pwc-archive/papers-with-abstracts huggingface dataset:
Discriminative Sparse Neighbor Approximation for Imbalanced Learning
Authors: Chen Huang, Chen Change Loy, Xiaoou Tang
Date: 2016-02-03 00:00:00
Tasks: Prediction

Data imbalance is common in many vision tasks where one or more classes are
rare. Without addressing this issue conventional methods tend to be biased
toward the majority class with poor predictive accuracy for the minority class.
These methods further deteriorate on small, imbalanced data that has a large
degree of class overlap. In this study, we propose a novel discriminative
sparse neighbor approximation (DSNA) method to ameliorate the effect of
class-imbalance during prediction. Specifically, given a test sample, we first
traverse it through a cost-sensitive decision forest to collect a good subset
of training examples in its local neighborhood. Then we generate from this
subset several class-discriminating but overlapping clusters and model each as
an affine subspace. From these subspaces, the proposed DSNA iteratively seeks
an optimal approximation of the test sample and outputs an unbiased prediction.
We show that our method not only effectively mitigates the imbalance issue, but
also allows the prediction to extrapolate to unseen data. The latter capability
is crucial for achieving accurate prediction on small dataset with limited
samples. The proposed imbalanced learning method can be applied to both
classification and regression tasks at a wide range of imbalance levels. It
significantly outperforms the state-of-the-art methods that do not possess an
imbalance handling mechanism, and is found to perform comparably or even better
than recent deep learning methods by using hand-crafted features only.
Single-Solution Hypervolume Maximization and its use for Improving Generalization of Neural Networks
Authors: Conrado S. Miranda, Fernando J. Von Zuben
Date: 2016-02-03 00:00:00

This paper introduces the hypervolume maximization with a single solution as
an alternative to the mean loss minimization. The relationship between the two
problems is proved through bounds on the cost function when an optimal solution
to one of the problems is evaluated on the other, with a hyperparameter to
control the similarity between the two problems. This same hyperparameter
allows higher weight to be placed on samples with higher loss when computing
the hypervolume's gradient, whose normalized version can range from the mean
loss to the max loss. An experiment on MNIST with a neural network is used to
validate the theory developed, showing that the hypervolume maximization can
behave similarly to the mean loss minimization and can also provide better
performance, resulting on a 20% reduction of the classification error on the
test set.
GraphPrints: Towards a Graph Analytic Method for Network Anomaly Detection
Authors: Christopher R. Harshaw, Robert A. Bridges, Michael D. Iannacone, Joel W. Reed, John R. Goodall
Date: 2016-02-02 00:00:00
Tasks: Anomaly Detection, Outlier Detection

This paper introduces a novel graph-analytic approach for detecting anomalies
in network flow data called GraphPrints. Building on foundational
network-mining techniques, our method represents time slices of traffic as a
graph, then counts graphlets -- small induced subgraphs that describe local
topology. By performing outlier detection on the sequence of graphlet counts,
anomalous intervals of traffic are identified, and furthermore, individual IPs
experiencing abnormal behavior are singled-out. Initial testing of GraphPrints
is performed on real network data with an implanted anomaly. Evaluation shows
false positive rates bounded by 2.84% at the time-interval level, and 0.05% at
the IP-level with 100% true positive rates at both.
On the Nyström and Column-Sampling Methods for the Approximate Principal Components Analysis of Large Data Sets
Authors: Darren Homrighausen, Daniel J. McDonald
Date: 2016-02-02 00:00:00
Tasks: Dimensionality Reduction

