pwc-archive/papers-with-abstracts huggingface dataset:
An Efficient Method of Partitioning High Volumes of Multidimensional Data for Parallel Clustering Algorithms
Authors: Saraswati Mishra, Avnish Chandra Suman
Date: 2016-09-20 00:00:00
Tasks: Clustering

An optimal data partitioning in parallel & distributed implementation of
clustering algorithms is a necessary computation as it ensures independent task
completion, fair distribution, less number of affected points and better &
faster merging. Though partitioning using Kd Tree is being conventionally used
in academia, it suffers from performance drenches and bias (non equal
distribution) as dimensionality of data increases and hence is not suitable for
practical use in industry where dimensionality can be of order of 100s to
1000s. To address these issues we propose two new partitioning techniques using
existing mathematical models & study their feasibility, performance (bias and
partitioning speed) & possible variants in choosing initial seeds. First method
uses an n dimensional hashed grid based approach which is based on mapping the
points in space to a set of cubes which hashes the points. Second method uses a
tree of voronoi planes where each plane corresponds to a partition. We found
that grid based approach was computationally impractical, while using a tree of
voronoi planes (using scalable K-Means++ initial seeds) drastically
outperformed the Kd-tree tree method as dimensionality increased.
Energy-Efficient Object Detection using Semantic Decomposition
Authors: Priyadarshini Panda, Swagath Venkataramani, Abhronil Sengupta, Anand Raghunathan, Kaushik Roy
Date: 2015-09-29 00:00:00
Tasks: General Classification, Object, object-detection, Object Detection

Machine-learning algorithms offer immense possibilities in the development of
several cognitive applications. In fact, large scale machine-learning
classifiers now represent the state-of-the-art in a wide range of object
detection/classification problems. However, the network complexities of
large-scale classifiers present them as one of the most challenging and energy
intensive workloads across the computing spectrum. In this paper, we present a
new approach to optimize energy efficiency of object detection tasks using
semantic decomposition to build a hierarchical classification framework. We
observe that certain semantic information like color/texture are common across
various images in real-world datasets for object detection applications. We
exploit these common semantic features to distinguish the objects of interest
from the remaining inputs (non-objects of interest) in a dataset at a lower
computational effort. We propose a 2-stage hierarchical classification
framework, with increasing levels of complexity, wherein the first stage is
trained to recognize the broad representative semantic features relevant to the
object of interest. The first stage rejects the input instances that do not
have the representative features and passes only the relevant instances to the
second stage. Our methodology thus allows us to reject certain information at
lower complexity and utilize the full computational effort of a network only on
a smaller fraction of inputs to perform detection. We use color and texture as
distinctive traits to carry out several experiments for object detection. Our
experiments on the Caltech101/CIFAR10 dataset show that the proposed method
yields 1.93x/1.46x improvement in average energy, respectively, over the
traditional single classifier model.
SimVerb-3500: A Large-Scale Evaluation Set of Verb Similarity
Authors: Daniela Gerz, Ivan Vulić, Felix Hill, Roi Reichart, Anna Korhonen
Date: 2016-08-02 00:00:00
Proceeding: EMNLP 2016 11
Tasks: Diversity, Representation Learning

Verbs play a critical role in the meaning of sentences, but these ubiquitous
words have received little attention in recent distributional semantics
research. We introduce SimVerb-3500, an evaluation resource that provides human
ratings for the similarity of 3,500 verb pairs. SimVerb-3500 covers all normed
verb types from the USF free-association database, providing at least three
examples for every VerbNet class. This broad coverage facilitates detailed
analyses of how syntactic and semantic phenomena together influence human
understanding of verb meaning. Further, with significantly larger development
and test sets than existing benchmarks, SimVerb-3500 enables more robust
evaluation of representation learning architectures and promotes the
development of methods tailored to verbs. We hope that SimVerb-3500 will enable
a richer understanding of the diversity and complexity of verb semantics and
guide the development of systems that can effectively represent and interpret
this meaning.
Exploiting Vagueness for Multi-Agent Consensus
Authors: Michael Crosscombe, Jonathan Lawry
Date: 2016-07-19 00:00:00

