the dengue fever.
Maximally Informative Hierarchical Representations of High-Dimensional Data
Authors: Greg Ver Steeg, Aram Galstyan
Date: 2014-10-27 00:00:00
Tasks: Vocal Bursts Intensity Prediction

We consider a set of probabilistic functions of some input variables as a
representation of the inputs. We present bounds on how informative a
representation is about input data. We extend these bounds to hierarchical
representations so that we can quantify the contribution of each layer towards
capturing the information in the original data. The special form of these
bounds leads to a simple, bottom-up optimization procedure to construct
hierarchical representations that are also maximally informative about the
data. This optimization has linear computational complexity and constant sample
complexity in the number of variables. These results establish a new approach
to unsupervised learning of deep representations that is both principled and
practical. We demonstrate the usefulness of the approach on both synthetic and
real-world data.
Max-Margin Object Detection
Authors: Davis E. King
Date: 2015-01-31 00:00:00
Tasks: Face Detection, Object, object-detection, Object Detection

Most object detection methods operate by applying a binary classifier to
sub-windows of an image, followed by a non-maximum suppression step where
detections on overlapping sub-windows are removed. Since the number of possible
sub-windows in even moderately sized image datasets is extremely large, the
classifier is typically learned from only a subset of the windows. This avoids
the computational difficulty of dealing with the entire set of sub-windows,
however, as we will show in this paper, it leads to sub-optimal detector
performance.
  In particular, the main contribution of this paper is the introduction of a
new method, Max-Margin Object Detection (MMOD), for learning to detect objects
in images. This method does not perform any sub-sampling, but instead optimizes
over all sub-windows. MMOD can be used to improve any object detection method
which is linear in the learned parameters, such as HOG or bag-of-visual-word
models. Using this approach we show substantial performance gains on three
publicly available datasets. Strikingly, we show that a single rigid HOG filter
can outperform a state-of-the-art deformable part model on the Face Detection
Data Set and Benchmark when the HOG filter is learned via MMOD.
Text mixing shapes the anatomy of rank-frequency distributions: A modern Zipfian mechanics for natural language
Authors: Jake Ryland Williams, James P. Bagrow, Christopher M. Danforth, Peter Sheridan Dodds
Date: 2014-09-12 00:00:00
Tasks: Anatomy

Natural languages are full of rules and exceptions. One of the most famous
quantitative rules is Zipf's law which states that the frequency of occurrence
of a word is approximately inversely proportional to its rank. Though this
`law' of ranks has been found to hold across disparate texts and forms of data,
analyses of increasingly large corpora over the last 15 years have revealed the
existence of two scaling regimes. These regimes have thus far been explained by
a hypothesis suggesting a separability of languages into core and non-core
lexica. Here, we present and defend an alternative hypothesis, that the two
scaling regimes result from the act of aggregating texts. We observe that text
mixing leads to an effective decay of word introduction, which we show provides
accurate predictions of the location and severity of breaks in scaling. Upon
examining large corpora from 10 languages in the Project Gutenberg eBooks
collection (eBooks), we find emphatic empirical support for the universality of
our claim.
maxDNN: An Efficient Convolution Kernel for Deep Learning with Maxwell GPUs
Authors: Andrew Lavin
Date: 2015-01-27 00:00:00
Tasks: Computational Efficiency, Deep Learning, GPU

This paper describes maxDNN, a computationally efficient convolution kernel
for deep learning with the NVIDIA Maxwell GPU. maxDNN reaches 96.3%
computational efficiency on typical deep learning network architectures. The
design combines ideas from cuda-convnet2 with the Maxas SGEMM assembly code. We
only address forward propagation (FPROP) operation of the network, but we
believe that the same techniques used here will be effective for backward
propagation (BPROP) as well.
SHOE: Supervised Hashing with Output Embeddings
Authors: Sravanthi Bondugula, Varun Manjunatha, Larry S. Davis, David Doermann
Date: 2015-01-30 00:00:00
Tasks: Attribute, Dimensionality Reduction, Image Retrieval, Retrieval, Supervised dimensionality reduction

