pwc-archive/papers-with-abstracts huggingface dataset:
Building Graph Representations of Deep Vector Embeddings
Authors: Dario Garcia-Gasulla, Armand Vilalta, Ferran Parés, Jonatan Moreno, Eduard Ayguadé, Jesus Labarta, Ulises Cortés, Toyotaro Suzumura
Date: 2017-07-24 00:00:00
Proceeding: WS 2017 9
Tasks: Descriptive, Graph Embedding

Patterns stored within pre-trained deep neural networks compose large and
powerful descriptive languages that can be used for many different purposes.
Typically, deep network representations are implemented within vector embedding
spaces, which enables the use of traditional machine learning algorithms on top
of them. In this short paper we propose the construction of a graph embedding
space instead, introducing a methodology to transform the knowledge coded
within a deep convolutional network into a topological space (i.e. a network).
We outline how such graph can hold data instances, data features, relations
between instances and features, and relations among features. Finally, we
introduce some preliminary experiments to illustrate how the resultant graph
embedding space can be exploited through graph analytics algorithms.
Decoupled Learning of Environment Characteristics for Safe Exploration
Authors: Pieter Van Molle, Tim Verbelen, Steven Bohez, Sam Leroux, Pieter Simoens, Bart Dhoedt
Date: 2017-08-09 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Safe Exploration

Reinforcement learning is a proven technique for an agent to learn a task.
However, when learning a task using reinforcement learning, the agent cannot
distinguish the characteristics of the environment from those of the task. This
makes it harder to transfer skills between tasks in the same environment.
Furthermore, this does not reduce risk when training for a new task. In this
paper, we introduce an approach to decouple the environment characteristics
from the task-specific ones, allowing an agent to develop a sense of survival.
We evaluate our approach in an environment where an agent must learn a sequence
of collection tasks, and show that decoupled learning allows for a safer
utilization of prior knowledge.
SPLODE: Semi-Probabilistic Point and Line Odometry with Depth Estimation from RGB-D Camera Motion
Authors: Pedro F. Proença, Yang Gao
Date: 2017-08-09 00:00:00
Tasks: Depth Estimation, Motion Estimation, Visual Odometry

Active depth cameras suffer from several limitations, which cause incomplete
and noisy depth maps, and may consequently affect the performance of RGB-D
Odometry. To address this issue, this paper presents a visual odometry method
based on point and line features that leverages both measurements from a depth
sensor and depth estimates from camera motion. Depth estimates are generated
continuously by a probabilistic depth estimation framework for both types of
features to compensate for the lack of depth measurements and inaccurate
feature depth associations. The framework models explicitly the uncertainty of
triangulating depth from both point and line observations to validate and
obtain precise estimates. Furthermore, depth measurements are exploited by
propagating them through a depth map registration module and using a
frame-to-frame motion estimation method that considers 3D-to-2D and 2D-to-3D
reprojection errors, independently. Results on RGB-D sequences captured on
large indoor and outdoor scenes, where depth sensor limitations are critical,
show that the combination of depth measurements and estimates through our
approach is able to overcome the absence and inaccuracy of depth measurements.
Anveshak - A Groundtruth Generation Tool for Foreground Regions of Document Images
Authors: Soumyadeep Dey, Jayanta Mukherjee, Shamik Sural, Amit Vijay Nandedkar
Date: 2017-08-09 00:00:00

We propose a graphical user interface based groundtruth generation tool in
this paper. Here, annotation of an input document image is done based on the
foreground pixels. Foreground pixels are grouped together with user interaction
to form labeling units. These units are then labeled by the user with the user
defined labels. The output produced by the tool is an image with an XML file
containing its metadata information. This annotated data can be further used in
different applications of document image analysis.
Exploit imaging through opaque wall via deep learning
Authors: Meng Lyu, Hao Wang, Guowei Li, Guohai Situ
Date: 2017-08-09 00:00:00
Tasks: Astronomy, Deep Learning

