pwc-archive/papers-with-abstracts huggingface dataset:
Some Advances in Role Discovery in Graphs
Authors: Sean Gilpin, Chia-Tung Kuo, Tina Eliassi-Rad, Ian Davidson
Date: 2016-09-09 00:00:00

Role discovery in graphs is an emerging area that allows analysis of complex
graphs in an intuitive way. In contrast to other graph prob- lems such as
community discovery, which finds groups of highly connected nodes, the role
discovery problem finds groups of nodes that share similar graph topological
structure. However, existing work so far has two severe limitations that
prevent its use in some domains. Firstly, it is completely unsupervised which
is undesirable for a number of reasons. Secondly, most work is limited to a
single relational graph. We address both these lim- itations in an intuitive
and easy to implement alternating least squares framework. Our framework allows
convex constraints to be placed on the role discovery problem which can provide
useful supervision. In par- ticular we explore supervision to enforce i)
sparsity, ii) diversity and iii) alternativeness. We then show how to lift this
work for multi-relational graphs. A natural representation of a
multi-relational graph is an order 3 tensor (rather than a matrix) and that a
Tucker decomposition allows us to find complex interactions between collections
of entities (E-groups) and the roles they play for a combination of relations
(R-groups). Existing Tucker decomposition methods in tensor toolboxes are not
suited for our purpose, so we create our own algorithm that we demonstrate is
pragmatically useful.
Real-Time Visual Tracking: Promoting the Robustness of Correlation Filter Learning
Authors: Yao Sui, Ziming Zhang, Guanghui Wang, Yafei Tang, Li Zhang
Date: 2016-08-29 00:00:00
Tasks: Real-Time Visual Tracking, Visual Tracking

Correlation filtering based tracking model has received lots of attention and
achieved great success in real-time tracking, however, the lost function in
current correlation filtering paradigm could not reliably response to the
appearance changes caused by occlusion and illumination variations. This study
intends to promote the robustness of the correlation filter learning. By
exploiting the anisotropy of the filter response, three sparsity related loss
functions are proposed to alleviate the overfitting issue of previous methods
and improve the overall tracking performance. As a result, three real-time
trackers are implemented. Extensive experiments in various challenging
situations demonstrate that the robustness of the learned correlation filter
has been greatly improved via the designed loss functions. In addition, the
study reveals, from an experimental perspective, how different loss functions
essentially influence the tracking performance. An important conclusion is that
the sensitivity of the peak values of the filter in successive frames is
consistent with the tracking performance. This is a useful reference criterion
in designing a robust correlation filter for visual tracking.
Tracking Completion
Authors: Yao Sui, Guanghui Wang, Yafei Tang, Li Zhang
Date: 2016-08-29 00:00:00
Tasks: Matrix Completion

A fundamental component of modern trackers is an online learned tracking
model, which is typically modeled either globally or locally. The two kinds of
models perform differently in terms of effectiveness and robustness under
different challenging situations. This work exploits the advantages of both
models. A subspace model, from a global perspective, is learned from previously
obtained targets via rank-minimization to address the tracking, and a
pixel-level local observation is leveraged si- multaneously, from a local point
of view, to augment the subspace model. A matrix completion method is employed
to integrate the two models. Unlike previous tracking methods, which locate the
target among all fully observed target candidates, the proposed approach first
estimates an expected target via the matrix completion through partially
observed target candidates, and then, identifies the target according to the
estimation accuracy with respect to the target candidates. Specifically, the
tracking is formulated as a problem of target appearance estimation. Extensive
experiments on various challenging video sequences verify the effectiveness of
the proposed approach and demonstrate that the proposed tracker outperforms
other popular state-of-the-art trackers.
Distributed Processing of Biosignal-Database for Emotion Recognition with Mahout
Authors: Varvara Kollia, Oguz H. Elibol
Date: 2016-09-09 00:00:00
Tasks: BIG-bench Machine Learning, Emotion Recognition

