pwc-archive/papers-with-abstracts huggingface dataset:
Image stitching with perspective-preserving warping
Authors: Tian-Zhu Xiang, Gui-Song Xia, Liangpei Zhang
Date: 2016-05-17 00:00:00
Tasks: Image Stitching

Image stitching algorithms often adopt the global transformation, such as
homography, and work well for planar scenes or parallax free camera motions.
However, these conditions are easily violated in practice. With casual camera
motions, variable taken views, large depth change, or complex structures, it is
a challenging task for stitching these images. The global transformation model
often provides dreadful stitching results, such as misalignments or projective
distortions, especially perspective distortion. To this end, we suggest a
perspective-preserving warping for image stitching, which spatially combines
local projective transformations and similarity transformation. By weighted
combination scheme, our approach gradually extrapolates the local projective
transformations of the overlapping regions into the non-overlapping regions,
and thus the final warping can smoothly change from projective to similarity.
The proposed method can provide satisfactory alignment accuracy as well as
reduce the projective distortions and maintain the multi-perspective view.
Experiments on a variety of challenging images confirm the efficiency of the
approach.
Adversarial Diversity and Hard Positive Generation
Authors: Andras Rozsa, Ethan M. Rudd, Terrance E. Boult
Date: 2016-05-05 00:00:00
Tasks: Data Augmentation, Diversity

State-of-the-art deep neural networks suffer from a fundamental problem -
they misclassify adversarial examples formed by applying small perturbations to
inputs. In this paper, we present a new psychometric perceptual adversarial
similarity score (PASS) measure for quantifying adversarial images, introduce
the notion of hard positive generation, and use a diverse set of adversarial
perturbations - not just the closest ones - for data augmentation. We introduce
a novel hot/cold approach for adversarial example generation, which provides
multiple possible adversarial perturbations for every single image. The
perturbations generated by our novel approach often correspond to semantically
meaningful image structures, and allow greater flexibility to scale
perturbation-amplitudes, which yields an increased diversity of adversarial
images. We present adversarial images on several network topologies and
datasets, including LeNet on the MNIST dataset, and GoogLeNet and ResidualNet
on the ImageNet dataset. Finally, we demonstrate on LeNet and GoogLeNet that
fine-tuning with a diverse set of hard positives improves the robustness of
these networks compared to training with prior methods of generating
adversarial images.
SemiContour: A Semi-supervised Learning Approach for Contour Detection
Authors: Zizhao Zhang, Fuyong Xing, Xiaoshuang Shi, Lin Yang
Date: 2016-05-17 00:00:00
Proceeding: CVPR 2016 6
Tasks: Contour Detection, Ensemble Learning

Supervised contour detection methods usually require many labeled training
images to obtain satisfactory performance. However, a large set of annotated
data might be unavailable or extremely labor intensive. In this paper, we
investigate the usage of semi-supervised learning (SSL) to obtain competitive
detection accuracy with very limited training data (three labeled images).
Specifically, we propose a semi-supervised structured ensemble learning
approach for contour detection built on structured random forests (SRF). To
allow SRF to be applicable to unlabeled data, we present an effective sparse
representation approach to capture inherent structure in image patches by
finding a compact and discriminative low-dimensional subspace representation in
an unsupervised manner, enabling the incorporation of abundant unlabeled
patches with their estimated structured labels to help SRF perform better node
splitting. We re-examine the role of sparsity and propose a novel and fast
sparse coding algorithm to boost the overall learning efficiency. To the best
of our knowledge, this is the first attempt to apply SSL for contour detection.
Extensive experiments on the BSDS500 segmentation dataset and the NYU Depth
dataset demonstrate the superiority of the proposed method.
A Constant-Factor Bi-Criteria Approximation Guarantee for $k$-means++
Authors: Dennis Wei
Date: 2016-05-16 00:00:00
Tasks: Clustering