Imaging through scattering media is encountered in many disciplines or
sciences, ranging from biology, mesescopic physics and astronomy. But it is
still a big challenge because light suffers from multiple scattering is such
media and can be totally decorrelated. Here, we propose a deep-learning-based
method that can retrieve the image of a target behind a thick scattering
medium. The method uses a trained deep neural network to fit the way of mapping
of objects at one side of a thick scattering medium to the corresponding
speckle patterns observed at the other side. For demonstration, we retrieve the
images of a set of objects hidden behind a 3mm thick white polystyrene slab,
the optical depth of which is 13.4 times of the scattering mean free path. Our
work opens up a new way to tackle the longstanding challenge by using the
technique of deep learning.
Importance of Image Enhancement Techniques in Color Image Segmentation: A Comprehensive and Comparative Study
Authors: Dibya Jyoti Bora
Date: 2017-08-09 00:00:00
Tasks: Image Enhancement, Image Segmentation, Segmentation, Semantic Segmentation

Color image segmentation is a very emerging research topic in the area of
color image analysis and pattern recognition. Many state-of-the-art algorithms
have been developed for this purpose. But, often the segmentation results of
these algorithms seem to be suffering from miss-classifications and
over-segmentation. The reasons behind these are the degradation of image
quality during the acquisition, transmission and color space conversion. So,
here arises the need of an efficient image enhancement technique which can
remove the redundant pixels or noises from the color image before proceeding
for final segmentation. In this paper, an effort has been made to study and
analyze different image enhancement techniques and thereby finding out the
better one for color image segmentation. Also, this comparative study is done
on two well-known color spaces HSV and LAB separately to find out which color
space supports segmentation task more efficiently with respect to those
enhancement techniques.
Stochastic Optimization with Bandit Sampling
Authors: Farnood Salehi, L. Elisa Celis, Patrick Thiran
Date: 2017-08-08 00:00:00
Tasks: Stochastic Optimization

Many stochastic optimization algorithms work by estimating the gradient of
the cost function on the fly by sampling datapoints uniformly at random from a
training set. However, the estimator might have a large variance, which
inadvertently slows down the convergence rate of the algorithms. One way to
reduce this variance is to sample the datapoints from a carefully selected
non-uniform distribution. In this work, we propose a novel non-uniform sampling
approach that uses the multi-armed bandit framework. Theoretically, we show
that our algorithm asymptotically approximates the optimal variance within a
factor of 3. Empirically, we show that using this datapoint-selection technique
results in a significant reduction in the convergence time and variance of
several stochastic optimization algorithms such as SGD, SVRG and SAGA. This
approach for sampling datapoints is general, and can be used in conjunction
with any algorithm that uses an unbiased gradient estimation -- we expect it to
have broad applicability beyond the specific examples explored in this work.
Full-Network Embedding in a Multimodal Embedding Pipeline
Authors: Armand Vilalta, Dario Garcia-Gasulla, Ferran Parés, Eduard Ayguadé, Jesus Labarta, Ulises Cortés, Toyotaro Suzumura
Date: 2017-07-24 00:00:00
Proceeding: WS 2017 9
Tasks: Image Retrieval, Network Embedding, Retrieval

The current state-of-the-art for image annotation and image retrieval tasks
is obtained through deep neural networks, which combine an image representation
and a text representation into a shared embedding space. In this paper we
evaluate the impact of using the Full-Network embedding in this setting,
replacing the original image representation in a competitive multimodal
embedding generation scheme. Unlike the one-layer image embeddings typically
used by most approaches, the Full-Network embedding provides a multi-scale
representation of images, which results in richer characterizations. To measure
the influence of the Full-Network embedding, we evaluate its performance on
three different datasets, and compare the results with the original multimodal
embedding generation scheme when using a one-layer image embedding, and with
the rest of the state-of-the-art. Results for image annotation and image
retrieval tasks indicate that the Full-Network embedding is consistently
superior to the one-layer embedding. These results motivate the integration of
the Full-Network embedding on any multimodal embedding generation scheme,
something feasible thanks to the flexibility of the approach.
AFP Algorithm and a Canonical Normal Form for Horn Formulas
Authors: Ruhollah Majdoddin
Date: 2014-08-20 00:00:00
Tasks: Form

