pwc-archive/papers-with-abstracts huggingface dataset:
A Nonparametric Latent Factor Model For Location-Aware Video Recommendations
Authors: Ehtsham Elahi
Date: 2016-12-05 00:00:00

We are interested in learning customers' video preferences from their
historic viewing patterns and geographical location. We consider a Bayesian
latent factor modeling approach for this task. In order to tune the complexity
of the model to best represent the data, we make use of Bayesian nonparameteric
techniques. We describe an inference technique that can scale to large
real-world data sets. Finally we show results obtained by applying the model to
a large internal Netflix data set, that illustrates that the model was able to
capture interesting relationships between viewing patterns and geographical
location.
Structured Inference Networks for Nonlinear State Space Models
Authors: Rahul G. Krishnan, Uri Shalit, David Sontag
Date: 2016-09-30 00:00:00
Tasks: Multivariate Time Series Forecasting, State Space Models

Gaussian state space models have been used for decades as generative models
of sequential data. They admit an intuitive probabilistic interpretation, have
a simple functional form, and enjoy widespread adoption. We introduce a unified
algorithm to efficiently learn a broad class of linear and non-linear state
space models, including variants where the emission and transition
distributions are modeled by deep neural networks. Our learning algorithm
simultaneously learns a compiled inference network and the generative model,
leveraging a structured variational approximation parameterized by recurrent
neural networks to mimic the posterior distribution. We apply the learning
algorithm to both synthetic and real-world datasets, demonstrating its
scalability and versatility. We find that using the structured approximation to
the posterior results in models with significantly higher held-out likelihood.
Authoring image decompositions with generative models
Authors: Jason Rock, Theerasit Issaranon, Aditya Deshpande, David Forsyth
Date: 2016-12-05 00:00:00

We show how to extend traditional intrinsic image decompositions to
incorporate further layers above albedo and shading. It is hard to obtain data
to learn a multi-layer decomposition. Instead, we can learn to decompose an
image into layers that are "like this" by authoring generative models for each
layer using proxy examples that capture the Platonic ideal (Mondrian images for
albedo; rendered 3D primitives for shading; material swatches for shading
detail). Our method then generates image layers, one from each model, that
explain the image. Our approach rests on innovation in generative models for
images. We introduce a Convolutional Variational Auto Encoder (conv-VAE), a
novel VAE architecture that can reconstruct high fidelity images. The approach
is general, and does not require that layers admit a physical interpretation.
Support vector regression model for BigData systems
Authors: Alessandro Maria Rizzi
Date: 2016-12-05 00:00:00
Tasks: Cloud Computing, model, regression

Nowadays Big Data are becoming more and more important. Many sectors of our
economy are now guided by data-driven decision processes. Big Data and business
intelligence applications are facilitated by the MapReduce programming model
while, at infrastructural layer, cloud computing provides flexible and cost
effective solutions for allocating on demand large clusters. In such systems,
capacity allocation, which is the ability to optimally size minimal resources
for achieve a certain level of performance, is a key challenge to enhance
performance for MapReduce jobs and minimize cloud resource costs. In order to
do so, one of the biggest challenge is to build an accurate performance model
to estimate job execution time of MapReduce systems. Previous works applied
simulation based models for modeling such systems. Although this approach can
accurately describe the behavior of Big Data clusters, it is too
computationally expensive and does not scale to large system. We try to
overcome these issues by applying machine learning techniques. More precisely
we focus on Support Vector Regression (SVR) which is intrinsically more robust
w.r.t other techniques, like, e.g., neural networks, and less sensitive to
outliers in the training set. To better investigate these benefits, we compare
SVR to linear regression.
DROW: Real-Time Deep Learning based Wheelchair Detection in 2D Range Data
Authors: Lucas Beyer, Alexander Hermans, Bastian Leibe
Date: 2016-03-08 00:00:00

