pwc-archive/papers-with-abstracts huggingface dataset:
Fast Iteratively Reweighted Least Squares Algorithms for Analysis-Based Sparsity Reconstruction
Authors: Chen Chen, Junzhou Huang, Lei He, Hongsheng Li
Date: 2014-11-18 00:00:00
Tasks: Compressive Sensing

In this paper, we propose a novel algorithm for analysis-based sparsity
reconstruction. It can solve the generalized problem by structured sparsity
regularization with an orthogonal basis and total variation regularization. The
proposed algorithm is based on the iterative reweighted least squares (IRLS)
model, which is further accelerated by the preconditioned conjugate gradient
method. The convergence rate of the proposed algorithm is almost the same as
that of the traditional IRLS algorithms, that is, exponentially fast. Moreover,
with the specifically devised preconditioner, the computational cost for each
iteration is significantly less than that of traditional IRLS algorithms, which
enables our approach to handle large scale problems. In addition to the fast
convergence, it is straightforward to apply our method to standard sparsity,
group sparsity, overlapping group sparsity and TV based problems. Experiments
are conducted on a practical application: compressive sensing magnetic
resonance imaging. Extensive results demonstrate that the proposed algorithm
achieves superior performance over 14 state-of-the-art algorithms in terms of
both accuracy and computational cost.
A Robust Lane Detection and Departure Warning System
Authors: Mrinal Haloi, Dinesh Babu Jayagopi
Date: 2015-04-28 00:00:00
Tasks: Lane Detection, Optical Flow Estimation

In this work, we have developed a robust lane detection and departure warning
technique. Our system is based on single camera sensor. For lane detection a
modified Inverse Perspective Mapping using only a few extrinsic camera
parameters and illuminant Invariant techniques is used. Lane markings are
represented using a combination of 2nd and 4th order steerable filters, robust
to shadowing. Effect of shadowing and extra sun light are removed using Lab
color space, and illuminant invariant representation. Lanes are assumed to be
cubic curves and fitted using robust RANSAC. This method can reliably detect
lanes of the road and its boundary. This method has been experimented in Indian
road conditions under different challenging situations and the result obtained
were very good. For lane departure angle an optical flow based method were
used.
Joint Learning of Distributed Representations for Images and Texts
Authors: Xiaodong He, Rupesh Srivastava, Jianfeng Gao, Li Deng
Date: 2015-04-13 00:00:00
Tasks: Semantic Similarity, Semantic Textual Similarity

This technical report provides extra details of the deep multimodal
similarity model (DMSM) which was proposed in (Fang et al. 2015,
arXiv:1411.4952). The model is trained via maximizing global semantic
similarity between images and their captions in natural language using the
public Microsoft COCO database, which consists of a large set of images and
their corresponding captions. The learned representations attempt to capture
the combination of various visual concepts and cues.
Becoming the Expert - Interactive Multi-Class Machine Teaching
Authors: Edward Johns, Oisin Mac Aodha, Gabriel J. Brostow
Date: 2015-04-28 00:00:00
Proceeding: CVPR 2015 6

Compared to machines, humans are extremely good at classifying images into
categories, especially when they possess prior knowledge of the categories at
hand. If this prior information is not available, supervision in the form of
teaching images is required. To learn categories more quickly, people should
see important and representative images first, followed by less important
images later - or not at all. However, image-importance is individual-specific,
i.e. a teaching image is important to a student if it changes their overall
ability to discriminate between classes. Further, students keep learning, so
while image-importance depends on their current knowledge, it also varies with
time.
  In this work we propose an Interactive Machine Teaching algorithm that
enables a computer to teach challenging visual concepts to a human. Our
adaptive algorithm chooses, online, which labeled images from a teaching set
should be shown to the student as they learn. We show that a teaching strategy
that probabilistically models the student's ability and progress, based on
their correct and incorrect answers, produces better 'experts'. We present
results using real human participants across several varied and challenging
real-world datasets.
Can Machines Truly Think
Authors: Murat Okandan
Date: 2015-04-28 00:00:00

