pwc-archive/papers-with-abstracts huggingface dataset:
Recommending Investors for Crowdfunding Projects
Authors: Jisun An, Daniele Quercia, Jon Crowcroft
Date: 2014-09-26 00:00:00

To bring their innovative ideas to market, those embarking in new ventures
have to raise money, and, to do so, they have often resorted to banks and
venture capitalists. Nowadays, they have an additional option: that of
crowdfunding. The name refers to the idea that funds come from a network of
people on the Internet who are passionate about supporting others' projects.
One of the most popular crowdfunding sites is Kickstarter. In it, creators post
descriptions of their projects and advertise them on social media sites (mainly
Twitter), while investors look for projects to support. The most common reason
for project failure is the inability of founders to connect with a sufficient
number of investors, and that is mainly because hitherto there has not been any
automatic way of matching creators and investors. We thus set out to propose
different ways of recommending investors found on Twitter for specific
Kickstarter projects. We do so by conducting hypothesis-driven analyses of
pledging behavior and translate the corresponding findings into different
recommendation strategies. The best strategy achieves, on average, 84% of
accuracy in predicting a list of potential investors' Twitter accounts for any
given project. Our findings also produced key insights about the whys and
wherefores of investors deciding to support innovative efforts.
Q-learning for Optimal Control of Continuous-time Systems
Authors: Biao Luo, Derong Liu, Ting-Wen Huang
Date: 2014-10-11 00:00:00
Tasks: Q-Learning, Reinforcement Learning

In this paper, two Q-learning (QL) methods are proposed and their convergence
theories are established for addressing the model-free optimal control problem
of general nonlinear continuous-time systems. By introducing the Q-function for
continuous-time systems, policy iteration based QL (PIQL) and value iteration
based QL (VIQL) algorithms are proposed for learning the optimal control policy
from real system data rather than using mathematical system model. It is proved
that both PIQL and VIQL methods generate a nonincreasing Q-function sequence,
which converges to the optimal Q-function. For implementation of the QL
algorithms, the method of weighted residuals is applied to derived the
parameters update rule. The developed PIQL and VIQL algorithms are essentially
off-policy reinforcement learning approachs, where the system data can be
collected arbitrary and thus the exploration ability is increased. With the
data collected from the real system, the QL methods learn the optimal control
policy offline, and then the convergent control policy will be employed to real
system. The effectiveness of the developed QL algorithms are verified through
computer simulation.
On model selection consistency of regularized M-estimators
Authors: Jason D. Lee, Yuekai Sun, Jonathan E. Taylor
Date: 2013-05-31 00:00:00
Tasks: model, Model Selection

Regularized M-estimators are used in diverse areas of science and engineering
to fit high-dimensional models with some low-dimensional structure. Usually the
low-dimensional structure is encoded by the presence of the (unknown)
parameters in some low-dimensional model subspace. In such settings, it is
desirable for estimates of the model parameters to be \emph{model selection
consistent}: the estimates also fall in the model subspace. We develop a
general framework for establishing consistency and model selection consistency
of regularized M-estimators and show how it applies to some special cases of
interest in statistical learning. Our analysis identifies two key properties of
regularized M-estimators, referred to as geometric decomposability and
irrepresentability, that ensure the estimators are consistent and model
selection consistent.
Use of Computer Vision to Detect Tangles in Tangled Objects
Authors: Paritosh Parmar
Date: 2014-05-19 00:00:00

Untangling of structures like ropes and wires by autonomous robots can be
useful in areas such as personal robotics, industries and electrical wiring &
repairing by robots. This problem can be tackled by using computer vision
system in robot. This paper proposes a computer vision based method for
analyzing visual data acquired from camera for perceiving the overlap of wires,
ropes, hoses i.e. detecting tangles. Information obtained after processing
image according to the proposed method comprises of position of tangles in
tangled object and which wire passes over which wire. This information can then
be used to guide robot to untangle wire/s. Given an image, preprocessing is
done to remove noise. Then edges of wire are detected. After that, the image is
divided into smaller blocks and each block is checked for wire overlap/s and
finding other relevant information. TANGLED-100 dataset was introduced, which
consists of images of tangled linear deformable objects. Method discussed in
here was tested on the TANGLED-100 dataset. Accuracy achieved during
experiments was found to be 74.9%. Robotic simulations were carried out to
demonstrate the use of the proposed method in applications of robot. Proposed
method is a general method that can be used by robots working in different
situations.
Do Deep Nets Really Need to be Deep?
Authors: Lei Jimmy Ba, Rich Caruana
Date: 2013-12-21 00:00:00
Proceeding: NeurIPS 2014 12
Tasks: Phoneme Recognition, speech-recognition, Speech Recognition