In this paper we analyze approximate methods for undertaking a principal
components analysis (PCA) on large data sets. PCA is a classical dimension
reduction method that involves the projection of the data onto the subspace
spanned by the leading eigenvectors of the covariance matrix. This projection
can be used either for exploratory purposes or as an input for further
analysis, e.g. regression. If the data have billions of entries or more, the
computational and storage requirements for saving and manipulating the design
matrix in fast memory is prohibitive. Recently, the Nystr\"om and
column-sampling methods have appeared in the numerical linear algebra community
for the randomized approximation of the singular value decomposition of large
matrices. However, their utility for statistical applications remains unclear.
We compare these approximations theoretically by bounding the distance between
the induced subspaces and the desired, but computationally infeasible, PCA
subspace. Additionally we show empirically, through simulations and a real data
example involving a corpus of emails, the trade-off of approximation accuracy
and computational complexity.
Do Cascades Recur?
Authors: Justin Cheng, Lada A. Adamic, Jon Kleinberg, Jure Leskovec
Date: 2016-02-02 00:00:00

Cascades of information-sharing are a primary mechanism by which content
reaches its audience on social media, and an active line of research has
studied how such cascades, which form as content is reshared from person to
person, develop and subside. In this paper, we perform a large-scale analysis
of cascades on Facebook over significantly longer time scales, and find that a
more complex picture emerges, in which many large cascades recur, exhibiting
multiple bursts of popularity with periods of quiescence in between. We
characterize recurrence by measuring the time elapsed between bursts, their
overlap and proximity in the social network, and the diversity in the
demographics of individuals participating in each peak. We discover that
content virality, as revealed by its initial popularity, is a main driver of
recurrence, with the availability of multiple copies of that content helping to
spark new bursts. Still, beyond a certain popularity of content, the rate of
recurrence drops as cascades start exhausting the population of interested
individuals. We reproduce these observed patterns in a simple model of content
recurrence simulated on a real social network. Using only characteristics of a
cascade's initial burst, we demonstrate strong performance in predicting
whether it will recur in the future.
Self-taught Object Localization with Deep Networks
Authors: Loris Bazzani, Alessandro Bergamo, Dragomir Anguelov, Lorenzo Torresani
Date: 2014-09-13 00:00:00
Tasks: Clustering, Object, Object Localization

This paper introduces self-taught object localization, a novel approach that
leverages deep convolutional networks trained for whole-image recognition to
localize objects in images without additional human supervision, i.e., without
using any ground-truth bounding boxes for training. The key idea is to analyze
the change in the recognition scores when artificially masking out different
regions of the image. The masking out of a region that includes the object
typically causes a significant drop in recognition score. This idea is embedded
into an agglomerative clustering technique that generates self-taught
localization hypotheses. Our object localization scheme outperforms existing
proposal methods in both precision and recall for small number of subwindow
proposals (e.g., on ILSVRC-2012 it produces a relative gain of 23.4% over the
state-of-the-art for top-1 hypothesis). Furthermore, our experiments show that
the annotations automatically-generated by our method can be used to train
object detectors yielding recognition results remarkably close to those
obtained by training on manually-annotated bounding boxes.
Development of an Ideal Observer that Incorporates Nuisance Parameters and Processes List-Mode Data
Authors: Christopher J. MacGahan, Matthew A. Kupinski, Nathan R. Hilton, Erik M. Brubaker, William C. Johnson
Date: 2016-02-02 00:00:00

Observer models were developed to process data in list-mode format in order
to perform binary discrimination tasks for use in an arms-control-treaty
context. Data used in this study was generated using GEANT4 Monte Carlo
simulations for photons using custom models of plutonium inspection objects and
a radiation imaging system. Observer model performance was evaluated and
presented using the area under the receiver operating characteristic curve. The
ideal observer was studied under both signal-known-exactly conditions and in
the presence of unknowns such as object orientation and absolute count-rate
variability; when these additional sources of randomness were present, their
incorporation into the observer yielded superior performance.
A Comparative Study of Ranking-based Semantics for Abstract Argumentation
Authors: Elise Bonzon, Jérôme Delobelle, Sébastien Konieczny, Nicolas Maudet
Date: 2016-02-02 00:00:00
Tasks: Abstract Argumentation