AFP Algorithm is a learning algorithm for Horn formulas. We show that it does
not improve the complexity of AFP Algorithm, if after each negative
counterexample more that just one refinements are performed. Moreover, a
canonical normal form for Horn formulas is presented, and it is proved that the
output formula of AFP Algorithm is in this normal form.
BlitzNet: A Real-Time Deep Network for Scene Understanding
Authors: Nikita Dvornik, Konstantin Shmelkov, Julien Mairal, Cordelia Schmid
Date: 2017-08-09 00:00:00
Proceeding: ICCV 2017 10
Tasks: Autonomous Driving, Object, object-detection, Object Detection, Real-Time Object Detection, Real-Time Semantic Segmentation, Scene Understanding, Segmentation, Semantic Segmentation

Real-time scene understanding has become crucial in many applications such as
autonomous driving. In this paper, we propose a deep architecture, called
BlitzNet, that jointly performs object detection and semantic segmentation in
one forward pass, allowing real-time computations. Besides the computational
gain of having a single network to perform several tasks, we show that object
detection and semantic segmentation benefit from each other in terms of
accuracy. Experimental results for VOC and COCO datasets show state-of-the-art
performance for object detection and segmentation among real time systems.
Preference fusion and Condorcet's Paradox under uncertainty
Authors: Yiru Zhang, Tassadit Bouadi, Arnaud Martin
Date: 2017-08-09 00:00:00

Facing an unknown situation, a person may not be able to firmly elicit
his/her preferences over different alternatives, so he/she tends to express
uncertain preferences. Given a community of different persons expressing their
preferences over certain alternatives under uncertainty, to get a collective
representative opinion of the whole community, a preference fusion process is
required. The aim of this work is to propose a preference fusion method that
copes with uncertainty and escape from the Condorcet paradox. To model
preferences under uncertainty, we propose to develop a model of preferences
based on belief function theory that accurately describes and captures the
uncertainty associated with individual or collective preferences. This work
improves and extends the previous results. This work improves and extends the
contribution presented in a previous work. The benefits of our contribution are
twofold. On the one hand, we propose a qualitative and expressive preference
modeling strategy based on belief-function theory which scales better with the
number of sources. On the other hand, we propose an incremental distance-based
algorithm (using Jousselme distance) for the construction of the collective
preference order to avoid the Condorcet Paradox.
DeepFaceLIFT: Interpretable Personalized Models for Automatic Estimation of Self-Reported Pain
Authors: Dianbo Liu, Fengjiao Peng, Andrew Shea, Ognjen, Rudovic, Rosalind Picard
Date: 2017-08-09 00:00:00
Tasks: Multi-Task Learning

Previous research on automatic pain estimation from facial expressions has
focused primarily on "one-size-fits-all" metrics (such as PSPI). In this work,
we focus on directly estimating each individual's self-reported visual-analog
scale (VAS) pain metric, as this is considered the gold standard for pain
measurement. The VAS pain score is highly subjective and context-dependent, and
its range can vary significantly among different persons. To tackle these
issues, we propose a novel two-stage personalized model, named DeepFaceLIFT,
for automatic estimation of VAS. This model is based on (1) Neural Network and
(2) Gaussian process regression models, and is used to personalize the
estimation of self-reported pain via a set of hand-crafted personal features
and multi-task learning. We show on the benchmark dataset for pain analysis
(The UNBC-McMaster Shoulder Pain Expression Archive) that the proposed
personalized model largely outperforms the traditional, unpersonalized models:
the intra-class correlation improves from a baseline performance of 19\% to a
personalized performance of 35\% while also providing confidence in the
model\textquotesingle s estimates -- in contrast to existing models for the
target task. Additionally, DeepFaceLIFT automatically discovers the
pain-relevant facial regions for each person, allowing for an easy
interpretation of the pain-related facial cues.
Neighborhood-Based Label Propagation in Large Protein Graphs
Authors: Sabeur Aridhi, Seyed Ziaeddin Alborzi, Malika Smaïl-Tabbone, Marie-Dominique Devignes, David Ritchie
Date: 2017-08-09 00:00:00
Tasks: Drug Discovery