Currently, deep neural networks are the state of the art on problems such as
speech recognition and computer vision. In this extended abstract, we show that
shallow feed-forward networks can learn the complex functions previously
learned by deep nets and achieve accuracies previously only achievable with
deep models. Moreover, in some cases the shallow neural nets can learn these
deep functions using a total number of parameters similar to the original deep
model. We evaluate our method on the TIMIT phoneme recognition task and are
able to train shallow fully-connected nets that perform similarly to complex,
well-engineered, deep convolutional architectures. Our success in training
shallow neural nets to mimic deeper models suggests that there probably exist
better algorithms for training shallow feed-forward nets than those currently
available.
Riesz Logic
Authors: Daoud Clarke
Date: 2014-10-10 00:00:00

We introduce Riesz Logic, whose models are abelian lattice ordered groups,
which generalise Riesz spaces (vector lattices), and show soundness and
completeness. Our motivation is to provide a logic for distributional semantics
of natural language, where words are typically represented as elements of a
vector space whose dimensions correspond to contexts in which words may occur.
This basis provides a lattice ordering on the space, and this ordering may be
interpreted as "distributional entailment". Several axioms of Riesz Logic are
familiar from Basic Fuzzy Logic, and we show how the models of these two logics
may be related; Riesz Logic may thus be considered a new fuzzy logic. In
addition to applications in natural language processing, there is potential for
applying the theory to neuro-fuzzy systems.
Approximate False Positive Rate Control in Selection Frequency for Random Forest
Authors: Ender Konukoglu, Melanie Ganz
Date: 2014-10-10 00:00:00
Tasks: feature selection

Random Forest has become one of the most popular tools for feature selection.
Its ability to deal with high-dimensional data makes this algorithm especially
useful for studies in neuroimaging and bioinformatics. Despite its popularity
and wide use, feature selection in Random Forest still lacks a crucial
ingredient: false positive rate control. To date there is no efficient,
principled and computationally light-weight solution to this shortcoming. As a
result, researchers using Random Forest for feature selection have to resort to
using heuristically set thresholds on feature rankings. This article builds an
approximate probabilistic model for the feature selection process in random
forest training, which allows us to compute an estimated false positive rate
for a given threshold on selection frequency. Hence, it presents a principled
way to determine thresholds for the selection of relevant features without any
additional computational load. Experimental analysis with synthetic data
demonstrates that the proposed approach can limit false positive rates on the
order of the desired values and keep false negative rates low. Results show
that this holds even in the presence of a complex correlation structure between
features. Its good statistical properties and light-weight computational needs
make this approach widely applicable to feature selection for a wide-range of
applications.
On Backdoors To Tractable Constraint Languages
Authors: Clement Carbonnel, Martin C. Cooper, Emmanuel Hebrard
Date: 2014-04-14 00:00:00

In the context of CSPs, a strong backdoor is a subset of variables such that
every complete assignment yields a residual instance guaranteed to have a
specified property. If the property allows efficient solving, then a small
strong backdoor provides a reasonable decomposition of the original instance
into easy instances. An important challenge is the design of algorithms that
can find quickly a small strong backdoor if one exists. We present a systematic
study of the parameterized complexity of backdoor detection when the target
property is a restricted type of constraint language defined by means of a
family of polymorphisms. In particular, we show that under the weak assumption
that the polymorphisms are idempotent, the problem is unlikely to be FPT when
the parameter is either r (the constraint arity) or k (the size of the
backdoor) unless P = NP or FPT = W[2]. When the parameter is k+r, however, we
are able to identify large classes of languages for which the problem of
finding a small backdoor is FPT.
New SVD based initialization strategy for Non-negative Matrix Factorization
Authors: Hanli Qiao
Date: 2014-10-10 00:00:00
Tasks: Triplet