Argumentation is a process of evaluating and comparing a set of arguments. A
way to compare them consists in using a ranking-based semantics which
rank-order arguments from the most to the least acceptable ones. Recently, a
number of such semantics have been proposed independently, often associated
with some desirable properties. However, there is no comparative study which
takes a broader perspective. This is what we propose in this work. We provide a
general comparison of all these semantics with respect to the proposed
properties. That allows to underline the differences of behavior between the
existing semantics.
Improved Achievability and Converse Bounds for Erdős-Rényi Graph Matching
Authors: Daniel Cullina, Negar Kiyavash
Date: 2016-02-02 00:00:00
Tasks: Graph Matching

We consider the problem of perfectly recovering the vertex correspondence
between two correlated Erd\H{o}s-R\'enyi (ER) graphs. For a pair of correlated
graphs on the same vertex set, the correspondence between the vertices can be
obscured by randomly permuting the vertex labels of one of the graphs. In some
cases, the structural information in the graphs allow this correspondence to be
recovered. We investigate the information-theoretic threshold for exact
recovery, i.e. the conditions under which the entire vertex correspondence can
be correctly recovered given unbounded computational resources.
  Pedarsani and Grossglauser provided an achievability result of this type.
Their result establishes the scaling dependence of the threshold on the number
of vertices. We improve on their achievability bound. We also provide a
converse bound, establishing conditions under which exact recovery is
impossible. Together, these establish the scaling dependence of the threshold
on the level of correlation between the two graphs. The converse and
achievability bounds differ by a factor of two for sparse, significantly
correlated graphs.
On Deep Multi-View Representation Learning: Objectives and Optimization
Authors: Weiran Wang, Raman Arora, Karen Livescu, Jeff Bilmes
Date: 2016-02-02 00:00:00
Tasks: Representation Learning, Stochastic Optimization

We consider learning representations (features) in the setting in which we
have access to multiple unlabeled views of the data for learning while only one
view is available for downstream tasks. Previous work on this problem has
proposed several techniques based on deep neural networks, typically involving
either autoencoder-like networks with a reconstruction objective or paired
feedforward networks with a batch-style correlation-based objective. We analyze
several techniques based on prior work, as well as new variants, and compare
them empirically on image, speech, and text tasks. We find an advantage for
correlation-based representation learning, while the best results on most tasks
are obtained with our new variant, deep canonically correlated autoencoders
(DCCAE). We also explore a stochastic optimization procedure for minibatch
correlation-based objectives and discuss the time/performance trade-offs for
kernel-based and neural network-based implementations.
A-expansion for multiple "hedgehog" shapes
Authors: Hossam Isack, Yuri Boykov, Olga Veksler
Date: 2016-02-02 00:00:00
Tasks: Segmentation, Semantic Segmentation

Overlapping colors and cluttered or weak edges are common segmentation
problems requiring additional regularization. For example, star-convexity is
popular for interactive single object segmentation due to simplicity and
amenability to exact graph cut optimization. This paper proposes an approach to
multiobject segmentation where objects could be restricted to separate
"hedgehog" shapes. We show that a-expansion moves are submodular for our
multi-shape constraints. Each "hedgehog" shape has its surface normals
constrained by some vector field, e.g. gradients of a distance transform for
user scribbles. Tight constraint give an extreme case of a shape prior
enforcing skeleton consistency with the scribbles. Wider cones of allowed
normals gives more relaxed hedgehog shapes. A single click and +/-90 degrees
normal orientation constraints reduce our hedgehog prior to star-convexity. If
all hedgehogs come from single clicks then our approach defines multi-star
prior. Our general method has significantly more applications than standard
one-star segmentation. For example, in medical data we can separate multiple
non-star organs with similar appearances and weak or noisy edges.
What Can I Do Around Here? Deep Functional Scene Understanding for Cognitive Robots
Authors: Chengxi Ye, Yezhou Yang, Cornelia Fermuller, Yiannis Aloimonos
Date: 2016-01-29 00:00:00
Tasks: image-classification, Image Classification, Object Recognition, Scene Understanding