This paper studies the $k$-means++ algorithm for clustering as well as the
class of $D^\ell$ sampling algorithms to which $k$-means++ belongs. It is shown
that for any constant factor $\beta > 1$, selecting $\beta k$ cluster centers
by $D^\ell$ sampling yields a constant-factor approximation to the optimal
clustering with $k$ centers, in expectation and without conditions on the
dataset. This result extends the previously known $O(\log k)$ guarantee for the
case $\beta = 1$ to the constant-factor bi-criteria regime. It also improves
upon an existing constant-factor bi-criteria result that holds only with
constant probability.
Convex Optimization for Linear Query Processing under Approximate Differential Privacy
Authors: Ganzhao Yuan, Yin Yang, Zhenjie Zhang, Zhifeng Hao
Date: 2016-02-13 00:00:00

Differential privacy enables organizations to collect accurate aggregates
over sensitive data with strong, rigorous guarantees on individuals' privacy.
Previous work has found that under differential privacy, computing multiple
correlated aggregates as a batch, using an appropriate \emph{strategy}, may
yield higher accuracy than computing each of them independently. However,
finding the best strategy that maximizes result accuracy is non-trivial, as it
involves solving a complex constrained optimization program that appears to be
non-linear and non-convex. Hence, in the past much effort has been devoted in
solving this non-convex optimization program. Existing approaches include
various sophisticated heuristics and expensive numerical solutions. None of
them, however, guarantees to find the optimal solution of this optimization
problem.
  This paper points out that under ($\epsilon$, $\delta$)-differential privacy,
the optimal solution of the above constrained optimization problem in search of
a suitable strategy can be found, rather surprisingly, by solving a simple and
elegant convex optimization program. Then, we propose an efficient algorithm
based on Newton's method, which we prove to always converge to the optimal
solution with linear global convergence rate and quadratic local convergence
rate. Empirical evaluations demonstrate the accuracy and efficiency of the
proposed solution.
Geometry of Interest (GOI): Spatio-Temporal Destination Extraction and Partitioning in GPS Trajectory Data
Authors: Seyed Morteza Mousavi, Aaron Harwood, Shanika Karunasekera, Mojtaba Maghrebi
Date: 2016-03-14 00:00:00
Tasks: Management

Nowadays large amounts of GPS trajectory data is being continuously collected
by GPS-enabled devices such as vehicles navigation systems and mobile phones.
GPS trajectory data is useful for applications such as traffic management,
location forecasting, and itinerary planning. Such applications often need to
extract the time-stamped Sequence of Visited Locations (SVLs) of the mobile
objects. The nearest neighbor query (NNQ) is the most applied method for
labeling the visited locations based on the IDs of the POIs in the process of
SVL generation. NNQ in some scenarios is not accurate enough. To improve the
quality of the extracted SVLs, instead of using NNQ, we label the visited
locations as the IDs of the POIs which geometrically intersect with the GPS
observations. Intersection operator requires the accurate geometry of the
points of interest which we refer to them as the Geometries of Interest (GOIs).
In some application domains (e.g. movement trajectories of animals), adequate
information about the POIs and their GOIs may not be available a priori, or
they may not be publicly accessible and, therefore, they need to be derived
from GPS trajectory data. In this paper we propose a novel method for
estimating the POIs and their GOIs, which consists of three phases: (i)
extracting the geometries of the stay regions; (ii) constructing the geometry
of destination regions based on the extracted stay regions; and (iii)
constructing the GOIs based on the geometries of the destination regions. Using
the geometric similarity to known GOIs as the major evaluation criterion, the
experiments we performed using long-term GPS trajectory data show that our
method outperforms the existing approaches.
Self-Reflective Risk-Aware Artificial Cognitive Modeling for Robot Response to Human Behaviors
Authors: Fei Han, Christopher Reardon, Lynne E. Parker, Hao Zhang
Date: 2016-05-16 00:00:00
Tasks: Decision Making