There are two problems need to be dealt with for Non-negative Matrix
Factorization (NMF): choose a suitable rank of the factorization and provide a
good initialization method for NMF algorithms. This paper aims to solve these
two problems using Singular Value Decomposition (SVD). At first we extract the
number of main components as the rank, actually this method is inspired from
[1, 2]. Second, we use the singular value and its vectors to initialize NMF
algorithm. In 2008, Boutsidis and Gollopoulos [3] provided the method titled
NNDSVD to enhance initialization of NMF algorithms. They extracted the positive
section and respective singular triplet information of the unit matrices
{C(j)}k j=1 which were obtained from singular vector pairs. This strategy aims
to use positive section to cope with negative elements of the singular vectors,
but in experiments we found that even replacing negative elements by their
absolute values could get better results than NNDSVD. Hence, we give another
method based SVD to fulfil initialization for NMF algorithms (SVD-NMF).
Numerical experiments on two face databases ORL and YALE [16, 17] show that our
method is better than NNDSVD.
A Survey on Heterogeneous Face Recognition: Sketch, Infra-red, 3D and Low-resolution
Authors: Shuxin Ouyang, Timothy Hospedales, Yi-Zhe Song, Xueming Li
Date: 2014-09-17 00:00:00
Tasks: Face Recognition, Heterogeneous Face Recognition

Heterogeneous face recognition (HFR) refers to matching face imagery across different domains. It has received much interest from the research community as a result of its profound implications in law enforcement. A wide variety of new invariant features, cross-modality matching models and heterogeneous datasets being established in recent years. This survey provides a comprehensive review of established techniques and recent developments in HFR. Moreover, we offer a detailed account of datasets and benchmarks commonly used for evaluation. We finish by assessing the state of the field and discussing promising directions for future research.
Lifted Variable Elimination for Probabilistic Logic Programming
Authors: Elena Bellodi, Evelina Lamma, Fabrizio Riguzzi, Vitor Santos Costa, Riccardo Zese
Date: 2014-05-13 00:00:00

Lifted inference has been proposed for various probabilistic logical
frameworks in order to compute the probability of queries in a time that
depends on the size of the domains of the random variables rather than the
number of instances. Even if various authors have underlined its importance for
probabilistic logic programming (PLP), lifted inference has been applied up to
now only to relational languages outside of logic programming. In this paper we
adapt Generalized Counting First Order Variable Elimination (GC-FOVE) to the
problem of computing the probability of queries to probabilistic logic programs
under the distribution semantics. In particular, we extend the Prolog Factor
Language (PFL) to include two new types of factors that are needed for
representing ProbLog programs. These factors take into account the existing
causal independence relationships among random variables and are managed by the
extension to variable elimination proposed by Zhang and Poole for dealing with
convergent variables and heterogeneous factors. Two new operators are added to
GC-FOVE for treating heterogeneous factors. The resulting algorithm, called
LP$^2$ for Lifted Probabilistic Logic Programming, has been implemented by
modifying the PFL implementation of GC-FOVE and tested on three benchmarks for
lifted inference. A comparison with PITA and ProbLog2 shows the potential of
the approach.
Compressed Sensing With Side Information: Geometrical Interpretation and Performance Bounds
Authors: João F. C. Mota, Nikos Deligiannis, Miguel R. D. Rodrigues
Date: 2014-10-10 00:00:00
Tasks: compressed sensing

We address the problem of Compressed Sensing (CS) with side information.
Namely, when reconstructing a target CS signal, we assume access to a similar
signal. This additional knowledge, the side information, is integrated into CS
via L1-L1 and L1-L2 minimization. We then provide lower bounds on the number of
measurements that these problems require for successful reconstruction of the
target signal. If the side information has good quality, the number of
measurements is significantly reduced via L1-L1 minimization, but not so much
via L1-L2 minimization. We provide geometrical interpretations and experimental
results illustrating our findings.
Challenge IEEE-ISBI/TCB : Application of Covariance matrices and wavelet marginals
Authors: Florian Yger
Date: 2014-10-10 00:00:00

