pwc-archive/papers-with-abstracts huggingface dataset:
Classification of Alzheimer's Disease using fMRI Data and Deep Learning Convolutional Neural Networks
Authors: Saman Sarraf, Ghassem Tofighi
Date: 2016-03-29 00:00:00
Tasks: BIG-bench Machine Learning, General Classification

Over the past decade, machine learning techniques especially predictive
modeling and pattern recognition in biomedical sciences from drug delivery
system to medical imaging has become one of the important methods which are
assisting researchers to have deeper understanding of entire issue and to solve
complex medical problems. Deep learning is power learning machine learning
algorithm in classification while extracting high-level features. In this
paper, we used convolutional neural network to classify Alzheimer's brain from
normal healthy brain. The importance of classifying this kind of medical data
is to potentially develop a predict model or system in order to recognize the
type disease from normal subjects or to estimate the stage of the disease.
Classification of clinical data such as Alzheimer's disease has been always
challenging and most problematic part has been always selecting the most
discriminative features. Using Convolutional Neural Network (CNN) and the
famous architecture LeNet-5, we successfully classified functional MRI data of
Alzheimer's subjects from normal controls where the accuracy of test data on
trained data reached 96.85%. This experiment suggests us the shift and scale
invariant features extracted by CNN followed by deep learning classification is
most powerful method to distinguish clinical data from healthy data in fMRI.
This approach also enables us to expand our methodology to predict more
complicated systems.
Weakly Supervised Localization using Deep Feature Maps
Authors: Archith J. Bency, Heesung Kwon, Hyungtae Lee, S. Karthikeyan, B. S. Manjunath
Date: 2016-03-01 00:00:00
Tasks: General Classification, Object, Object Localization, Object Recognition, Weakly Supervised Object Detection

Object localization is an important computer vision problem with a variety of
applications. The lack of large scale object-level annotations and the relative
abundance of image-level labels makes a compelling case for weak supervision in
the object localization task. Deep Convolutional Neural Networks are a class of
state-of-the-art methods for the related problem of object recognition. In this
paper, we describe a novel object localization algorithm which uses
classification networks trained on only image labels. This weakly supervised
method leverages local spatial and semantic patterns captured in the
convolutional layers of classification networks. We propose an efficient beam
search based approach to detect and localize multiple objects in images. The
proposed method significantly outperforms the state-of-the-art in standard
object localization data-sets with a 8 point increase in mAP scores.
Combinatorial Multi-Armed Bandit and Its Extension to Probabilistically Triggered Arms
Authors: Wei Chen, Yajun Wang, Yang Yuan, Qinshi Wang
Date: 2014-07-31 00:00:00

We define a general framework for a large class of combinatorial multi-armed
bandit (CMAB) problems, where subsets of base arms with unknown distributions
form super arms. In each round, a super arm is played and the base arms
contained in the super arm are played and their outcomes are observed. We
further consider the extension in which more based arms could be
probabilistically triggered based on the outcomes of already triggered arms.
The reward of the super arm depends on the outcomes of all played arms, and it
only needs to satisfy two mild assumptions, which allow a large class of
nonlinear reward instances. We assume the availability of an offline
(\alpha,\beta)-approximation oracle that takes the means of the outcome
distributions of arms and outputs a super arm that with probability {\beta}
generates an {\alpha} fraction of the optimal expected reward. The objective of
an online learning algorithm for CMAB is to minimize
(\alpha,\beta)-approximation regret, which is the difference between the
\alpha{\beta} fraction of the expected reward when always playing the optimal
super arm, and the expected reward of playing super arms according to the
algorithm. We provide CUCB algorithm that achieves O(log n)
distribution-dependent regret, where n is the number of rounds played, and we
further provide distribution-independent bounds for a large class of reward
functions. Our regret analysis is tight in that it matches the bound of UCB1
algorithm (up to a constant factor) for the classical MAB problem, and it
significantly improves the regret bound in a earlier paper on combinatorial
bandits with linear rewards. We apply our CMAB framework to two new
applications, probabilistic maximum coverage and social influence maximization,
both having nonlinear reward structures. In particular, application to social
influence maximization requires our extension on probabilistically triggered
arms.
The Conditional Lucas & Kanade Algorithm
Authors: Chen-Hsuan Lin, Rui Zhu, Simon Lucey
Date: 2016-03-29 00:00:00