A framework for consensus modelling is introduced using Kleene's three valued
logic as a means to express vagueness in agents' beliefs. Explicitly borderline
cases are inherent to propositions involving vague concepts where sentences of
a propositional language may be absolutely true, absolutely false or
borderline. By exploiting these intermediate truth values, we can allow agents
to adopt a more vague interpretation of underlying concepts in order to weaken
their beliefs and reduce the levels of inconsistency, so as to achieve
consensus. We consider a consensus combination operation which results in
agents adopting the borderline truth value as a shared viewpoint if they are in
direct conflict. Simulation experiments are presented which show that applying
this operator to agents chosen at random (subject to a consistency threshold)
from a population, with initially diverse opinions, results in convergence to a
smaller set of more precise shared beliefs. Furthermore, if the choice of
agents for combination is dependent on the payoff of their beliefs, this acting
as a proxy for performance or usefulness, then the system converges to beliefs
which, on average, have higher payoff.
Hands-Free Segmentation of Medical Volumes via Binary Inputs
Authors: Florian Dubost, Loic Peter, Christian Rupprecht, Benjamin Gutierrez-Becker, Nassir Navab
Date: 2016-09-20 00:00:00
Tasks: Efficient Exploration

We propose a novel hands-free method to interactively segment 3D medical
volumes. In our scenario, a human user progressively segments an organ by
answering a series of questions of the form "Is this voxel inside the object to
segment?". At each iteration, the chosen question is defined as the one halving
a set of candidate segmentations given the answered questions. For a quick and
efficient exploration, these segmentations are sampled according to the
Metropolis-Hastings algorithm. Our sampling technique relies on a combination
of relaxed shape prior, learnt probability map and consistency with previous
answers. We demonstrate the potential of our strategy on a prostate
segmentation MRI dataset. Through the study of failure cases with synthetic
examples, we demonstrate the adaptation potential of our method. We also show
that our method outperforms two intuitive baselines: one based on random
questions, the other one being the thresholded probability map.
Transfer Learning for Material Classification using Convolutional Networks
Authors: Patrick Wieschollek, Hendrik P. A. Lensch
Date: 2016-09-20 00:00:00
Tasks: Classification, Descriptive, General Classification, Material Classification, Material Recognition, Object Recognition, Transfer Learning

Material classification in natural settings is a challenge due to complex
interplay of geometry, reflectance properties, and illumination. Previous work
on material classification relies strongly on hand-engineered features of
visual samples. In this work we use a Convolutional Neural Network (convnet)
that learns descriptive features for the specific task of material recognition.
Specifically, transfer learning from the task of object recognition is
exploited to more effectively train good features for material classification.
The approach of transfer learning using convnets yields significantly higher
recognition rates when compared to previous state-of-the-art approaches. We
then analyze the relative contribution of reflectance and shading information
by a decomposition of the image into its intrinsic components. The use of
convnets for material classification was hindered by the strong demand for
sufficient and diverse training data, even with transfer learning approaches.
Therefore, we present a new data set containing approximately 10k images
divided into 10 material categories.
Identifiable Phenotyping using Constrained Non-Negative Matrix Factorization
Authors: Shalmali Joshi, Suriya Gunasekar, David Sontag, Joydeep Ghosh
Date: 2016-08-02 00:00:00