This short memo aims at explaining our approach for the challenge IEEE-ISBI
on Bone Texture Characterization. In this work, we focus on the use of
covariance matrices and wavelet marginals in an SVM classifier.
Risk Dynamics in Trade Networks
Authors: Rafael M. Frongillo, Mark D. Reid
Date: 2014-10-01 00:00:00

We introduce a new framework to model interactions among agents which seek to
trade to minimize their risk with respect to some future outcome. We quantify
this risk using the concept of risk measures from finance, and introduce a
class of trade dynamics which allow agents to trade contracts contingent upon
the future outcome. We then show that these trade dynamics exactly correspond
to a variant of randomized coordinate descent. By extending the analysis of
these coordinate descent methods to account for our more organic setting, we
are able to show convergence rates for very general trade dynamics, showing
that the market or network converges to a unique steady state. Applying these
results to prediction markets, we expand on recent results by adding
convergence rates and general aggregation properties. Finally, we illustrate
the generality of our framework by applying it to agent interactions on a
scale-free network.
Contrastive Unsupervised Word Alignment with Non-Local Features
Authors: Yang Liu, Maosong Sun
Date: 2014-10-08 00:00:00
Tasks: Word Alignment

Word alignment is an important natural language processing task that
indicates the correspondence between natural languages. Recently, unsupervised
learning of log-linear models for word alignment has received considerable
attention as it combines the merits of generative and discriminative
approaches. However, a major challenge still remains: it is intractable to
calculate the expectations of non-local features that are critical for
capturing the divergence between natural languages. We propose a contrastive
approach that aims to differentiate observed training examples from noises. It
not only introduces prior knowledge to guide unsupervised learning but also
cancels out partition functions. Based on the observation that the probability
mass of log-linear models for word alignment is usually highly concentrated, we
propose to use top-n alignments to approximate the expectations with respect to
posterior distributions. This allows for efficient and accurate calculation of
expectations of non-local features. Experiments show that our approach achieves
significant improvements over state-of-the-art unsupervised word alignment
methods.
Distributed Estimation, Information Loss and Exponential Families
Authors: Qiang Liu, Alexander Ihler
Date: 2014-10-09 00:00:00
Proceeding: NeurIPS 2014 12

Distributed learning of probabilistic models from multiple data repositories
with minimum communication is increasingly important. We study a simple
communication-efficient learning framework that first calculates the local
maximum likelihood estimates (MLE) based on the data subsets, and then combines
the local MLEs to achieve the best possible approximation to the global MLE
given the whole dataset. We study this framework's statistical properties,
showing that the efficiency loss compared to the global setting relates to how
much the underlying distribution families deviate from full exponential
families, drawing connection to the theory of information loss by Fisher, Rao
and Efron. We show that the "full-exponential-family-ness" represents the lower
bound of the error rate of arbitrary combinations of local MLEs, and is
achieved by a KL-divergence-based combination method but not by a more common
linear combination method. We also study the empirical properties of both
methods, showing that the KL method significantly outperforms linear
combination in practical settings with issues such as model misspecification,
non-convexity, and heterogeneous data partitions.
Hybrid approaches for automatic vowelization of Arabic texts
Authors: Mohamed Bebah, Chennoufi Amine, Mazroui Azzeddine, Lakhouaja Abdelhak
Date: 2014-10-09 00:00:00
Tasks: Morphological Analysis