In order for cooperative robots ("co-robots") to respond to human behaviors
accurately and efficiently in human-robot collaboration, interpretation of
human actions, awareness of new situations, and appropriate decision making are
all crucial abilities for co-robots. For this purpose, the human behaviors
should be interpreted by co-robots in the same manner as human peers. To
address this issue, a novel interpretability indicator is introduced so that
robot actions are appropriate to the current human behaviors. In addition, the
complete consideration of all potential situations of a robot's environment is
nearly impossible in real-world applications, making it difficult for the
co-robot to act appropriately and safely in new scenarios. This is true even
when the pretrained model is highly accurate in a known situation. For
effective and safe teaming with humans, we introduce a new generalizability
indicator that allows a co-robot to self-reflect and reason about when an
observation falls outside the co-robot's learned model. Based on topic modeling
and two novel indicators, we propose a new Self-reflective Risk-aware
Artificial Cognitive (SRAC) model. The co-robots are able to consider action
risks and identify new situations so that better decisions can be made.
Experiments both using real-world datasets and on physical robots suggest that
our SRAC model significantly outperforms the traditional methodology and
enables better decision making in response to human activities.
Classification of Big Data with Application to Imaging Genetics
Authors: Magnus O. Ulfarsson, Frosti Palsson, Jakob Sigurdsson, Johannes R. Sveinsson
Date: 2016-05-16 00:00:00
Tasks: Classification, General Classification

Big data applications, such as medical imaging and genetics, typically
generate datasets that consist of few observations n on many more variables p,
a scenario that we denote as p>>n. Traditional data processing methods are
often insufficient for extracting information out of big data. This calls for
the development of new algorithms that can deal with the size, complexity, and
the special structure of such datasets. In this paper, we consider the problem
of classifying p>>n data and propose a classification method based on linear
discriminant analysis (LDA). Traditional LDA depends on the covariance estimate
of the data, but when p>>n the sample covariance estimate is singular. The
proposed method estimates the covariance by using a sparse version of noisy
principal component analysis (nPCA). The use of sparsity in this setting aims
at automatically selecting variables that are relevant for classification. In
experiments, the new method is compared to state-of-the art methods for big
data problems using both simulated datasets and imaging genetics datasets.
Large Scale Distributed Semi-Supervised Learning Using Streaming Approximation
Authors: Sujith Ravi, Qiming Diao
Date: 2015-12-06 00:00:00
Tasks: graph construction

Traditional graph-based semi-supervised learning (SSL) approaches, even
though widely applied, are not suited for massive data and large label
scenarios since they scale linearly with the number of edges $|E|$ and distinct
labels $m$. To deal with the large label size problem, recent works propose
sketch-based methods to approximate the distribution on labels per node thereby
achieving a space reduction from $O(m)$ to $O(\log m)$, under certain
conditions. In this paper, we present a novel streaming graph-based SSL
approximation that captures the sparsity of the label distribution and ensures
the algorithm propagates labels accurately, and further reduces the space
complexity per node to $O(1)$. We also provide a distributed version of the
algorithm that scales well to large data sizes. Experiments on real-world
datasets demonstrate that the new method achieves better performance than
existing state-of-the-art algorithms with significant reduction in memory
footprint. We also study different graph construction mechanisms for natural
language applications and propose a robust graph augmentation strategy trained
using state-of-the-art unsupervised deep learning architectures that yields
further significant quality gains.
Data-driven Sequential Monte Carlo in Probabilistic Programming
Authors: Yura N. Perov, Tuan Anh Le, Frank Wood
Date: 2015-12-14 00:00:00
Tasks: Probabilistic Programming

Most of Markov Chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC)
algorithms in existing probabilistic programming systems suboptimally use only
model priors as proposal distributions. In this work, we describe an approach
for training a discriminative model, namely a neural network, in order to
approximate the optimal proposal by using posterior estimates from previous
runs of inference. We show an example that incorporates a data-driven proposal
for use in a non-parametric model in the Anglican probabilistic programming
system. Our results show that data-driven proposals can significantly improve
inference performance so that considerably fewer particles are necessary to
perform a good posterior estimation.
On Distributed Cooperative Decision-Making in Multiarmed Bandits
Authors: Peter Landgren, Vaibhav Srivastava, Naomi Ehrich Leonard
Date: 2015-12-21 00:00:00
Tasks: Decision Making