This work proposes a new algorithm for automated and simultaneous phenotyping
of multiple co-occurring medical conditions, also referred as comorbidities,
using clinical notes from the electronic health records (EHRs). A basic latent
factor estimation technique of non-negative matrix factorization (NMF) is
augmented with domain specific constraints to obtain sparse latent factors that
are anchored to a fixed set of chronic conditions. The proposed anchoring
mechanism ensures a one-to-one identifiable and interpretable mapping between
the latent factors and the target comorbidities. Qualitative assessment of the
empirical results by clinical experts suggests that the proposed model learns
clinically interpretable phenotypes while being predictive of 30 day mortality.
The proposed method can be readily adapted to any non-negative EHR data across
various healthcare institutions.
Discriminative Scale Space Tracking
Authors: Martin Danelljan, Gustav Häger, Fahad Shahbaz Khan, Michael Felsberg
Date: 2016-09-20 00:00:00
Tasks: Object Tracking, Visual Object Tracking

Accurate scale estimation of a target is a challenging research problem in
visual object tracking. Most state-of-the-art methods employ an exhaustive
scale search to estimate the target size. The exhaustive search strategy is
computationally expensive and struggles when encountered with large scale
variations. This paper investigates the problem of accurate and robust scale
estimation in a tracking-by-detection framework. We propose a novel scale
adaptive tracking approach by learning separate discriminative correlation
filters for translation and scale estimation. The explicit scale filter is
learned online using the target appearance sampled at a set of different
scales. Contrary to standard approaches, our method directly learns the
appearance change induced by variations in the target scale. Additionally, we
investigate strategies to reduce the computational cost of our approach.
  Extensive experiments are performed on the OTB and the VOT2014 datasets.
Compared to the standard exhaustive scale search, our approach achieves a gain
of 2.5% in average overlap precision on the OTB dataset. Additionally, our
method is computationally efficient, operating at a 50% higher frame rate
compared to the exhaustive scale search. Our method obtains the top rank in
performance by outperforming 19 state-of-the-art trackers on OTB and 37
state-of-the-art trackers on VOT2014.
A framework for mining process models from emails logs
Authors: Diana Jlailaty, Daniela Grigori, Khalid Belhajjame
Date: 2016-09-20 00:00:00
Tasks: Activity Recognition

Due to its wide use in personal, but most importantly, professional contexts,
email represents a valuable source of information that can be harvested for
understanding, reengineering and repurposing undocumented business processes of
companies and institutions. Towards this aim, a few researchers investigated
the problem of extracting process oriented information from email logs in order
to take benefit of the many available process mining techniques and tools. In
this paper we go further in this direction, by proposing a new method for
mining process models from email logs that leverage unsupervised machine
learning techniques with little human involvement. Moreover, our method allows
to semi-automatically label emails with activity names, that can be used for
activity recognition in new incoming emails. A use case demonstrates the
usefulness of the proposed solution using a modest in size, yet real-world,
dataset containing emails that belong to two different process models.
Predictive modelling of football injuries
Authors: Stylianos Kampakis
Date: 2016-09-20 00:00:00
Tasks: BIG-bench Machine Learning, feature selection, Game of Football, Injury Prediction

The goal of this thesis is to investigate the potential of predictive
modelling for football injuries. This work was conducted in close collaboration
with Tottenham Hotspurs FC (THFC), the PGA European tour and the participation
of Wolverhampton Wanderers (WW).
  Three investigations were conducted:
  1. Predicting the recovery time of football injuries using the UEFA injury
recordings: The UEFA recordings is a common standard for recording injuries in
professional football. For this investigation, three datasets of UEFA injury
recordings were available. Different machine learning algorithms were used in
order to build a predictive model. The performance of the machine learning
models is then improved by using feature selection conducted through
correlation-based subset feature selection and random forests.
  2. Predicting injuries in professional football using exposure records: The
relationship between exposure (in training hours and match hours) in
professional football athletes and injury incidence was studied. A common
problem in football is understanding how the training schedule of an athlete
can affect the chance of him getting injured. The task was to predict the
number of days a player can train before he gets injured.
  3. Predicting intrinsic injury incidence using in-training GPS measurements:
A significant percentage of football injuries can be attributed to overtraining
and fatigue. GPS data collected during training sessions might provide
indicators of fatigue, or might be used to detect very intense training
sessions which can lead to overtraining. This research used GPS data gathered
during training sessions of the first team of THFC, in order to predict whether
an injury would take place during a week.
Sparse Boltzmann Machines with Structure Learning as Applied to Text Analysis
Authors: Zhourong Chen, Nevin L. Zhang, Dit-yan Yeung, Peixian Chen
Date: 2016-09-17 00:00:00