The Lucas & Kanade (LK) algorithm is the method of choice for efficient dense
image and object alignment. The approach is efficient as it attempts to model
the connection between appearance and geometric displacement through a linear
relationship that assumes independence across pixel coordinates. A drawback of
the approach, however, is its generative nature. Specifically, its performance
is tightly coupled with how well the linear model can synthesize appearance
from geometric displacement, even though the alignment task itself is
associated with the inverse problem. In this paper, we present a new approach,
referred to as the Conditional LK algorithm, which: (i) directly learns linear
models that predict geometric displacement as a function of appearance, and
(ii) employs a novel strategy for ensuring that the generative pixel
independence assumption can still be taken advantage of. We demonstrate that
our approach exhibits superior performance to classical generative forms of the
LK algorithm. Furthermore, we demonstrate its comparable performance to
state-of-the-art methods such as the Supervised Descent Method with
substantially less training examples, as well as the unique ability to "swap"
geometric warp functions without having to retrain from scratch. Finally, from
a theoretical perspective, our approach hints at possible redundancies that
exist in current state-of-the-art methods for alignment that could be leveraged
in vision systems of the future.
Long-Range Motion Trajectories Extraction of Articulated Human Using Mesh Evolution
Authors: Yuanyuan Wu, Xiaohai He, Byeongkeun Kang, Haiying Song, Truong Q. Nguyen
Date: 2015-06-30 00:00:00
Tasks: Motion Estimation

This letter presents a novel approach to extract reliable dense and
long-range motion trajectories of articulated human in a video sequence.
Compared with existing approaches that emphasize temporal consistency of each
tracked point, we also consider the spatial structure of tracked points on the
articulated human. We treat points as a set of vertices, and build a triangle
mesh to join them in image space. The problem of extracting long-range motion
trajectories is changed to the issue of consistency of mesh evolution over
time. First, self-occlusion is detected by a novel mesh-based method and an
adaptive motion estimation method is proposed to initialize mesh between
successive frames. Furthermore, we propose an iterative algorithm to
efficiently adjust vertices of mesh for a physically plausible deformation,
which can meet the local rigidity of mesh and silhouette constraints. Finally,
we compare the proposed method with the state-of-the-art methods on a set of
challenging sequences. Evaluations demonstrate that our method achieves
favorable performance in terms of both accuracy and integrity of extracted
trajectories.
Prepositional Attachment Disambiguation Using Bilingual Parsing and Alignments
Authors: Geetanjali Rakshit, Sagar Sontakke, Pushpak Bhattacharyya, Gholamreza Haffari
Date: 2016-03-29 00:00:00
Tasks: Machine Translation, Sentence, Translation

In this paper, we attempt to solve the problem of Prepositional Phrase (PP)
attachments in English. The motivation for the work comes from NLP applications
like Machine Translation, for which, getting the correct attachment of
prepositions is very crucial. The idea is to correct the PP-attachments for a
sentence with the help of alignments from parallel data in another language.
The novelty of our work lies in the formulation of the problem into a dual
decomposition based algorithm that enforces agreement between the parse trees
from two languages as a constraint. Experiments were performed on the
English-Hindi language pair and the performance improved by 10% over the
baseline, where the baseline is the attachment predicted by the MSTParser model
trained for English.
Exploring Local Context for Multi-target Tracking in Wide Area Aerial Surveillance
Authors: Bor-Jeng Chen, Gerard Medioni
Date: 2016-03-28 00:00:00