We introduce the DROW detector, a deep learning based detector for 2D range
data. Laser scanners are lighting invariant, provide accurate range data, and
typically cover a large field of view, making them interesting sensors for
robotics applications. So far, research on detection in laser range data has
been dominated by hand-crafted features and boosted classifiers, potentially
losing performance due to suboptimal design choices. We propose a Convolutional
Neural Network (CNN) based detector for this task. We show how to effectively
apply CNNs for detection in 2D range data, and propose a depth preprocessing
step and voting scheme that significantly improve CNN performance. We
demonstrate our approach on wheelchairs and walkers, obtaining state of the art
detection results. Apart from the training data, none of our design choices
limits the detector to these two classes, though. We provide a ROS node for our
detector and release our dataset containing 464k laser scans, out of which 24k
were annotated.
N-gram Opcode Analysis for Android Malware Detection
Authors: BooJoong Kang, Suleiman Y. Yerima, Sakir Sezer, Kieran McLaughlin
Date: 2016-12-05 00:00:00
Tasks: Android Malware Detection, feature selection, Malware Detection

Android malware has been on the rise in recent years due to the increasing
popularity of Android and the proliferation of third party application markets.
Emerging Android malware families are increasingly adopting sophisticated
detection avoidance techniques and this calls for more effective approaches for
Android malware detection. Hence, in this paper we present and evaluate an
n-gram opcode features based approach that utilizes machine learning to
identify and categorize Android malware. This approach enables automated
feature discovery without relying on prior expert or domain knowledge for
pre-determined features. Furthermore, by using a data segmentation technique
for feature selection, our analysis is able to scale up to 10-gram opcodes. Our
experiments on a dataset of 2520 samples showed an f-measure of 98% using the
n-gram opcode based approach. We also provide empirical findings that
illustrate factors that have probable impact on the overall n-gram opcodes
performance trends.
Proportional Rankings
Authors: Piotr Skowron, Martin Lackner, Markus Brill, Dominik Peters, Edith Elkind
Date: 2016-12-05 00:00:00
Tasks: Recommendation Systems

In this paper we extend the principle of proportional representation to
rankings. We consider the setting where alternatives need to be ranked based on
approval preferences. In this setting, proportional representation requires
that cohesive groups of voters are represented proportionally in each initial
segment of the ranking. Proportional rankings are desirable in situations where
initial segments of different lengths may be relevant, e.g., hiring decisions
(if it is unclear how many positions are to be filled), the presentation of
competing proposals on a liquid democracy platform (if it is unclear how many
proposals participants are taking into consideration), or recommender systems
(if a ranking has to accommodate different user types). We study the
proportional representation provided by several ranking methods and prove
theoretical guarantees. Furthermore, we experimentally evaluate these methods
and present preliminary evidence as to which methods are most suitable for
producing proportional rankings.
Zeroth-order Asynchronous Doubly Stochastic Algorithm with Variance Reduction
Authors: Bin Gu, Zhouyuan Huo, Heng Huang
Date: 2016-12-05 00:00:00

Zeroth-order (derivative-free) optimization attracts a lot of attention in
machine learning, because explicit gradient calculations may be computationally
expensive or infeasible. To handle large scale problems both in volume and
dimension, recently asynchronous doubly stochastic zeroth-order algorithms were
proposed. The convergence rate of existing asynchronous doubly stochastic
zeroth order algorithms is $O(\frac{1}{\sqrt{T}})$ (also for the sequential
stochastic zeroth-order optimization algorithms). In this paper, we focus on
the finite sums of smooth but not necessarily convex functions, and propose an
asynchronous doubly stochastic zeroth-order optimization algorithm using the
accelerated technology of variance reduction (AsyDSZOVR). Rigorous theoretical
analysis show that the convergence rate can be improved from
$O(\frac{1}{\sqrt{T}})$ the best result of existing algorithms to
$O(\frac{1}{T})$. Also our theoretical results is an improvement to the ones of
the sequential stochastic zeroth-order optimization algorithms.
Mapping the Dialog Act Annotations of the LEGO Corpus into the Communicative Functions of ISO 24617-2
Authors: Eugénio Ribeiro, Ricardo Ribeiro, David Martins de Matos
Date: 2016-12-05 00:00:00