We present a supervised binary encoding scheme for image retrieval that
learns projections by taking into account similarity between classes obtained
from output embeddings. Our motivation is that binary hash codes learned in
this way improve both the visual quality of retrieval results and existing
supervised hashing schemes. We employ a sequential greedy optimization that
learns relationship aware projections by minimizing the difference between
inner products of binary codes and output embedding vectors. We develop a joint
optimization framework to learn projections which improve the accuracy of
supervised hashing over the current state of the art with respect to standard
and sibling evaluation metrics. We further boost performance by applying the
supervised dimensionality reduction technique on kernelized input CNN features.
Experiments are performed on three datasets: CUB-2011, SUN-Attribute and
ImageNet ILSVRC 2010. As a by-product of our method, we show that using a
simple k-nn pooling classifier with our discriminative codes improves over the
complex classification models on fine grained datasets like CUB and offer an
impressive compression ratio of 1024 on CNN features.
A Light Transport Model for Mitigating Multipath Interference in TOF Sensors
Authors: Nikhil Naik, Achuta Kadambi, Christoph Rhemann, Shahram Izadi, Ramesh Raskar, Sing Bing Kang
Date: 2015-01-20 00:00:00
Proceeding: CVPR 2015

Continuous-wave Time-of-flight (TOF) range imaging has become a commercially
viable technology with many applications in computer vision and graphics.
However, the depth images obtained from TOF cameras contain scene dependent
errors due to multipath interference (MPI). Specifically, MPI occurs when
multiple optical reflections return to a single spatial location on the imaging
sensor. Many prior approaches to rectifying MPI rely on sparsity in optical
reflections, which is an extreme simplification. In this paper, we correct MPI
by combining the standard measurements from a TOF camera with information from
direct and global light transport. We report results on both simulated
experiments and physical experiments (using the Kinect sensor). Our results,
evaluated against ground truth, demonstrate a quantitative improvement in depth
accuracy.
Beyond Frontal Faces: Improving Person Recognition Using Multiple Cues
Authors: Ning Zhang, Manohar Paluri, Yaniv Taigman, Rob Fergus, Lubomir Bourdev
Date: 2015-01-23 00:00:00
Proceeding: CVPR 2015 6
Tasks: Person Recognition

We explore the task of recognizing peoples' identities in photo albums in an
unconstrained setting. To facilitate this, we introduce the new People In Photo
Albums (PIPA) dataset, consisting of over 60000 instances of 2000 individuals
collected from public Flickr photo albums. With only about half of the person
images containing a frontal face, the recognition task is very challenging due
to the large variations in pose, clothing, camera viewpoint, image resolution
and illumination. We propose the Pose Invariant PErson Recognition (PIPER)
method, which accumulates the cues of poselet-level person recognizers trained
by deep convolutional networks to discount for the pose variations, combined
with a face recognizer and a global recognizer. Experiments on three different
settings confirm that in our unconstrained setup PIPER significantly improves
on the performance of DeepFace, which is one of the best face recognizers as
measured on the LFW dataset.
Multi-task Image Classification via Collaborative, Hierarchical Spike-and-Slab Priors
Authors: Hojjat Seyed Mousavi, Umamahesh Srinivas, Vishal Monga, Yuanming Suo, Minh Dao, Trac. D. Tran
Date: 2015-01-30 00:00:00
Tasks: Classification, Face Recognition, General Classification, image-classification, Image Classification

Promising results have been achieved in image classification problems by
exploiting the discriminative power of sparse representations for
classification (SRC). Recently, it has been shown that the use of
\emph{class-specific} spike-and-slab priors in conjunction with the
class-specific dictionaries from SRC is particularly effective in low training
scenarios. As a logical extension, we build on this framework for multitask
scenarios, wherein multiple representations of the same physical phenomena are
available. We experimentally demonstrate the benefits of mining joint
information from different camera views for multi-view face recognition.
An Analytical Study of different Document Image Binarization Methods
Authors: Mahua Nandy, Satadal Saha
Date: 2015-01-30 00:00:00
Tasks: Binarization