This paper investigates the use of distributed processing on the problem of
emotion recognition from physiological sensors using a popular machine learning
library on distributed mode. Specifically, we run a random forests classifier
on the biosignal-data, which have been pre-processed to form exclusive groups
in an unsupervised fashion, on a Cloudera cluster using Mahout. The use of
distributed processing significantly reduces the time required for the offline
training of the classifier, enabling processing of large physiological datasets
through many iterations.
Multivariate Dependence Beyond Shannon Information
Authors: Ryan G. James, James P. Crutchfield
Date: 2016-09-05 00:00:00

Accurately determining dependency structure is critical to discovering a
system's causal organization. We recently showed that the transfer entropy
fails in a key aspect of this---measuring information flow---due to its
conflation of dyadic and polyadic relationships. We extend this observation to
demonstrate that this is true of all such Shannon information measures when
used to analyze multivariate dependencies. This has broad implications,
particularly when employing information to express the organization and
mechanisms embedded in complex systems, including the burgeoning efforts to
combine complex network theory with information theory. Here, we do not suggest
that any aspect of information theory is wrong. Rather, the vast majority of
its informational measures are simply inadequate for determining the meaningful
dependency structure within joint probability distributions. Therefore, such
information measures are inadequate for discovering intrinsic causal relations.
We close by demonstrating that such distributions exist across an arbitrary set
of variables.
Bottom-up Instance Segmentation using Deep Higher-Order CRFs
Authors: Anurag Arnab, Philip H. S. Torr
Date: 2016-09-08 00:00:00
Tasks: Instance Segmentation, Object, object-detection, Object Detection, Scene Understanding, Segmentation, Semantic Segmentation

Traditional Scene Understanding problems such as Object Detection and
Semantic Segmentation have made breakthroughs in recent years due to the
adoption of deep learning. However, the former task is not able to localise
objects at a pixel level, and the latter task has no notion of different
instances of objects of the same class. We focus on the task of Instance
Segmentation which recognises and localises objects down to a pixel level. Our
model is based on a deep neural network trained for semantic segmentation. This
network incorporates a Conditional Random Field with end-to-end trainable
higher order potentials based on object detector outputs. This allows us to
reason about instances from an initial, category-level semantic segmentation.
Our simple method effectively leverages the great progress recently made in
semantic segmentation and object detection. The accurate instance-level
segmentations that our network produces is reflected by the considerable
improvements obtained over previous work.
A Mathematical Formalization of Hierarchical Temporal Memory's Spatial Pooler
Authors: James Mnatzaganian, Ernest Fokoué, Dhireesha Kudithipudi
Date: 2016-01-22 00:00:00
Tasks: Attribute, Dimensionality Reduction

Hierarchical temporal memory (HTM) is an emerging machine learning algorithm,
with the potential to provide a means to perform predictions on spatiotemporal
data. The algorithm, inspired by the neocortex, currently does not have a
comprehensive mathematical framework. This work brings together all aspects of
the spatial pooler (SP), a critical learning component in HTM, under a single
unifying framework. The primary learning mechanism is explored, where a maximum
likelihood estimator for determining the degree of permanence update is
proposed. The boosting mechanisms are studied and found to be only relevant
during the initial few iterations of the network. Observations are made
relating HTM to well-known algorithms such as competitive learning and
attribute bagging. Methods are provided for using the SP for classification as
well as dimensionality reduction. Empirical evidence verifies that given the
proper parameterizations, the SP may be used for feature learning.
Latest Datasets and Technologies Presented in the Workshop on Grasping and Manipulation Datasets
Authors: Matteo Bianchi, Jeannette Bohg, Yu Sun
Date: 2016-09-08 00:00:00
Tasks: Robotic Grasping