In this paper we present strategies for mapping the dialog act annotations of
the LEGO corpus into the communicative functions of the ISO 24617-2 standard.
Using these strategies, we obtained an additional 347 dialogs annotated
according to the standard. This is particularly important given the reduced
amount of existing data in those conditions due to the recency of the standard.
Furthermore, these are dialogs from a widely explored corpus for dialog related
tasks. However, its dialog annotations have been neglected due to their high
domain-dependency, which renders them unuseful outside the context of the
corpus. Thus, through our mapping process, we both obtain more data annotated
according to a recent standard and provide useful dialog act annotations for a
widely explored corpus in the context of dialog research.
A New Type-II Fuzzy Logic Based Controller for Non-linear Dynamical Systems with Application to a 3-PSP Parallel Robot
Authors: Hamid Reza Hassanzadeh
Date: 2016-12-05 00:00:00
Tasks: Vocal Bursts Type Prediction

The concept of uncertainty is posed in almost any complex system including
parallel robots as an outstanding instance of dynamical robotics systems. As
suggested by the name, uncertainty, is some missing information that is beyond
the knowledge of human thus we may tend to handle it properly to minimize the
side-effects through the control process.
  Type-II fuzzy logic has shown its superiority over traditional fuzzy logic
when dealing with uncertainty. Type-II fuzzy logic controllers are however
newer and more promising approaches that have been recently applied to various
fields due to their significant contribution especially when noise (as an
important instance of uncertainty) emerges. During the design of Type-I fuzzy
logic systems, we presume that we are almost certain about the fuzzy membership
functions which is not true in many cases. Thus T2FLS as a more realistic
approach dealing with practical applications might have a lot to offer. Type-II
fuzzy logic takes into account a higher level of uncertainty, in other words,
the membership grade for a type-II fuzzy variable is no longer a crisp number
but rather is itself a type-I linguistic term. In this thesis the effects of
uncertainty in dynamic control of a parallel robot is considered. More
specifically, it is intended to incorporate the Type-II Fuzzy Logic paradigm
into a model based controller, the so-called computed torque control method,
and apply the result to a 3 degrees of freedom parallel manipulator.
  ...
Implicit Modeling -- A Generalization of Discriminative and Generative Approaches
Authors: Dmitrij Schlesinger, Carsten Rother
Date: 2016-12-05 00:00:00
Tasks: General Classification, Image Segmentation, Semantic Segmentation

We propose a new modeling approach that is a generalization of generative and
discriminative models. The core idea is to use an implicit parameterization of
a joint probability distribution by specifying only the conditional
distributions. The proposed scheme combines the advantages of both worlds -- it
can use powerful complex discriminative models as its parts, having at the same
time better generalization capabilities. We thoroughly evaluate the proposed
method for a simple classification task with artificial data and illustrate its
advantages for real-word scenarios on a semantic image segmentation problem.
Diagnostic Prediction Using Discomfort Drawings
Authors: Cheng Zhang, Hedvig Kjellstrom, Bo C. Bertilson
Date: 2016-12-05 00:00:00
Tasks: BIG-bench Machine Learning, Diagnostic, Prediction

In this paper, we explore the possibility to apply machine learning to make
diagnostic predictions using discomfort drawings. A discomfort drawing is an
intuitive way for patients to express discomfort and pain related symptoms.
These drawings have proven to be an effective method to collect patient data
and make diagnostic decisions in real-life practice. A dataset from real-world
patient cases is collected for which medical experts provide diagnostic labels.
Next, we extend a factorized multimodal topic model, Inter-Battery Topic Model
(IBTM), to train a system that can make diagnostic predictions given an unseen
discomfort drawing. Experimental results show reasonable predictions of
diagnostic labels given an unseen discomfort drawing. The positive result
indicates a significant potential of machine learning to be used for parts of
the pain diagnostic process and to be a decision support system for physicians
and other health care personnel.
Highly Efficient Regression for Scalable Person Re-Identification
Authors: Hanxiao Wang, Shaogang Gong, Tao Xiang
Date: 2016-12-05 00:00:00
Tasks: Active Learning, Person Re-Identification, regression