Tracking many vehicles in wide coverage aerial imagery is crucial for
understanding events in a large field of view. Most approaches aim to associate
detections from frame differencing into tracks. However, slow or stopped
vehicles result in long-term missing detections and further cause tracking
discontinuities. Relying merely on appearance clue to recover missing
detections is difficult as targets are extremely small and in grayscale. In
this paper, we address the limitations of detection association methods by
coupling it with a local context tracker (LCT), which does not rely on motion
detections. On one hand, our LCT learns neighboring spatial relation and tracks
each target in consecutive frames using graph optimization. It takes the
advantage of context constraints to avoid drifting to nearby targets. We
generate hypotheses from sparse and dense flow efficiently to keep solutions
tractable. On the other hand, we use detection association strategy to extract
short tracks in batch processing. We explicitly handle merged detections by
generating additional hypotheses from them. Our evaluation on wide area aerial
imagery sequences shows significant improvement over state-of-the-art methods.
Generalized Exponential Concentration Inequality for Rényi Divergence Estimation
Authors: Shashank Singh, Barnabás Póczos
Date: 2016-03-28 00:00:00
Tasks: BIG-bench Machine Learning

Estimating divergences in a consistent way is of great importance in many
machine learning tasks. Although this is a fundamental problem in nonparametric
statistics, to the best of our knowledge there has been no finite sample
exponential inequality convergence bound derived for any divergence estimators.
The main contribution of our work is to provide such a bound for an estimator
of R\'enyi-$\alpha$ divergence for a smooth H\"older class of densities on the
$d$-dimensional unit cube $[0, 1]^d$. We also illustrate our theoretical
results with a numerical experiment.
Exponential Concentration of a Density Functional Estimator
Authors: Shashank Singh, Barnabás P óczos
Date: 2016-03-28 00:00:00
Proceeding: NeurIPS 2014 12

We analyze a plug-in estimator for a large class of integral functionals of
one or more continuous probability densities. This class includes important
families of entropy, divergence, mutual information, and their conditional
versions. For densities on the $d$-dimensional unit cube $[0,1]^d$ that lie in
a $\beta$-H\"older smoothness class, we prove our estimator converges at the
rate $O \left( n^{-\frac{\beta}{\beta + d}} \right)$. Furthermore, we prove the
estimator is exponentially concentrated about its mean, whereas most previous
related results have proven only expected error bounds on estimators.
Analysis of classifiers' robustness to adversarial perturbations
Authors: Alhussein Fawzi, Omar Fawzi, Pascal Frossard
Date: 2015-02-09 00:00:00
Tasks: General Classification

The goal of this paper is to analyze an intriguing phenomenon recently
discovered in deep networks, namely their instability to adversarial
perturbations (Szegedy et. al., 2014). We provide a theoretical framework for
analyzing the robustness of classifiers to adversarial perturbations, and show
fundamental upper bounds on the robustness of classifiers. Specifically, we
establish a general upper bound on the robustness of classifiers to adversarial
perturbations, and then illustrate the obtained upper bound on the families of
linear and quadratic classifiers. In both cases, our upper bound depends on a
distinguishability measure that captures the notion of difficulty of the
classification task. Our results for both classes imply that in tasks involving
small distinguishability, no classifier in the considered set will be robust to
adversarial perturbations, even if a good accuracy is achieved. Our theoretical
framework moreover suggests that the phenomenon of adversarial instability is
due to the low flexibility of classifiers, compared to the difficulty of the
classification task (captured by the distinguishability). Moreover, we show the
existence of a clear distinction between the robustness of a classifier to
random noise and its robustness to adversarial perturbations. Specifically, the
former is shown to be larger than the latter by a factor that is proportional
to \sqrt{d} (with d being the signal dimension) for linear classifiers. This
result gives a theoretical explanation for the discrepancy between the two
robustness properties in high dimensional problems, which was empirically
observed in the context of neural networks. To the best of our knowledge, our
results provide the first theoretical work that addresses the phenomenon of
adversarial instability recently observed for deep networks. Our analysis is
complemented by experimental results on controlled and real-world data.
Zoom Better to See Clearer: Human and Object Parsing with Hierarchical Auto-Zoom Net
Authors: Fangting Xia, Peng Wang, Liang-Chieh Chen, Alan L. Yuille
Date: 2015-11-21 00:00:00