This paper reports the activities and outcomes in the Workshop on Grasping
and Manipulation Datasets that was organized under the International Conference
on Robotics and Automation (ICRA) 2016. The half day workshop was packed with
nine invited talks, 12 interactive presentations, and one panel discussion with
ten panelists. This paper summarizes all the talks and presentations and recaps
what has been discussed in the panels session. This summary servers as a review
of recent developments in data collection in grasping and manipulation. Many of
the presentations describe ongoing efforts or explorations that could be
achieved and fully available in a year or two. The panel discussion not only
commented on the current approaches, but also indicates new directions and
focuses. The workshop clearly displayed the importance of quality datasets in
robotics and robotic grasping and manipulation field. Hopefully the workshop
could motivate larger efforts to create big datasets that are comparable with
big datasets in other communities such as computer vision.
True Online Temporal-Difference Learning
Authors: Harm van Seijen, A. Rupam Mahmood, Patrick M. Pilarski, Marlos C. Machado, Richard S. Sutton
Date: 2015-12-13 00:00:00
Tasks: Atari Games, Reinforcement Learning

The temporal-difference methods TD($\lambda$) and Sarsa($\lambda$) form a
core part of modern reinforcement learning. Their appeal comes from their good
performance, low computational cost, and their simple interpretation, given by
their forward view. Recently, new versions of these methods were introduced,
called true online TD($\lambda$) and true online Sarsa($\lambda$), respectively
(van Seijen & Sutton, 2014). These new versions maintain an exact equivalence
with the forward view at all times, whereas the traditional versions only
approximate it for small step-sizes. We hypothesize that these true online
methods not only have better theoretical properties, but also dominate the
regular methods empirically. In this article, we put this hypothesis to the
test by performing an extensive empirical comparison. Specifically, we compare
the performance of true online TD($\lambda$)/Sarsa($\lambda$) with regular
TD($\lambda$)/Sarsa($\lambda$) on random MRPs, a real-world myoelectric
prosthetic arm, and a domain from the Arcade Learning Environment. We use
linear function approximation with tabular, binary, and non-binary features.
Our results suggest that the true online methods indeed dominate the regular
methods. Across all domains/representations the learning speed of the true
online methods are often better, but never worse than that of the regular
methods. An additional advantage is that no choice between traces has to be
made for the true online methods. Besides the empirical results, we provide an
in-depth analysis of the theory behind true online temporal-difference
learning. In addition, we show that new true online temporal-difference methods
can be derived by making changes to the online forward view and then rewriting
the update equations.
DiSMEC - Distributed Sparse Machines for Extreme Multi-label Classification
Authors: Rohit Babbar, Bernhard Shoelkopf
Date: 2016-09-08 00:00:00
Tasks: Classification, Extreme Multi-Label Classification, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Multi-Label Learning

Extreme multi-label classification refers to supervised multi-label learning
involving hundreds of thousands or even millions of labels. Datasets in extreme
classification exhibit fit to power-law distribution, i.e. a large fraction of
labels have very few positive instances in the data distribution. Most
state-of-the-art approaches for extreme multi-label classification attempt to
capture correlation among labels by embedding the label matrix to a
low-dimensional linear sub-space. However, in the presence of power-law
distributed extremely large and diverse label spaces, structural assumptions
such as low rank can be easily violated.
  In this work, we present DiSMEC, which is a large-scale distributed framework
for learning one-versus-rest linear classifiers coupled with explicit capacity
control to control model size. Unlike most state-of-the-art methods, DiSMEC
does not make any low rank assumptions on the label matrix. Using double layer
of parallelization, DiSMEC can learn classifiers for datasets consisting
hundreds of thousands labels within few hours. The explicit capacity control
mechanism filters out spurious parameters which keep the model compact in size,
without losing prediction accuracy. We conduct extensive empirical evaluation
on publicly available real-world datasets consisting upto 670,000 labels. We
compare DiSMEC with recent state-of-the-art approaches, including - SLEEC which
is a leading approach for learning sparse local embeddings, and FastXML which
is a tree-based approach optimizing ranking based loss function. On some of the
datasets, DiSMEC can significantly boost prediction accuracies - 10% better
compared to SLECC and 15% better compared to FastXML, in absolute terms.
Functorial Hierarchical Clustering with Overlaps
Authors: Jared Culbertson, Dan P. Guralnik, Peter F. Stiller
Date: 2016-09-08 00:00:00
Tasks: Clustering