Document image has been the area of research for a couple of decades because
of its potential application in the area of text recognition, line recognition
or any other shape recognition from the image. For most of these purposes
binarization of image becomes mandatory as far as recognition is concerned.
Throughout couple decades standard algorithms have already been developed for
this purpose. Some of these algorithms are applicable to degraded image also.
Our objective behind this work is to study the existing techniques, compare
them in view of advantages and disadvantages and modify some of these
algorithms to optimize time or performance.
Orthogonal Matrix Retrieval in Cryo-Electron Microscopy
Authors: Tejal Bhamre, Teng Zhang, Amit Singer
Date: 2014-12-01 00:00:00
Tasks: Retrieval

In single particle reconstruction (SPR) from cryo-electron microscopy
(cryo-EM), the 3D structure of a molecule needs to be determined from its 2D
projection images taken at unknown viewing directions. Zvi Kam showed already
in 1980 that the autocorrelation function of the 3D molecule over the rotation
group SO(3) can be estimated from 2D projection images whose viewing directions
are uniformly distributed over the sphere. The autocorrelation function
determines the expansion coefficients of the 3D molecule in spherical harmonics
up to an orthogonal matrix of size $(2l+1)\times (2l+1)$ for each
$l=0,1,2,...$. In this paper we show how techniques for solving the phase
retrieval problem in X-ray crystallography can be modified for the cryo-EM
setup for retrieving the missing orthogonal matrices. Specifically, we present
two new approaches that we term Orthogonal Extension and Orthogonal
Replacement, in which the main algorithmic components are the singular value
decomposition and semidefinite programming. We demonstrate the utility of these
approaches through numerical experiments on simulated data.
A Proximal Bregman Projection Approach to Continuous Max-Flow Problems Using Entropic Distances
Authors: John S. H. Baxter, Martin Rajchl, Jing Yuan, Terry M. Peters
Date: 2015-01-30 00:00:00
Tasks: Segmentation

One issue limiting the adaption of large-scale multi-region segmentation is
the sometimes prohibitive memory requirements. This is especially troubling
considering advances in massively parallel computing and commercial graphics
processing units because of their already limited memory compared to the
current random access memory used in more traditional computation. To address
this issue in the field of continuous max-flow segmentation, we have developed
a \textit{pseudo-flow} framework using the theory of Bregman proximal
projections and entropic distances which implicitly represents flow variables
between labels and designated source and sink nodes. This reduces the memory
requirements for max-flow segmentation by approximately 20\% for Potts models
and approximately 30\% for hierarchical max-flow (HMF) and directed acyclic
graph max-flow (DAGMF) models. This represents a great improvement in the
state-of-the-art in max-flow segmentation, allowing for much larger problems to
be addressed and accelerated using commercially available graphics processing
hardware.
Significant Subgraph Mining with Multiple Testing Correction
Authors: Mahito Sugiyama, Felipe Llinares López, Niklas Kasenburg, Karsten M. Borgwardt
Date: 2014-07-01 00:00:00
Tasks: Open-Ended Question Answering, Two-sample testing

The problem of finding itemsets that are statistically significantly enriched
in a class of transactions is complicated by the need to correct for multiple
hypothesis testing. Pruning untestable hypotheses was recently proposed as a
strategy for this task of significant itemset mining. It was shown to lead to
greater statistical power, the discovery of more truly significant itemsets,
than the standard Bonferroni correction on real-world datasets. An open
question, however, is whether this strategy of excluding untestable hypotheses
also leads to greater statistical power in subgraph mining, in which the number
of hypotheses is much larger than in itemset mining. Here we answer this
question by an empirical investigation on eight popular graph benchmark
datasets. We propose a new efficient search strategy, which always returns the
same solution as the state-of-the-art approach and is approximately two orders
of magnitude faster. Moreover, we exploit the dependence between subgraphs by
considering the effective number of tests and thereby further increase the
statistical power.
RANSAC based three points algorithm for ellipse fitting of spherical object's projection
Authors: Shenghui Xu
Date: 2015-01-30 00:00:00
Tasks: 3D Reconstruction, Object