Parsing articulated objects, e.g. humans and animals, into semantic parts
(e.g. body, head and arms, etc.) from natural images is a challenging and
fundamental problem for computer vision. A big difficulty is the large
variability of scale and location for objects and their corresponding parts.
Even limited mistakes in estimating scale and location will degrade the parsing
output and cause errors in boundary details. To tackle these difficulties, we
propose a "Hierarchical Auto-Zoom Net" (HAZN) for object part parsing which
adapts to the local scales of objects and parts. HAZN is a sequence of two
"Auto-Zoom Net" (AZNs), each employing fully convolutional networks that
perform two tasks: (1) predict the locations and scales of object instances
(the first AZN) or their parts (the second AZN); (2) estimate the part scores
for predicted object instance or part regions. Our model can adaptively "zoom"
(resize) predicted image regions into their proper scales to refine the
parsing.
  We conduct extensive experiments over the PASCAL part datasets on humans,
horses, and cows. For humans, our approach significantly outperforms the
state-of-the-arts by 5% mIOU and is especially better at segmenting small
instances and small parts. We obtain similar improvements for parsing cows and
horses over alternative methods. In summary, our strategy of first zooming into
objects and then zooming into parts is very effective. It also enables us to
process different regions of the image at different scales adaptively so that,
for example, we do not need to waste computational resources scaling the entire
image.
One-to-many face recognition with bilinear CNNs
Authors: Aruni RoyChowdhury, Tsung-Yu Lin, Subhransu Maji, Erik Learned-Miller
Date: 2015-06-03 00:00:00
Tasks: Face Detection, Face Model, Face Recognition

The recent explosive growth in convolutional neural network (CNN) research
has produced a variety of new architectures for deep learning. One intriguing
new architecture is the bilinear CNN (B-CNN), which has shown dramatic
performance gains on certain fine-grained recognition problems [15]. We apply
this new CNN to the challenging new face recognition benchmark, the IARPA Janus
Benchmark A (IJB-A) [12]. It features faces from a large number of identities
in challenging real-world conditions. Because the face images were not
identified automatically using a computerized face detection system, it does
not have the bias inherent in such a database. We demonstrate the performance
of the B-CNN model beginning from an AlexNet-style network pre-trained on
ImageNet. We then show results for fine-tuning using a moderate-sized and
public external database, FaceScrub [17]. We also present results with
additional fine-tuning on the limited training data provided by the protocol.
In each case, the fine-tuned bilinear model shows substantial improvements over
the standard CNN. Finally, we demonstrate how a standard CNN pre-trained on a
large face database, the recently released VGG-Face model [20], can be
converted into a B-CNN without any additional feature training. This B-CNN
improves upon the CNN performance on the IJB-A benchmark, achieving 89.5%
rank-1 recall.
Kernelized Weighted SUSAN based Fuzzy C-Means Clustering for Noisy Image Segmentation
Authors: Satrajit Mukherjee, Bodhisattwa Prasad Majumder, Aritran Piplai, Swagatam Das
Date: 2016-03-28 00:00:00
Tasks: Clustering, Image Segmentation, Semantic Segmentation

The paper proposes a novel Kernelized image segmentation scheme for noisy
images that utilizes the concept of Smallest Univalue Segment Assimilating
Nucleus (SUSAN) and incorporates spatial constraints by computing circular
colour map induced weights. Fuzzy damping coefficients are obtained for each
nucleus or center pixel on the basis of the corresponding weighted SUSAN area
values, the weights being equal to the inverse of the number of horizontal and
vertical moves required to reach a neighborhood pixel from the center pixel.
These weights are used to vary the contributions of the different nuclei in the
Kernel based framework. The paper also presents an edge quality metric obtained
by fuzzy decision based edge candidate selection and final computation of the
blurriness of the edges after their selection. The inability of existing
algorithms to preserve edge information and structural details in their
segmented maps necessitates the computation of the edge quality factor (EQF)
for all the competing algorithms. Qualitative and quantitative analysis have
been rendered with respect to state-of-the-art algorithms and for images ridden
with varying types of noises. Speckle noise ridden SAR images and Rician noise
ridden Magnetic Resonance Images have also been considered for evaluating the
effectiveness of the proposed algorithm in extracting important segmentation
information.
Genetic cellular neural networks for generating three-dimensional geometry
Authors: Hugo Martay
Date: 2016-03-28 00:00:00