Understanding protein function is one of the keys to understanding life at
the molecular level. It is also important in several scenarios including human
disease and drug discovery. In this age of rapid and affordable biological
sequencing, the number of sequences accumulating in databases is rising with an
increasing rate. This presents many challenges for biologists and computer
scientists alike. In order to make sense of this huge quantity of data, these
sequences should be annotated with functional properties. UniProtKB consists of
two components: i) the UniProtKB/Swiss-Prot database containing protein
sequences with reliable information manually reviewed by expert bio-curators
and ii) the UniProtKB/TrEMBL database that is used for storing and processing
the unknown sequences. Hence, for all proteins we have available the sequence
along with few more information such as the taxon and some structural domains.
Pairwise similarity can be defined and computed on proteins based on such
attributes. Other important attributes, while present for proteins in
Swiss-Prot, are often missing for proteins in TrEMBL, such as their function
and cellular localization. The enormous number of protein sequences now in
TrEMBL calls for rapid procedures to annotate them automatically. In this work,
we present DistNBLP, a novel Distributed Neighborhood-Based Label Propagation
approach for large-scale annotation of proteins. To do this, the functional
annotations of reviewed proteins are used to predict those of non-reviewed
proteins using label propagation on a graph representation of the protein
database. DistNBLP is built on top of the "akka" toolkit for building resilient
distributed message-driven applications.
Jet Constituents for Deep Neural Network Based Top Quark Tagging
Authors: Jannicke Pearkes, Wojciech Fedorko, Alison Lister, Colin Gay
Date: 2017-04-07 00:00:00
Tasks: General Classification

Recent literature on deep neural networks for tagging of highly energetic
jets resulting from top quark decays has focused on image based techniques or
multivariate approaches using high-level jet substructure variables. Here, a
sequential approach to this task is taken by using an ordered sequence of jet
constituents as training inputs. Unlike the majority of previous approaches,
this strategy does not result in a loss of information during pixelisation or
the calculation of high level features. The jet classification method achieves
a background rejection of 45 at a 50% efficiency operating point for
reconstruction level jets with transverse momentum range of 600 to 2500 GeV and
is insensitive to multiple proton-proton interactions at the levels expected
throughout Run 2 of the LHC.
Parallelizing Spectral Algorithms for Kernel Learning
Authors: Gilles Blanchard, Nicole Mücke
Date: 2016-10-24 00:00:00
Tasks: regression

We consider a distributed learning approach in supervised learning for a
large class of spectral regularization methods in an RKHS framework. The data
set of size n is partitioned into $m=O(n^\alpha)$ disjoint subsets. On each
subset, some spectral regularization method (belonging to a large class,
including in particular Kernel Ridge Regression, $L^2$-boosting and spectral
cut-off) is applied. The regression function $f$ is then estimated via simple
averaging, leading to a substantial reduction in computation time. We show that
minimax optimal rates of convergence are preserved if m grows sufficiently
slowly (corresponding to an upper bound for $\alpha$) as $n \to \infty$,
depending on the smoothness assumptions on $f$ and the intrinsic
dimensionality. In spirit, our approach is classical.
Non-Adaptive Randomized Algorithm for Group Testing
Authors: Nader H. Bshouty, Nuha Diab, Shada R. Kawar, Robert J. Shahla
Date: 2017-08-09 00:00:00

We study the problem of group testing with a non-adaptive randomized
algorithm in the random incidence design (RID) model where each entry in the
test is chosen randomly independently from $\{0,1\}$ with a fixed probability
$p$.
  The property that is sufficient and necessary for a unique decoding is the
separability of the tests, but unfortunately no linear time algorithm is known
for such tests. In order to achieve linear-time decodable tests, the algorithms
in the literature use the disjunction property that gives almost optimal number
of tests.
  We define a new property for the tests which we call semi-disjunction
property. We show that there is a linear time decoding for such test and for
$d\to \infty$ the number of tests converges to the number of tests with the
separability property and is therefore optimal (in the RID model). Our analysis
shows that, in the RID model, the number of tests in our algorithm is better
than the one with the disjunction property even for small $d$.
ProjectionNet: Learning Efficient On-Device Deep Networks Using Neural Projections
Authors: Sujith Ravi
Date: 2017-08-02 00:00:00
Tasks: Image Classification, text-classification, Text Classification