For robots that have the capability to interact with the physical environment
through their end effectors, understanding the surrounding scenes is not merely
a task of image classification or object recognition. To perform actual tasks,
it is critical for the robot to have a functional understanding of the visual
scene. Here, we address the problem of localizing and recognition of functional
areas from an arbitrary indoor scene, formulated as a two-stage deep learning
based detection pipeline. A new scene functionality testing-bed, which is
complied from two publicly available indoor scene datasets, is used for
evaluation. Our method is evaluated quantitatively on the new dataset,
demonstrating the ability to perform efficient recognition of functional areas
from arbitrary indoor scenes. We also demonstrate that our detection model can
be generalized onto novel indoor scenes by cross validating it with the images
from two different datasets.
Head Pose Estimation of Occluded Faces using Regularized Regression
Authors: Amit Kumar, Rishabh Bindal, Soumya Indela, Michael Rotkowitz
Date: 2016-02-02 00:00:00
Tasks: General Classification, Head Pose Estimation, Pose Estimation, regression

This paper presents regression methods for estimation of head pose from
occluded 2-D face images. The process primarily involves reconstructing a face
from its occluded image, followed by classification. Typical methods for
reconstruction assume that the pixel errors of the occluded regions are
independent. However, such an assumption is not true in the case of occlusion,
because of its inherent contiguous nature. Hence, we use nuclear norm as a
metric that can describe well the structure of the error. We also use LASSO
Regression based l1 - regularization to improve reconstruction. Next, we
implement Nuclear Norm Regularized Regression (NR), and also our proposed
method, for reconstruction and subsequent classification. Finally, we compare
the performance of the methods in terms of accuracy of head pose estimation of
occluded faces.
Marvin: Semantic annotation using multiple knowledge sources
Authors: Nikola Milosevic
Date: 2016-02-01 00:00:00
Tasks: Information Retrieval, Retrieval, text annotation

People are producing more written material then anytime in the history. The
increase is so high that professionals from the various fields are no more able
to cope with this amount of publications. Text mining tools can offer tools to
help them and one of the tools that can aid information retrieval and
information extraction is semantic text annotation. In this report we present
Marvin, a text annotator written in Java, which can be used as a command line
tool and as a Java library. Marvin is able to annotate text using multiple
sources, including WordNet, MetaMap, DBPedia and thesauri represented as SKOS.
On distances, paths and connections for hyperspectral image segmentation
Authors: Guillaume Noyel, Jesus Angulo, Dominique Jeulin
Date: 2016-02-02 00:00:00
Tasks: Hyperspectral Image Segmentation, Image Segmentation, Segmentation, Semantic Segmentation

The present paper introduces the $\eta$ and {\eta} connections in order to
add regional information on $\lambda$-flat zones, which only take into account
a local information. A top-down approach is considered. First $\lambda$-flat
zones are built in a way leading to a sub-segmentation. Then a finer
segmentation is obtained by computing $\eta$-bounded regions and $\mu$-geodesic
balls inside the $\lambda$-flat zones. The proposed algorithms for the
construction of new partitions are based on queues with an ordered selection of
seeds using the cumulative distance. $\eta$-bounded regions offers a control on
the variations of amplitude in the class from a point, called center, and
$\mu$-geodesic balls controls the "size" of the class. These results are
applied to hyperspectral images.
Fast unsupervised Bayesian image segmentation with adaptive spatial regularisation
Authors: Marcelo Pereyra, Steve McLaughlin
Date: 2015-02-05 00:00:00
Tasks: Clustering, Denoising, Image Segmentation, Segmentation, Semantic Segmentation