Can machines truly think? This question and its answer have many implications
that depend, in large part, on any number of assumptions underlying how the
issue has been addressed or considered previously. A crucial question, and one
that is almost taken for granted, is the starting point for this discussion:
Can "thought" be achieved or emulated by algorithmic procedures?
Using Syntax-Based Machine Translation to Parse English into Abstract Meaning Representation
Authors: Michael Pust, Ulf Hermjakob, Kevin Knight, Daniel Marcu, Jonathan May
Date: 2015-04-24 00:00:00
Tasks: Abstract Meaning Representation, Language Modeling, Language Modelling, Machine Translation, Translation

We present a parser for Abstract Meaning Representation (AMR). We treat
English-to-AMR conversion within the framework of string-to-tree, syntax-based
machine translation (SBMT). To make this work, we transform the AMR structure
into a form suitable for the mechanics of SBMT and useful for modeling. We
introduce an AMR-specific language model and add data and features drawn from
semantic resources. Our resulting AMR parser improves upon state-of-the-art
results by 7 Smatch points.
Differentially Private Release and Learning of Threshold Functions
Authors: Mark Bun, Kobbi Nissim, Uri Stemmer, Salil Vadhan
Date: 2015-04-28 00:00:00
Tasks: PAC learning

We prove new upper and lower bounds on the sample complexity of $(\epsilon, \delta)$ differentially private algorithms for releasing approximate answers to threshold functions. A threshold function $c_x$ over a totally ordered domain $X$ evaluates to $c_x(y) = 1$ if $y \le x$, and evaluates to $0$ otherwise. We give the first nontrivial lower bound for releasing thresholds with $(\epsilon,\delta)$ differential privacy, showing that the task is impossible over an infinite domain $X$, and moreover requires sample complexity $n \ge \Omega(\log^*|X|)$, which grows with the size of the domain. Inspired by the techniques used to prove this lower bound, we give an algorithm for releasing thresholds with $n \le 2^{(1+ o(1))\log^*|X|}$ samples. This improves the previous best upper bound of $8^{(1 + o(1))\log^*|X|}$ (Beimel et al., RANDOM '13). Our sample complexity upper and lower bounds also apply to the tasks of learning distributions with respect to Kolmogorov distance and of properly PAC learning thresholds with differential privacy. The lower bound gives the first separation between the sample complexity of properly learning a concept class with $(\epsilon,\delta)$ differential privacy and learning without privacy. For properly learning thresholds in $\ell$ dimensions, this lower bound extends to $n \ge \Omega(\ell \cdot \log^*|X|)$. To obtain our results, we give reductions in both directions from releasing and properly learning thresholds and the simpler interior point problem. Given a database $D$ of elements from $X$, the interior point problem asks for an element between the smallest and largest elements in $D$. We introduce new recursive constructions for bounding the sample complexity of the interior point problem, as well as further reductions and techniques for proving impossibility results for other basic problems in differential privacy.
Speeding Up Neural Networks for Large Scale Classification using WTA Hashing
Authors: Amir H. Bakhtiary, Agata Lapedriza, David Masip
Date: 2015-04-28 00:00:00
Tasks: General Classification

In this paper we propose to use the Winner Takes All hashing technique to
speed up forward propagation and backward propagation in fully connected layers
in convolutional neural networks. The proposed technique reduces significantly
the computational complexity, which in turn, allows us to train layers with a
large number of kernels with out the associated time penalty.
  As a consequence we are able to train convolutional neural network on a very
large number of output classes with only a small increase in the computational
cost. To show the effectiveness of the technique we train a new output layer on
a pretrained network using both the regular multiplicative approach and our
proposed hashing methodology. Our results showed no drop in performance and
demonstrate, with our implementation, a 7 fold speed up during the training.
Entity-Augmented Distributional Semantics for Discourse Relations
Authors: Yangfeng Ji, Jacob Eisenstein
Date: 2014-12-17 00:00:00
Tasks: Relation, Sentence