We are interested in exploring the possibility and benefits of structure
learning for deep models. As the first step, this paper investigates the matter
for Restricted Boltzmann Machines (RBMs). We conduct the study with Replicated
Softmax, a variant of RBMs for unsupervised text analysis. We present a method
for learning what we call Sparse Boltzmann Machines, where each hidden unit is
connected to a subset of the visible units instead of all of them. Empirical
results show that the method yields models with significantly improved model
fit and interpretability as compared with RBMs where each hidden unit is
connected to all visible units.
FastBDT: A speed-optimized and cache-friendly implementation of stochastic gradient-boosted decision trees for multivariate classification
Authors: Thomas Keck
Date: 2016-09-20 00:00:00
Tasks: Classification, General Classification

Stochastic gradient-boosted decision trees are widely employed for
multivariate classification and regression tasks. This paper presents a
speed-optimized and cache-friendly implementation for multivariate
classification called FastBDT. FastBDT is one order of magnitude faster during
the fitting-phase and application-phase, in comparison with popular
implementations in software frameworks like TMVA, scikit-learn and XGBoost. The
concepts used to optimize the execution time and performance studies are
discussed in detail in this paper. The key ideas include: An equal-frequency
binning on the input data, which allows replacing expensive floating-point with
integer operations, while at the same time increasing the quality of the
classification; a cache-friendly linear access pattern to the input data, in
contrast to usual implementations, which exhibit a random access pattern.
FastBDT provides interfaces to C/C++, Python and TMVA. It is extensively used
in the field of high energy physics by the Belle II experiment.
Adaptive Decontamination of the Training Set: A Unified Formulation for Discriminative Visual Tracking
Authors: Martin Danelljan, Gustav Häger, Fahad Shahbaz Khan, Michael Felsberg
Date: 2016-09-20 00:00:00
Proceeding: CVPR 2016 6
Tasks: Visual Tracking

Tracking-by-detection methods have demonstrated competitive performance in
recent years. In these approaches, the tracking model heavily relies on the
quality of the training set. Due to the limited amount of labeled training
data, additional samples need to be extracted and labeled by the tracker
itself. This often leads to the inclusion of corrupted training samples, due to
occlusions, misalignments and other perturbations. Existing
tracking-by-detection methods either ignore this problem, or employ a separate
component for managing the training set.
  We propose a novel generic approach for alleviating the problem of corrupted
training samples in tracking-by-detection frameworks. Our approach dynamically
manages the training set by estimating the quality of the samples. Contrary to
existing approaches, we propose a unified formulation by minimizing a single
loss over both the target appearance model and the sample quality weights. The
joint formulation enables corrupted samples to be down-weighted while
increasing the impact of correct ones. Experiments are performed on three
benchmarks: OTB-2015 with 100 videos, VOT-2015 with 60 videos, and Temple-Color
with 128 videos. On the OTB-2015, our unified formulation significantly
improves the baseline, with a gain of 3.8% in mean overlap precision. Finally,
our method achieves state-of-the-art results on all three datasets. Code and
supplementary material are available at
http://www.cvl.isy.liu.se/research/objrec/visualtracking/decontrack/index.html .
The Digital Synaptic Neural Substrate: Size and Quality Matters
Authors: Azlan Iqbal
Date: 2016-09-20 00:00:00