Deep neural networks have become ubiquitous for applications related to
visual recognition and language understanding tasks. However, it is often
prohibitive to use typical neural networks on devices like mobile phones or
smart watches since the model sizes are huge and cannot fit in the limited
memory available on such devices. While these devices could make use of machine
learning models running on high-performance data centers with CPUs or GPUs,
this is not feasible for many applications because data can be privacy
sensitive and inference needs to be performed directly "on" device.
  We introduce a new architecture for training compact neural networks using a
joint optimization framework. At its core lies a novel objective that jointly
trains using two different types of networks--a full trainer neural network
(using existing architectures like Feed-forward NNs or LSTM RNNs) combined with
a simpler "projection" network that leverages random projections to transform
inputs or intermediate representations into bits. The simpler network encodes
lightweight and efficient-to-compute operations in bit space with a low memory
footprint. The two networks are trained jointly using backpropagation, where
the projection network learns from the full network similar to apprenticeship
learning. Once trained, the smaller network can be used directly for inference
at low memory and computation cost. We demonstrate the effectiveness of the new
approach at significantly shrinking the memory requirements of different types
of neural networks while preserving good accuracy on visual recognition and
text classification tasks. We also study the question "how many neural bits are
required to solve a given task?" using the new framework and show empirical
results contrasting model predictive capacity (in bits) versus accuracy on
several datasets.
Encoding Word Confusion Networks with Recurrent Neural Networks for Dialog State Tracking
Authors: Glorianna Jagfeld, Ngoc Thang Vu
Date: 2017-07-18 00:00:00
Proceeding: WS 2017 9
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), dialog state tracking, speech-recognition, Speech Recognition

This paper presents our novel method to encode word confusion networks, which
can represent a rich hypothesis space of automatic speech recognition systems,
via recurrent neural networks. We demonstrate the utility of our approach for
the task of dialog state tracking in spoken dialog systems that relies on
automatic speech recognition output. Encoding confusion networks outperforms
encoding the best hypothesis of the automatic speech recognition in a neural
system for dialog state tracking on the well-known second Dialog State Tracking
Challenge dataset.
Role of Secondary Attributes to Boost the Prediction Accuracy of Students Employability Via Data Mining
Authors: Pooja Thakar, Anil Mehta, Manisha
Date: 2017-08-09 00:00:00
Tasks: Fraud Detection, Management, Prediction

Data Mining is best-known for its analytical and prediction capabilities. It
is used in several areas such as fraud detection, predicting client behavior,
money market behavior, bankruptcy prediction. It can also help in establishing
an educational ecosystem, which discovers useful knowledge, and assist
educators to take proactive decisions to boost student performance and
employability. This paper presents an empirical study that compares varied
classification algorithms on two datasets of MCA (Masters in Computer
Applications) students collected from various affiliated colleges of a reputed
state university in India. One dataset includes only primary attributes,
whereas other dataset is feeded with secondary psychometric attributes in it.
The results showcase that solely primary academic attributes do not lead to
smart prediction accuracy of students employability, once they square measure
within the initial year of their education. The study analyzes and stresses the
role of secondary psychometric attributes for better prediction accuracy and
analysis of students performance. Timely prediction and analysis of students
performance can help Management, Teachers and Students to work on their gray
areas for better results and employment opportunities.
Multi-dimensional Gated Recurrent Units for Automated Anatomical Landmark Localization
Authors: Simon Andermatt, Simon Pezold, Michael Amann, Philippe C. Cattin
Date: 2017-08-09 00:00:00

We present an automated method for localizing an anatomical landmark in
three-dimensional medical images. The method combines two recurrent neural
networks in a coarse-to-fine approach: The first network determines a candidate
neighborhood by analyzing the complete given image volume. The second network
localizes the actual landmark precisely and accurately in the candidate
neighborhood. Both networks take advantage of multi-dimensional gated recurrent
units in their main layers, which allow for high model complexity with a
comparatively small set of parameters. We localize the medullopontine sulcus in
3D magnetic resonance images of the head and neck. We show that the proposed
approach outperforms similar localization techniques both in terms of mean
distance in millimeters and voxels w.r.t. manual labelings of the data. With a
mean localization error of 1.7 mm, the proposed approach performs on par with
neurological experts, as we demonstrate in an interrater comparison.