We study the explore-exploit tradeoff in distributed cooperative decision-making using the context of the multiarmed bandit (MAB) problem. For the distributed cooperative MAB problem, we design the cooperative UCB algorithm that comprises two interleaved distributed processes: (i) running consensus algorithms for estimation of rewards, and (ii) upper-confidence-bound-based heuristics for selection of arms. We rigorously analyze the performance of the cooperative UCB algorithm and characterize the influence of communication graph structure on the decision-making performance of the group.
Reducing the Model Order of Deep Neural Networks Using Information Theory
Authors: Ming Tu, Visar Berisha, Yu Cao, Jae-sun Seo
Date: 2016-05-16 00:00:00
Tasks: General Classification, Network Pruning, Quantization, Stochastic Optimization

Deep neural networks are typically represented by a much larger number of
parameters than shallow models, making them prohibitive for small footprint
devices. Recent research shows that there is considerable redundancy in the
parameter space of deep neural networks. In this paper, we propose a method to
compress deep neural networks by using the Fisher Information metric, which we
estimate through a stochastic optimization method that keeps track of
second-order information in the network. We first remove unimportant parameters
and then use non-uniform fixed point quantization to assign more bits to
parameters with higher Fisher Information estimates. We evaluate our method on
a classification task with a convolutional neural network trained on the MNIST
data set. Experimental results show that our method outperforms existing
methods for both network pruning and quantization.
Video2GIF: Automatic Generation of Animated GIFs from Video
Authors: Michael Gygli, Yale Song, Liangliang Cao
Date: 2016-05-16 00:00:00
Proceeding: CVPR 2016 6

We introduce the novel problem of automatically generating animated GIFs from
video. GIFs are short looping video with no sound, and a perfect combination
between image and video that really capture our attention. GIFs tell a story,
express emotion, turn events into humorous moments, and are the new wave of
photojournalism. We pose the question: Can we automate the entirely manual and
elaborate process of GIF creation by leveraging the plethora of user generated
GIF content? We propose a Robust Deep RankNet that, given a video, generates a
ranked list of its segments according to their suitability as GIF. We train our
model to learn what visual content is often selected for GIFs by using over
100K user generated GIFs and their corresponding video sources. We effectively
deal with the noisy web data by proposing a novel adaptive Huber loss in the
ranking formulation. We show that our approach is robust to outliers and picks
up several patterns that are frequently present in popular animated GIFs. On
our new large-scale benchmark dataset, we show the advantage of our approach
over several state-of-the-art methods.
Antisocial Behavior in Online Discussion Communities
Authors: Justin Cheng, Cristian Danescu-Niculescu-Mizil, Jure Leskovec
Date: 2015-04-02 00:00:00

User contributions in the form of posts, comments, and votes are essential to
the success of online communities. However, allowing user participation also
invites undesirable behavior such as trolling. In this paper, we characterize
antisocial behavior in three large online discussion communities by analyzing
users who were banned from these communities. We find that such users tend to
concentrate their efforts in a small number of threads, are more likely to post
irrelevantly, and are more successful at garnering responses from other users.
Studying the evolution of these users from the moment they join a community up
to when they get banned, we find that not only do they write worse than other
users over time, but they also become increasingly less tolerated by the
community. Further, we discover that antisocial behavior is exacerbated when
community feedback is overly harsh. Our analysis also reveals distinct groups
of users with different levels of antisocial behavior that can change over
time. We use these insights to identify antisocial users early on, a task of
high practical importance to community maintainers.
Compressing Word Embeddings
Authors: Martin Andrews
Date: 2015-11-19 00:00:00
Tasks: Word Embeddings