We investigate the 'Digital Synaptic Neural Substrate' (DSNS) computational
creativity approach further with respect to the size and quality of images that
can be used to seed the process. In previous work we demonstrated how combining
photographs of people and sequences taken from chess games between weak players
can be used to generate chess problems or puzzles of higher aesthetic quality,
on average, compared to alternative approaches. In this work we show
experimentally that using larger images as opposed to smaller ones improves the
output quality even further. The same is also true for using clearer or less
corrupted images. The reasons why these things influence the DSNS process is
presently not well-understood and debatable but the findings are nevertheless
immediately applicable for obtaining better results.
Modelling Stock-market Investors as Reinforcement Learning Agents [Correction]
Authors: Alvin Pastore, Umberto Esposito, Eleni Vasilaki
Date: 2016-09-20 00:00:00
Tasks: Decision Making, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Decision making in uncertain and risky environments is a prominent area of
research. Standard economic theories fail to fully explain human behaviour,
while a potentially promising alternative may lie in the direction of
Reinforcement Learning (RL) theory. We analyse data for 46 players extracted
from a financial market online game and test whether Reinforcement Learning
(Q-Learning) could capture these players behaviour using a risk measure based
on financial modeling. Moreover we test an earlier hypothesis that players are
"na\"ive" (short-sighted). Our results indicate that a simple Reinforcement
Learning model which considers only the selling component of the task captures
the decision-making process for a subset of players but this is not sufficient
to draw any conclusion on the population. We also find that there is not a
significant improvement of fitting of the players when using a full RL model
against a myopic version, where only immediate reward is valued by the players.
This indicates that players, if using a Reinforcement Learning approach, do so
na\"ively
Learning Robust Representations of Text
Authors: Yitong Li, Trevor Cohn, Timothy Baldwin
Date: 2016-09-20 00:00:00
Proceeding: EMNLP 2016 11
Tasks: Sensitivity

Deep neural networks have achieved remarkable results across many language
processing tasks, however these methods are highly sensitive to noise and
adversarial attacks. We present a regularization based method for limiting
network sensitivity to its inputs, inspired by ideas from computer vision, thus
learning models that are more robust. Empirical evaluation over a range of
sentiment datasets with a convolutional neural network shows that, compared to
a baseline model and the dropout method, our method achieves superior
performance over noisy inputs and out-of-domain data.
Robust Fusion of Multi-Band Images with Different Spatial and Spectral Resolutions for Change Detection
Authors: Vinicius Ferraris, Nicolas Dobigeon, Qi Wei, Marie Chabert
Date: 2016-09-20 00:00:00
Tasks: Change Detection

Archetypal scenarios for change detection generally consider two images
acquired through sensors of the same modality. However, in some specific cases
such as emergency situations, the only images available may be those acquired
through different kinds of sensors. More precisely, this paper addresses the
problem of detecting changes between two multi-band optical images
characterized by different spatial and spectral resolutions. This sensor
dissimilarity introduces additional issues in the context of operational change
detection. To alleviate these issues, classical change detection methods are
applied after independent preprocessing steps (e.g., resampling) used to get
the same spatial and spectral resolutions for the pair of observed images.
Nevertheless, these preprocessing steps tend to throw away relevant
information. Conversely, in this paper, we propose a method that more
effectively uses the available information by modeling the two observed images
as spatial and spectral versions of two (unobserved) latent images
characterized by the same high spatial and high spectral resolutions. As they
cover the same scene, these latent images are expected to be globally similar
except for possible changes in sparse spatial locations. Thus, the change
detection task is envisioned through a robust multi-band image fusion method
which enforces the differences between the estimated latent images to be
spatially sparse. This robust fusion problem is formulated as an inverse
problem which is iteratively solved using an efficient block-coordinate descent
algorithm. The proposed method is applied to real panchormatic/multispectral
and hyperspectral images with simulated realistic changes. A comparison with
state-of-the-art change detection methods evidences the accuracy of the
proposed strategy.