This work draws inspiration from three important sources of research on
dissimilarity-based clustering and intertwines those three threads into a
consistent principled functorial theory of clustering. Those three are the
overlapping clustering of Jardine and Sibson, the functorial approach of
Carlsson and M\'{e}moli to partition-based clustering, and the Isbell/Dress
school's study of injective envelopes. Carlsson and M\'{e}moli introduce the
idea of viewing clustering methods as functors from a category of metric spaces
to a category of clusters, with functoriality subsuming many desirable
properties. Our first series of results extends their theory of functorial
clustering schemes to methods that allow overlapping clusters in the spirit of
Jardine and Sibson. This obviates some of the unpleasant effects of chaining
that occur, for example with single-linkage clustering. We prove an equivalence
between these general overlapping clustering functors and projections of weight
spaces to what we term clustering domains, by focusing on the order structure
determined by the morphisms. As a specific application of this machinery, we
are able to prove that there are no functorial projections to cut metrics, or
even to tree metrics. Finally, although we focus less on the construction of
clustering methods (clustering domains) derived from injective envelopes, we
lay out some preliminary results, that hopefully will give a feel for how the
third leg of the stool comes into play.
Stacked Approximated Regression Machine: A Simple Deep Learning Approach
Authors: Zhangyang Wang, Shiyu Chang, Qing Ling, Shuai Huang, Xia Hu, Honghui Shi, Thomas S. Huang
Date: 2016-08-14 00:00:00
Tasks: Deep Learning, regression

With the agreement of my coauthors, I Zhangyang Wang would like to withdraw
the manuscript "Stacked Approximated Regression Machine: A Simple Deep Learning
Approach". Some experimental procedures were not included in the manuscript,
which makes a part of important claims not meaningful. In the relevant
research, I was solely responsible for carrying out the experiments; the other
coauthors joined in the discussions leading to the main algorithm.
  Please see the updated text for more details.
Title Generation for User Generated Videos
Authors: Kuo-Hao Zeng, Tseng-Hung Chen, Juan Carlos Niebles, Min Sun
Date: 2016-08-25 00:00:00
Tasks: Sentence, Video Captioning

A great video title describes the most salient event compactly and captures
the viewer's attention. In contrast, video captioning tends to generate
sentences that describe the video as a whole. Although generating a video title
automatically is a very useful task, it is much less addressed than video
captioning. We address video title generation for the first time by proposing
two methods that extend state-of-the-art video captioners to this new task.
First, we make video captioners highlight sensitive by priming them with a
highlight detector. Our framework allows for jointly training a model for title
generation and video highlight localization. Second, we induce high sentence
diversity in video captioners, so that the generated titles are also diverse
and catchy. This means that a large number of sentences might be required to
learn the sentence structure of titles. Hence, we propose a novel sentence
augmentation method to train a captioner with additional sentence-only examples
that come without corresponding videos. We collected a large-scale Video Titles
in the Wild (VTW) dataset of 18100 automatically crawled user-generated videos
and titles. On VTW, our methods consistently improve title prediction accuracy,
and achieve the best performance in both automatic and human evaluation.
Finally, our sentence augmentation method also outperforms the baselines on the
M-VAD dataset.
Reduced Memory Region Based Deep Convolutional Neural Network Detection
Authors: Denis Tome', Luca Bondi, Emanuele Plebani, Luca Baroffio, Danilo Pau, Stefano Tubaro
Date: 2016-09-08 00:00:00
Tasks: Pedestrian Detection