Discourse relations bind smaller linguistic elements into coherent texts.
However, automatically identifying discourse relations is difficult, because it
requires understanding the semantics of the linked sentences. A more subtle
challenge is that it is not enough to represent the meaning of each sentence of
a discourse relation, because the relation may depend on links between
lower-level elements, such as entity mentions. Our solution computes
distributional meaning representations by composition up the syntactic parse
tree. A key difference from previous work on compositional distributional
semantics is that we also compute representations for entity mentions, using a
novel downward compositional pass. Discourse relations are predicted not only
from the distributional representations of the sentences, but also of their
coreferent entity mentions. The resulting system obtains substantial
improvements over the previous state-of-the-art in predicting implicit
discourse relations in the Penn Discourse Treebank.
Identifying Reliable Annotations for Large Scale Image Segmentation
Authors: Alexander Kolesnikov, Christoph H. Lampert
Date: 2015-04-28 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Challenging computer vision tasks, in particular semantic image segmentation,
require large training sets of annotated images. While obtaining the actual
images is often unproblematic, creating the necessary annotation is a tedious
and costly process. Therefore, one often has to work with unreliable annotation
sources, such as Amazon Mechanical Turk or (semi-)automatic algorithmic
techniques. In this work, we present a Gaussian process (GP) based technique
for simultaneously identifying which images of a training set have unreliable
annotation and learning a segmentation model in which the negative effect of
these images is suppressed. Alternatively, the model can also just be used to
identify the most reliably annotated images from the training set, which can
then be used for training any other segmentation method. By relying on "deep
features" in combination with a linear covariance function, our GP can be
learned and its hyperparameter determined efficiently using only matrix
operations and gradient-based optimization. This makes our method scalable even
to large datasets with several million training instances.
CommentWatcher: An Open Source Web-based platform for analyzing discussions on web forums
Authors: Marian-Andrei Rizoiu, Adrien Guille, Julien Velcin
Date: 2015-04-28 00:00:00
Tasks: Diversity

We present CommentWatcher, an open source tool aimed at analyzing discussions
on web forums. Constructed as a web platform, CommentWatcher features automatic
mass fetching of user posts from forum on multiple sites, extracting topics,
visualizing the topics as an expression cloud and exploring their temporal
evolution. The underlying social network of users is simultaneously constructed
using the citation relations between users and visualized as a graph structure.
Our platform addresses the issues of the diversity and dynamics of structures
of webpages hosting the forums by implementing a parser architecture that is
independent of the HTML structure of webpages. This allows easy on-the-fly
adding of new websites. Two types of users are targeted: end users who seek to
study the discussed topics and their temporal evolution, and researchers in
need of establishing a forum benchmark dataset and comparing the performances
of analysis tools.
Embedded Platforms for Computer Vision-based Advanced Driver Assistance Systems: a Survey
Authors: Gorka Velez, Oihana Otaegui
Date: 2015-04-28 00:00:00

Computer Vision, either alone or combined with other technologies such as
radar or Lidar, is one of the key technologies used in Advanced Driver
Assistance Systems (ADAS). Its role understanding and analysing the driving
scene is of great importance as it can be noted by the number of ADAS
applications that use this technology. However, porting a vision algorithm to
an embedded automotive system is still very challenging, as there must be a
trade-off between several design requisites. Furthermore, there is not a
standard implementation platform, so different alternatives have been proposed
by both the scientific community and the industry. This paper aims to review
the requisites and the different embedded implementation platforms that can be
used for Computer Vision-based ADAS, with a critical analysis and an outlook to
future trends.
Formalising Hypothesis Virtues in Knowledge Graphs: A General Theoretical Framework and its Validation in Literature-Based Discovery Experiments
Authors: Vit Novacek
Date: 2015-03-31 00:00:00
Tasks: Knowledge Graphs, Philosophy