Recent methods for learning vector space representations of words have
succeeded in capturing fine-grained semantic and syntactic regularities using
vector arithmetic. However, these vector space representations (created through
large-scale text analysis) are typically stored verbatim, since their internal
structure is opaque. Using word-analogy tests to monitor the level of detail
stored in compressed re-representations of the same vector space, the
trade-offs between the reduction in memory usage and expressiveness are
investigated. A simple scheme is outlined that can reduce the memory footprint
of a state-of-the-art embedding by a factor of 10, with only minimal impact on
performance. Then, using the same `bit budget', a binary (approximate)
factorisation of the same space is also explored, with the aim of creating an
equivalent representation with better interpretability.
Multilevel Thresholding Segmentation of T2 weighted Brain MRI images using Convergent Heterogeneous Particle Swarm Optimization
Authors: Mohammad Hamed Mozaffari, Won-Sook Lee
Date: 2016-05-16 00:00:00
Tasks: Segmentation

This paper proposes a new image thresholding segmentation approach using the
heuristic method, Convergent Heterogeneous Particle Swarm Optimization
algorithm. The proposed algorithm incorporates a new strategy of searching the
problem space by dividing the swarm into subswarms. Each subswarm particles
search for better solution separately lead to better exploitation while they
cooperate with each other to find the best global position. The consequence of
the aforementioned cooperation is better exploration, convergence and it able
the algorithm to jump from local optimal solution to the better spots. A
practical application of this method is demonstrated for the problem of medical
image thresholding segmentation. We considered two classical thresholding
techniques of Otsu and Kapur separately as the objective function for the
optimization method and applied on a set of brain MR images. Comparative
experimental results reveal that the proposed method outperforms another state
of the art method from the literature in terms of accuracy, computation time
and stable results.
Detecting Relative Anomaly
Authors: Richard Neuberg, Yixin Shi
Date: 2016-05-12 00:00:00
Tasks: Anomaly Detection, Unsupervised Anomaly Detection

System states that are anomalous from the perspective of a domain expert
occur frequently in some anomaly detection problems. The performance of
commonly used unsupervised anomaly detection methods may suffer in that
setting, because they use frequency as a proxy for anomaly. We propose a novel
concept for anomaly detection, called relative anomaly detection. It is
tailored to be robust towards anomalies that occur frequently, by taking into
account their location relative to the most typical observations. The
approaches we develop are computationally feasible even for large data sets,
and they allow real-time detection. We illustrate using data sets of potential
scraping attempts and Wi-Fi channel utilization, both from Google, Inc.
An Alternative Matting Laplacian
Authors: François Pitié
Date: 2016-05-16 00:00:00
Tasks: Image Matting

Cutting out and object and estimate its transparency mask is a key task in
many applications. We take on the work on closed-form matting by Levin et al.,
that is used at the core of many matting techniques, and propose an alternative
formulation that offers more flexible controls over the matting priors. We also
show that this new approach is efficient at upscaling transparency maps from
coarse estimates.
Geometry Aware Mappings for High Dimensional Sparse Factors
Authors: Avradeep Bhowmik, Nathan Liu, Erheng Zhong, Badri Narayan Bhaskar, Suju Rajan
Date: 2016-05-16 00:00:00
Tasks: Vocal Bursts Intensity Prediction

While matrix factorisation models are ubiquitous in large scale
recommendation and search, real time application of such models requires inner
product computations over an intractably large set of item factors. In this
manuscript we present a novel framework that uses the inverted index
representation to exploit structural properties of sparse vectors to
significantly reduce the run time computational cost of factorisation models.
We develop techniques that use geometry aware permutation maps on a tessellated
unit sphere to obtain high dimensional sparse embeddings for latent factors
with sparsity patterns related to angular closeness of the original latent
factors. We also design several efficient and deterministic realisations within
this framework and demonstrate with experiments that our techniques lead to
faster run time operation with minimal loss of accuracy.
Identification of promising research directions using machine learning aided medical literature analysis
Authors: Victor Andrei, Ognjen Arandjelovic
Date: 2016-05-16 00:00:00
Tasks: BIG-bench Machine Learning

The rapidly expanding corpus of medical research literature presents major
challenges in the understanding of previous work, the extraction of maximum
information from collected data, and the identification of promising research
directions. We present a case for the use of advanced machine learning
techniques as an aide in this task and introduce a novel methodology that is
shown to be capable of extracting meaningful information from large
longitudinal corpora, and of tracking complex temporal changes within it.