This paper presents a new Bayesian estimation technique for hidden
Potts-Markov random fields with unknown regularisation parameters, with
application to fast unsupervised K-class image segmentation. The technique is
derived by first removing the regularisation parameter from the Bayesian model
by marginalisation, followed by a small-variance-asymptotic (SVA) analysis in
which the spatial regularisation and the integer-constrained terms of the Potts
model are decoupled. The evaluation of this SVA Bayesian estimator is then
relaxed into a problem that can be computed efficiently by iteratively solving
a convex total-variation denoising problem and a least-squares clustering
(K-means) problem, both of which can be solved straightforwardly, even in
high-dimensions, and with parallel computing techniques. This leads to a fast
fully unsupervised Bayesian image segmentation methodology in which the
strength of the spatial regularisation is adapted automatically to the observed
image during the inference procedure, and that can be easily applied in large
2D and 3D scenarios or in applications requiring low computing times.
Experimental results on real images, as well as extensive comparisons with
state-of-the-art algorithms, confirm that the proposed methodology offer
extremely fast convergence and produces accurate segmentation results, with the
important additional advantage of self-adjusting regularisation parameters.
Learning a Deep Model for Human Action Recognition from Novel Viewpoints
Authors: Hossein Rahmani, Ajmal Mian, Mubarak Shah
Date: 2016-02-02 00:00:00
Tasks: Action Recognition, Temporal Action Localization, Transfer Learning

Recognizing human actions from unknown and unseen (novel) views is a
challenging problem. We propose a Robust Non-Linear Knowledge Transfer Model
(R-NKTM) for human action recognition from novel views. The proposed R-NKTM is
a deep fully-connected neural network that transfers knowledge of human actions
from any unknown view to a shared high-level virtual view by finding a
non-linear virtual path that connects the views. The R-NKTM is learned from
dense trajectories of synthetic 3D human models fitted to real motion capture
data and generalizes to real videos of human actions. The strength of our
technique is that we learn a single R-NKTM for all actions and all viewpoints
for knowledge transfer of any real human action video without the need for
re-training or fine-tuning the model. Thus, R-NKTM can efficiently scale to
incorporate new action classes. R-NKTM is learned with dummy labels and does
not require knowledge of the camera viewpoint at any stage. Experiments on
three benchmark cross-view human action datasets show that our method
outperforms existing state-of-the-art.
Large Scale Business Discovery from Street Level Imagery
Authors: Qian Yu, Christian Szegedy, Martin C. Stumpe, Liron Yatziv, Vinay Shet, Julian Ibarz, Sacha Arnoud
Date: 2015-12-17 00:00:00

Search with local intent is becoming increasingly useful due to the
popularity of the mobile device. The creation and maintenance of accurate
listings of local businesses worldwide is time consuming and expensive. In this
paper, we propose an approach to automatically discover businesses that are
visible on street level imagery. Precise business store front detection enables
accurate geo-location of businesses, and further provides input for business
categorization, listing generation, etc. The large variety of business
categories in different countries makes this a very challenging problem.
Moreover, manual annotation is prohibitive due to the scale of this problem. We
propose the use of a MultiBox based approach that takes input image pixels and
directly outputs store front bounding boxes. This end-to-end learning approach
instead preempts the need for hand modeling either the proposal generation
phase or the post-processing phase, leveraging large labelled training
datasets. We demonstrate our approach outperforms the state of the art
detection techniques with a large margin in terms of performance and run-time
efficiency. In the evaluation, we show this approach achieves human accuracy in
the low-recall settings. We also provide an end-to-end evaluation of business
discovery in the real world.
Are Elephants Bigger than Butterflies? Reasoning about Sizes of Objects
Authors: Hessam Bagherinezhad, Hannaneh Hajishirzi, Yejin Choi, Ali Farhadi
Date: 2016-02-02 00:00:00
Tasks: Visual Reasoning

Human vision greatly benefits from the information about sizes of objects.
The role of size in several visual reasoning tasks has been thoroughly explored
in human perception and cognition. However, the impact of the information about
sizes of objects is yet to be determined in AI. We postulate that this is
mainly attributed to the lack of a comprehensive repository of size
information. In this paper, we introduce a method to automatically infer object
sizes, leveraging visual and textual information from web. By maximizing the
joint likelihood of textual and visual observations, our method learns reliable
relative size estimates, with no explicit human supervision. We introduce the
relative size dataset and show that our method outperforms competitive textual
and visual baselines in reasoning about size comparisons.