We introduce an approach to discovery informatics that uses so called
knowledge graphs as the essential representation structure. Knowledge graph is
an umbrella term that subsumes various approaches to tractable representation
of large volumes of loosely structured knowledge in a graph form. It has been
used primarily in the Web and Linked Open Data contexts, but is applicable to
any other area dealing with knowledge representation. In the perspective of our
approach motivated by the challenges of discovery informatics, knowledge graphs
correspond to hypotheses. We present a framework for formalising so called
hypothesis virtues within knowledge graphs. The framework is based on a classic
work in philosophy of science, and naturally progresses from mostly informative
foundational notions to actionable specifications of measures corresponding to
particular virtues. These measures can consequently be used to determine
refined sub-sets of knowledge graphs that have large relative potential for
making discoveries. We validate the proposed framework by experiments in
literature-based discovery. The experiments have demonstrated the utility of
our work and its superiority w.r.t. related approaches.
Provable Methods for Training Neural Networks with Sparse Connectivity
Authors: Hanie Sedghi, Anima Anandkumar
Date: 2014-12-08 00:00:00

We provide novel guaranteed approaches for training feedforward neural
networks with sparse connectivity. We leverage on the techniques developed
previously for learning linear networks and show that they can also be
effectively adopted to learn non-linear networks. We operate on the moments
involving label and the score function of the input, and show that their
factorization provably yields the weight matrix of the first layer of a deep
network under mild conditions. In practice, the output of our method can be
employed as effective initializers for gradient descent.
Lexical Translation Model Using a Deep Neural Network Architecture
Authors: Thanh-Le Ha, Jan Niehues, Alex Waibel
Date: 2015-04-28 00:00:00
Tasks: Sentence, Translation

In this paper we combine the advantages of a model using global source
sentence contexts, the Discriminative Word Lexicon, and neural networks. By
using deep neural networks instead of the linear maximum entropy model in the
Discriminative Word Lexicon models, we are able to leverage dependencies
between different source words due to the non-linearity. Furthermore, the
models for different target words can share parameters and therefore data
sparsity problems are effectively reduced.
  By using this approach in a state-of-the-art translation system, we can
improve the performance by up to 0.5 BLEU points for three different language
pairs on the TED translation task.
Building Classifiers to Predict the Start of Glucose-Lowering Pharmacotherapy Using Belgian Health Expenditure Data
Authors: Marc Claesen, Frank De Smet, Pieter Gillard, Chantal Mathieu, Bart De Moor
Date: 2015-04-28 00:00:00
Tasks: Prognosis

Early diagnosis is important for type 2 diabetes (T2D) to improve patient
prognosis, prevent complications and reduce long-term treatment costs. We
present a novel risk profiling approach based exclusively on health expenditure
data that is available to Belgian mutual health insurers. We used expenditure
data related to drug purchases and medical provisions to construct models that
predict whether a patient will start glucose-lowering pharmacotherapy in the
coming years, based on that patient's recent medical expenditure history. The
design and implementation of the modeling strategy are discussed in detail and
several learning methods are benchmarked for our application. Our best
performing model obtains between 74.9% and 76.8% area under the ROC curve,
which is comparable to state-of-the-art risk prediction approaches for T2D
based on questionnaires. In contrast to other methods, our approach can be
implemented on a population-wide scale at virtually no extra operational cost.
Possibly, our approach can be further improved by additional information about
some risk factors of T2D that is unavailable in health expenditure data.
Audio Source Separation with Discriminative Scattering Networks
Authors: Pablo Sprechmann, Joan Bruna, Yann Lecun
Date: 2014-12-22 00:00:00
Tasks: Audio Source Separation

In this report we describe an ongoing line of research for solving
single-channel source separation problems. Many monaural signal decomposition
techniques proposed in the literature operate on a feature space consisting of
a time-frequency representation of the input data. A challenge faced by these
approaches is to effectively exploit the temporal dependencies of the signals
at scales larger than the duration of a time-frame. In this work we propose to
tackle this problem by modeling the signals using a time-frequency
representation with multiple temporal resolutions. The proposed representation
consists of a pyramid of wavelet scattering operators, which generalizes
Constant Q Transforms (CQT) with extra layers of convolution and complex
modulus. We first show that learning standard models with this multi-resolution
setting improves source separation results over fixed-resolution methods. As
study case, we use Non-Negative Matrix Factorizations (NMF) that has been
widely considered in many audio application. Then, we investigate the inclusion
of the proposed multi-resolution setting into a discriminative training regime.
We discuss several alternatives using different deep neural network
architectures.
Combined A*-Ants Algorithm: A New Multi-Parameter Vehicle Navigation Scheme
Authors: Hojjat Salehinejad, Hossein Nezamabadi-pour, Saeid Saryazdi, Fereydoun Farrahi-Moghaddam
Date: 2015-04-28 00:00:00