There are a number of ways to procedurally generate interesting
three-dimensional shapes, and a method where a cellular neural network is
combined with a mesh growth algorithm is presented here. The aim is to create a
shape from a genetic code in such a way that a crude search can find
interesting shapes. Identical neural networks are placed at each vertex of a
mesh which can communicate with neural networks on neighboring vertices. The
output of the neural networks determine how the mesh grows, allowing
interesting shapes to be produced emergently, mimicking some of the complexity
of biological organism development. Since the neural networks' parameters can
be freely mutated, the approach is amenable for use in a genetic algorithm.
Generating Visual Explanations
Authors: Lisa Anne Hendricks, Zeynep Akata, Marcus Rohrbach, Jeff Donahue, Bernt Schiele, Trevor Darrell
Date: 2016-03-28 00:00:00
Tasks: General Classification, Reinforcement Learning, Sentence, Specificity

Clearly explaining a rationale for a classification decision to an end-user
can be as important as the decision itself. Existing approaches for deep visual
recognition are generally opaque and do not output any justification text;
contemporary vision-language models can describe image content but fail to take
into account class-discriminative image aspects which justify visual
predictions. We propose a new model that focuses on the discriminating
properties of the visible object, jointly predicts a class label, and explains
why the predicted label is appropriate for the image. We propose a novel loss
function based on sampling and reinforcement learning that learns to generate
sentences that realize a global sentence property, such as class specificity.
Our results on a fine-grained bird species classification dataset show that our
model is able to generate explanations which are not only consistent with an
image but also more discriminative than descriptions produced by existing
captioning methods.
Learning to Read Chest X-Rays: Recurrent Neural Cascade Model for Automated Image Annotation
Authors: Hoo-chang Shin, Kirk Roberts, Le Lu, Dina Demner-Fushman, Jianhua Yao, Ronald M. Summers
Date: 2016-03-28 00:00:00
Proceeding: CVPR 2016 6

Despite the recent advances in automatically describing image contents, their
applications have been mostly limited to image caption datasets containing
natural images (e.g., Flickr 30k, MSCOCO). In this paper, we present a deep
learning model to efficiently detect a disease from an image and annotate its
contexts (e.g., location, severity and the affected organs). We employ a
publicly available radiology dataset of chest x-rays and their reports, and use
its image annotations to mine disease names to train convolutional neural
networks (CNNs). In doing so, we adopt various regularization techniques to
circumvent the large normal-vs-diseased cases bias. Recurrent neural networks
(RNNs) are then trained to describe the contexts of a detected disease, based
on the deep CNN features. Moreover, we introduce a novel approach to use the
weights of the already trained pair of CNN/RNN on the domain-specific
image/text dataset, to infer the joint image/text contexts for composite image
labeling. Significantly improved image annotation results are demonstrated
using the recurrent neural cascade model by taking the joint image/text
contexts into account.
Estimating Mixture Models via Mixtures of Polynomials
Authors: Sida I. Wang, Arun Tejasvi Chaganty, Percy Liang
Date: 2016-03-28 00:00:00
Proceeding: NeurIPS 2015 12

Mixture modeling is a general technique for making any simple model more
expressive through weighted combination. This generality and simplicity in part
explains the success of the Expectation Maximization (EM) algorithm, in which
updates are easy to derive for a wide class of mixture models. However, the
likelihood of a mixture model is non-convex, so EM has no known global
convergence guarantees. Recently, method of moments approaches offer global
guarantees for some mixture models, but they do not extend easily to the range
of mixture models that exist. In this work, we present Polymom, an unifying
framework based on method of moments in which estimation procedures are easily
derivable, just as in EM. Polymom is applicable when the moments of a single
mixture component are polynomials of the parameters. Our key observation is
that the moments of the mixture model are a mixture of these polynomials, which
allows us to cast estimation as a Generalized Moment Problem. We solve its
relaxations using semidefinite optimization, and then extract parameters using
ideas from computer algebra. This framework allows us to draw insights and
apply tools from convex optimization, computer algebra and the theory of
moments to study problems in statistical estimation.