Accurate pedestrian detection has a primary role in automotive safety: for
example, by issuing warnings to the driver or acting actively on car's brakes,
it helps decreasing the probability of injuries and human fatalities. In order
to achieve very high accuracy, recent pedestrian detectors have been based on
Convolutional Neural Networks (CNN). Unfortunately, such approaches require
vast amounts of computational power and memory, preventing efficient
implementations on embedded systems. This work proposes a CNN-based detector,
adapting a general-purpose convolutional network to the task at hand. By
thoroughly analyzing and optimizing each step of the detection pipeline, we
develop an architecture that outperforms methods based on traditional image
features and achieves an accuracy close to the state-of-the-art while having
low computational complexity. Furthermore, the model is compressed in order to
fit the tight constrains of low power devices with a limited amount of embedded
memory available. This paper makes two main contributions: (1) it proves that a
region based deep neural network can be finely tuned to achieve adequate
accuracy for pedestrian detection (2) it achieves a very low memory usage
without reducing detection accuracy on the Caltech Pedestrian dataset.
Fashion DNA: Merging Content and Sales Data for Recommendation and Article Mapping
Authors: Christian Bracher, Sebastian Heinz, Roland Vollgraf
Date: 2016-09-08 00:00:00
Tasks: TAG

We present a method to determine Fashion DNA, coordinate vectors locating
fashion items in an abstract space. Our approach is based on a deep neural
network architecture that ingests curated article information such as tags and
images, and is trained to predict sales for a large set of frequent customers.
In the process, a dual space of customer style preferences naturally arises.
Interpretation of the metric of these spaces is straightforward: The product of
Fashion DNA and customer style vectors yields the forecast purchase likelihood
for the customer-item pair, while the angle between Fashion DNA vectors is a
measure of item similarity. Importantly, our models are able to generate
unbiased purchase probabilities for fashion items based solely on article
information, even in absence of sales data, thus circumventing the "cold-start
problem" of collaborative recommendation approaches. Likewise, it generalizes
easily and reliably to customers outside the training set. We experiment with
Fashion DNA models based on visual and/or tag item data, evaluate their
recommendation power, and discuss the resulting article similarities.
Combining Monte-Carlo and Hyper-heuristic methods for the Multi-mode Resource-constrained Multi-project Scheduling Problem
Authors: Shahriar Asta, Daniel Karapetyan, Ahmed Kheiri, Ender Özcan, Andrew J. Parkes
Date: 2015-11-13 00:00:00
Tasks: Scheduling

Multi-mode resource and precedence-constrained project scheduling is a
well-known challenging real-world optimisation problem. An important variant of
the problem requires scheduling of activities for multiple projects considering
availability of local and global resources while respecting a range of
constraints. A critical aspect of the benchmarks addressed in this paper is
that the primary objective is to minimise the sum of the project completion
times, with the usual makespan minimisation as a secondary objective. We
observe that this leads to an expected different overall structure of good
solutions and discuss the effects this has on the algorithm design. This paper
presents a carefully designed hybrid of Monte-Carlo tree search, novel
neighbourhood moves, memetic algorithms, and hyper-heuristic methods. The
implementation is also engineered to increase the speed with which iterations
are performed, and to exploit the computing power of multicore machines.
Empirical evaluation shows that the resulting information-sharing
multi-component algorithm significantly outperforms other solvers on a set of
"hidden" instances, i.e. instances not available at the algorithm design phase.
Quantifying Radiographic Knee Osteoarthritis Severity using Deep Convolutional Neural Networks
Authors: Joseph Antony, Kevin McGuinness, Noel E O Connor, Kieran Moran
Date: 2016-09-08 00:00:00
Tasks: General Classification

This paper proposes a new approach to automatically quantify the severity of
knee osteoarthritis (OA) from radiographs using deep convolutional neural
networks (CNN). Clinically, knee OA severity is assessed using Kellgren \&
Lawrence (KL) grades, a five point scale. Previous work on automatically
predicting KL grades from radiograph images were based on training shallow
classifiers using a variety of hand engineered features. We demonstrate that
classification accuracy can be significantly improved using deep convolutional
neural network models pre-trained on ImageNet and fine-tuned on knee OA images.
Furthermore, we argue that it is more appropriate to assess the accuracy of
automatic knee OA severity predictions using a continuous distance-based
evaluation metric like mean squared error than it is to use classification
accuracy. This leads to the formulation of the prediction of KL grades as a
regression problem and further improves accuracy. Results on a dataset of X-ray
images and KL grades from the Osteoarthritis Initiative (OAI) show a sizable
improvement over the current state-of-the-art.