In this paper a multi-parameter A*(A- star)-ants based algorithm is proposed
in order to find the best optimized multi-parameter path between two desired
points in regions. This algorithm recognizes paths, according to user desired
parameters using electronic maps. The proposed algorithm is a combination of A*
and ants algorithm in which the proposed A* algorithm is the prologue to the
suggested ant based algorithm .In fact, this A* algorithm invigorates some
paths pheromones in ants algorithm. As one of implementations of this method,
this algorithm was applied on a part of Kerman city, Iran as a multi-parameter
vehicle navigator. It finds the best optimized multi-parameter direction
between two desired junctions based on city traveler parameters. Comparison
results between the proposed method and ants algorithm demonstrates efficiency
and lower cost function results of the proposed method versus ants algorithm.
Toward Smart Power Grids: Communication Network Design for Power Grids Synchronization
Authors: Hojjat Salehinejad, Farhad Pouladi, Siamak Talebi
Date: 2015-04-28 00:00:00

In smart power grids, keeping the synchronicity of generators and the
corresponding controls is of great importance. To do so, a simple model is
employed in terms of swing equation to represent the interactions among
dynamics of generators and feedback control. In case of having a communication
network available, the control can be done based on the transmitted
measurements by the communication network. The stability of system is denoted
by the largest eigenvalue of the weighted sum of the Laplacian matrices of the
communication infrastructure and power network. In this work, we use graph
theory to model the communication network as a graph problem. Then, Ant Colony
System (ACS) is employed for optimum design of above graph for synchronization
of power grids. Performance evaluation of the proposed method for the 39-bus
New England power system versus methods such as exhaustive search and Rayleigh
quotient approximation indicates feasibility and effectiveness of our method
for even large scale smart power grids.
Reader-Aware Multi-Document Summarization via Sparse Coding
Authors: Piji Li, Lidong Bing, Wai Lam, Hang Li, Yi Liao
Date: 2015-04-28 00:00:00
Tasks: Document Summarization, Multi-Document Summarization, Reader-Aware Summarization

We propose a new MDS paradigm called reader-aware multi-document
summarization (RA-MDS). Specifically, a set of reader comments associated with
the news reports are also collected. The generated summaries from the reports
for the event should be salient according to not only the reports but also the
reader comments. To tackle this RA-MDS problem, we propose a
sparse-coding-based method that is able to calculate the salience of the text
units by jointly considering news reports and reader comments. Another
reader-aware characteristic of our framework is to improve linguistic quality
via entity rewriting. The rewriting consideration is jointly assessed together
with other summarization requirements under a unified optimization model. To
support the generation of compressive summaries via optimization, we explore a
finer syntactic unit, namely, noun/verb phrase. In this work, we also generate
a data set for conducting RA-MDS. Extensive experiments on this data set and
some classical data sets demonstrate the effectiveness of our proposed
approach.
Private Disclosure of Information in Health Tele-monitoring
Authors: Daniel Aranki, Ruzena Bajcsy
Date: 2015-04-28 00:00:00

We present a novel framework, called Private Disclosure of Information (PDI),
which is aimed to prevent an adversary from inferring certain sensitive
information about subjects using the data that they disclosed during
communication with an intended recipient. We show cases where it is possible to
achieve perfect privacy regardless of the adversary's auxiliary knowledge while
preserving full utility of the information to the intended recipient and
provide sufficient conditions for such cases. We also demonstrate the
applicability of PDI on a real-world data set that simulates a health
tele-monitoring scenario.