As the spherical object can be seen everywhere, we should extract the ellipse
image accurately and fit it by implicit algebraic curve in order to finish the
3D reconstruction. In this paper, we propose a new ellipse fitting algorithm
which only needs three points to fit the projection of spherical object and is
different from the traditional algorithms that need at least five point. The
fitting procedure is just similar as the estimation of Fundamental Matrix
estimation by seven points, and the RANSAC algorithm has also been used to
exclude the interference of noise and scattered points.
Confidence intervals for AB-test
Authors: Cyrille Dubarry
Date: 2015-01-30 00:00:00

AB-testing is a very popular technique in web companies since it makes it
possible to accurately predict the impact of a modification with the simplicity
of a random split across users. One of the critical aspects of an AB-test is
its duration and it is important to reliably compute confidence intervals
associated with the metric of interest to know when to stop the test. In this
paper, we define a clean mathematical framework to model the AB-test process.
We then propose three algorithms based on bootstrapping and on the central
limit theorem to compute reliable confidence intervals which extend to other
metrics than the common probabilities of success. They apply to both absolute
and relative increments of the most used comparison metrics, including the
number of occurrences of a particular event and a click-through rate implying a
ratio.
Gibbs-Ringing Artifact Removal Based on Local Subvoxel-shifts
Authors: Elias Kellner, Bibek Dhital, Marco Reisert
Date: 2015-01-30 00:00:00

Gibbs-ringing is a well known artifact which manifests itself as spurious
oscillations in the vicinity of sharp image transients, e.g. at tissue
boundaries. The origin can be seen in the truncation of k-space during MRI
data-acquisition. Consequently, correction techniques like Gegenbauer
reconstruction or extrapolation methods aim at recovering these missing data.
Here, we present a simple and robust method which exploits a different view on
the Gibbs-phenomena. The truncation in k-space can be interpreted as a
convolution with a sinc-function in image space. Hence, the severity of the
artifacts depends on how the sinc-function is sampled. We propose to
re-interpolate the image based on local, subvoxel shifts to sample the ringing
pattern at the zero-crossings of the oscillating sinc-function. With this, the
artifact can effectively and robustly be removed with a minimal amount of
smoothing.
Co-Regularized Deep Representations for Video Summarization
Authors: Olivier Morère, Hanlin Goh, Antoine Veillard, Vijay Chandrasekhar, Jie Lin
Date: 2015-01-30 00:00:00
Tasks: Informativeness, Video Summarization

Compact keyframe-based video summaries are a popular way of generating
viewership on video sharing platforms. Yet, creating relevant and compelling
summaries for arbitrarily long videos with a small number of keyframes is a
challenging task. We propose a comprehensive keyframe-based summarization
framework combining deep convolutional neural networks and restricted Boltzmann
machines. An original co-regularization scheme is used to discover meaningful
subject-scene associations. The resulting multimodal representations are then
used to select highly-relevant keyframes. A comprehensive user study is
conducted comparing our proposed method to a variety of schemes, including the
summarization currently in use by one of the most popular video sharing
websites. The results show that our method consistently outperforms the
baseline schemes for any given amount of keyframes both in terms of
attractiveness and informativeness. The lead is even more significant for
smaller summaries.
Blob indentation identification via curvature measurement
Authors: Matthew Sottile
Date: 2015-01-30 00:00:00

This paper presents a novel method for identifying indentations on the
boundary of solid 2D shape. It uses the signed curvature at a set of points
along the boundary to identify indentations and provides one parameter for
tuning the selection mechanism for discriminating indentations from other
boundary irregularities. An efficient implementation is described based on the
Fourier transform for calculating curvature from a sequence of points obtained
from the boundary of a binary blob.