Existing person re-identification models are poor for scaling up to large
data required in real-world applications due to: (1) Complexity: They employ
complex models for optimal performance resulting in high computational cost for
training at a large scale; (2) Inadaptability: Once trained, they are
unsuitable for incremental update to incorporate any new data available. This
work proposes a truly scalable solution to re-id by addressing both problems.
Specifically, a Highly Efficient Regression (HER) model is formulated by
embedding the Fisher's criterion to a ridge regression model for very fast
re-id model learning with scalable memory/storage usage. Importantly, this new
HER model supports faster than real-time incremental model updates therefore
making real-time active learning feasible in re-id with human-in-the-loop.
Extensive experiments show that such a simple and fast model not only
outperforms notably the state-of-the-art re-id methods, but also is more
scalable to large data with additional benefits to active learning for reducing
human labelling effort in re-id deployment.
A genetic algorithm to discover flexible motifs with support
Authors: Joan Serrà, Aleksandar Matic, Josep Luis Arcos, Alexandros Karatzoglou
Date: 2015-11-16 00:00:00
Tasks: Time Series, Time Series Analysis

Finding repeated patterns or motifs in a time series is an important
unsupervised task that has still a number of open issues, starting by the
definition of motif. In this paper, we revise the notion of motif support,
characterizing it as the number of patterns or repetitions that define a motif.
We then propose GENMOTIF, a genetic algorithm to discover motifs with support
which, at the same time, is flexible enough to accommodate other motif
specifications and task characteristics. GENMOTIF is an anytime algorithm that
easily adapts to many situations: searching in a range of segment lengths,
applying uniform scaling, dealing with multiple dimensions, using different
similarity and grouping criteria, etc. GENMOTIF is also parameter-friendly: it
has only two intuitive parameters which, if set within reasonable bounds, do
not substantially affect its performance. We demonstrate the value of our
approach in a number of synthetic and real-world settings, considering traffic
volume measurements, accelerometer signals, and telephone call records.
We used Neural Networks to Detect Clickbaits: You won't believe what happened Next!
Authors: Ankesh Anand, Tanmoy Chakraborty, Noseong Park
Date: 2016-12-05 00:00:00
Tasks: Articles, Clickbait Detection, Feature Engineering

Online content publishers often use catchy headlines for their articles in order to attract users to their websites. These headlines, popularly known as clickbaits, exploit a user's curiosity gap and lure them to click on links that often disappoint them. Existing methods for automatically detecting clickbaits rely on heavy feature engineering and domain knowledge. Here, we introduce a neural network architecture based on Recurrent Neural Networks for detecting clickbaits. Our model relies on distributed word representations learned from a large unannotated corpora, and character embeddings learned via Convolutional Neural Networks. Experimental results on a dataset of news headlines show that our model outperforms existing techniques for clickbait detection with an accuracy of 0.98 with F1-score of 0.98 and ROC-AUC of 0.99.
Stereo image de-fencing using smartphones
Authors: Sankaraganesh Jonna, Sukla Satapathy, Rajiv R. Sahay
Date: 2016-12-05 00:00:00

Conventional approaches to image de-fencing have limited themselves to using
only image data in adjacent frames of the captured video of an approximately
static scene. In this work, we present a method to harness disparity using a
stereo pair of fenced images in order to detect fence pixels. Tourists and
amateur photographers commonly carry smartphones/phablets which can be used to
capture a short video sequence of the fenced scene. We model the formation of
the occluded frames in the captured video. Furthermore, we propose an
optimization framework to estimate the de-fenced image using the total
variation prior to regularize the ill-posed problem.
Ranking Biomarkers Through Mutual Information
Authors: Konstantinos Sechidis, Emily Turner, Paul D. Metcalfe, James Weatherall, Gavin Brown
Date: 2016-12-05 00:00:00

We study information theoretic methods for ranking biomarkers. In clinical
trials there are two, closely related, types of biomarkers: predictive and
prognostic, and disentangling them is a key challenge. Our first step is to
phrase biomarker ranking in terms of optimizing an information theoretic
quantity. This formalization of the problem will enable us to derive rankings
of predictive/prognostic biomarkers, by estimating different, high dimensional,
conditional mutual information terms. To estimate these terms, we suggest
efficient low dimensional approximations, and we derive an empirical Bayes
estimator, which is suitable for small or sparse datasets. Finally, we
introduce a new visualisation tool that captures the prognostic and the
predictive strength of a set of biomarkers. We believe this representation will
prove to be a powerful tool in biomarker discovery.
Message Passing Multi-Agent GANs
Authors: Arnab Ghosh, Viveka Kulharia, Vinay Namboodiri
Date: 2016-12-05 00:00:00
Tasks: Image Generation

Communicating and sharing intelligence among agents is an important facet of
achieving Artificial General Intelligence. As a first step towards this
challenge, we introduce a novel framework for image generation: Message Passing
Multi-Agent Generative Adversarial Networks (MPM GANs). While GANs have
recently been shown to be very effective for image generation and other tasks,
these networks have been limited to mostly single generator-discriminator
networks. We show that we can obtain multi-agent GANs that communicate through
message passing to achieve better image generation. The objectives of the
individual agents in this framework are two fold: a co-operation objective and
a competing objective. The co-operation objective ensures that the message
sharing mechanism guides the other generator to generate better than itself
while the competing objective encourages each generator to generate better than
its counterpart. We analyze and visualize the messages that these GANs share
among themselves in various scenarios. We quantitatively show that the message
sharing formulation serves as a regularizer for the adversarial training.
Qualitatively, we show that the different generators capture different traits
of the underlying data distribution.
Point Pair Feature based Object Detection for Random Bin Picking
Authors: Wim Abbeloos, Toon Goedemé
Date: 2016-12-05 00:00:00
Tasks: 3D Object Detection, object-detection, Object Detection, Pose Estimation

Point pair features are a popular representation for free form 3D object
detection and pose estimation. In this paper, their performance in an
industrial random bin picking context is investigated. A new method to generate
representative synthetic datasets is proposed. This allows to investigate the
influence of a high degree of clutter and the presence of self similar
features, which are typical to our application. We provide an overview of
solutions proposed in literature and discuss their strengths and weaknesses. A
simple heuristic method to drastically reduce the computational complexity is
introduced, which results in improved robustness, speed and accuracy compared
to the naive approach.
Distributed Training of Deep Neural Networks: Theoretical and Practical Limits of Parallel Scalability
Authors: Janis Keuper, Franz-Josef Pfreundt
Date: 2016-09-22 00:00:00

This paper presents a theoretical analysis and practical evaluation of the
main bottlenecks towards a scalable distributed solution for the training of
Deep Neuronal Networks (DNNs). The presented results show, that the current
state of the art approach, using data-parallelized Stochastic Gradient Descent
(SGD), is quickly turning into a vastly communication bound problem. In
addition, we present simple but fixed theoretic constraints, preventing
effective scaling of DNN training beyond only a few dozen nodes. This leads to
poor scalability of DNN training in most practical scenarios.
Panoramic Structure from Motion via Geometric Relationship Detection
Authors: Satoshi Ikehata, Ivaylo Boyadzhiev, Qi Shan, Yasutaka Furukawa
Date: 2016-12-05 00:00:00
Tasks: Relationship Detection

This paper addresses the problem of Structure from Motion (SfM) for indoor
panoramic image streams, extremely challenging even for the state-of-the-art
due to the lack of textures and minimal parallax. The key idea is the fusion of
single-view and multi-view reconstruction techniques via geometric relationship
detection (e.g., detecting 2D lines as coplanar in 3D). Rough geometry suffices
to perform such detection, and our approach utilizes rough surface normal
estimates from an image-to-normal deep network to discover geometric
relationships among lines. The detected relationships provide exact geometric
constraints in our line-based linear SfM formulation. A constrained linear
least squares is used to reconstruct a 3D model and camera motions, followed by
the bundle adjustment. We have validated our algorithm on challenging datasets,
outperforming various state-of-the-art reconstruction techniques.