Hybrid approaches for automatic vowelization of Arabic texts are presented in
this article. The process is made up of two modules. In the first one, a
morphological analysis of the text words is performed using the open source
morphological Analyzer AlKhalil Morpho Sys. Outputs for each word analyzed out
of context, are its different possible vowelizations. The integration of this
Analyzer in our vowelization system required the addition of a lexical database
containing the most frequent words in Arabic language. Using a statistical
approach based on two hidden Markov models (HMM), the second module aims to
eliminate the ambiguities. Indeed, for the first HMM, the unvowelized Arabic
words are the observed states and the vowelized words are the hidden states.
The observed states of the second HMM are identical to those of the first, but
the hidden states are the lists of possible diacritics of the word without its
Arabic letters. Our system uses Viterbi algorithm to select the optimal path
among the solutions proposed by Al Khalil Morpho Sys. Our approach opens an
important way to improve the performance of automatic vowelization of Arabic
texts for other uses in automatic natural language processing.
Matrix Completion and Low-Rank SVD via Fast Alternating Least Squares
Authors: Trevor Hastie, Rahul Mazumder, Jason Lee, Reza Zadeh
Date: 2014-10-09 00:00:00
Tasks: Matrix Completion

The matrix-completion problem has attracted a lot of attention, largely as a
result of the celebrated Netflix competition. Two popular approaches for
solving the problem are nuclear-norm-regularized matrix approximation (Candes
and Tao, 2009, Mazumder, Hastie and Tibshirani, 2010), and maximum-margin
matrix factorization (Srebro, Rennie and Jaakkola, 2005). These two procedures
are in some cases solving equivalent problems, but with quite different
algorithms. In this article we bring the two approaches together, leading to an
efficient algorithm for large matrix factorization and completion that
outperforms both of these. We develop a software package "softImpute" in R for
implementing our approaches, and a distributed version for very large matrices
using the "Spark" cluster programming environment.
A unified approach for multi-object triangulation, tracking and camera calibration
Authors: Jeremie Houssineau, Daniel Clark, Spela Ivekovic, Chee Sing Lee, Jose Franco
Date: 2014-10-09 00:00:00
Tasks: Camera Calibration, Multi-Object Tracking, Object, Object Tracking, State Estimation

Object triangulation, 3-D object tracking, feature correspondence, and camera
calibration are key problems for estimation from camera networks. This paper
addresses these problems within a unified Bayesian framework for joint
multi-object tracking and sensor registration. Given that using standard
filtering approaches for state estimation from cameras is problematic, an
alternative parametrisation is exploited, called disparity space. The disparity
space-based approach for triangulation and object tracking is shown to be more
effective than non-linear versions of the Kalman filter and particle filtering
for non-rectified cameras. The approach for feature correspondence is based on
the Probability Hypothesis Density (PHD) filter, and hence inherits the ability
to update without explicit measurement association, to initiate new targets,
and to discriminate between target and clutter. The PHD filtering approach then
forms the basis of a camera calibration method from static or moving objects.
Results are shown on simulated data.
Genetic Stereo Matching Algorithm with Fuzzy Fitness
Authors: Haythem Ghazouani
Date: 2014-10-09 00:00:00
Tasks: Stereo Matching, Stereo Matching Hand

This paper presents a genetic stereo matching algorithm with fuzzy evaluation
function. The proposed algorithm presents a new encoding scheme in which a
chromosome is represented by a disparity matrix. Evolution is controlled by a
fuzzy fitness function able to deal with noise and uncertain camera
measurements, and uses classical evolutionary operators. The result of the
algorithm is accurate dense disparity maps obtained in a reasonable
computational time suitable for real-time applications as shown in experimental
results.
Learning Invariant Color Features for Person Re-Identification
Authors: Rahul Rama Varior, Gang Wang, Jiwen Lu
Date: 2014-10-04 00:00:00
Tasks: Person Re-Identification

Matching people across multiple camera views known as person
re-identification, is a challenging problem due to the change in visual
appearance caused by varying lighting conditions. The perceived color of the
subject appears to be different with respect to illumination. Previous works
use color as it is or address these challenges by designing color spaces
focusing on a specific cue. In this paper, we propose a data driven approach
for learning color patterns from pixels sampled from images across two camera
views. The intuition behind this work is that, even though pixel values of same
color would be different across views, they should be encoded with the same
values. We model color feature generation as a learning problem by jointly
learning a linear transformation and a dictionary to encode pixel values. We
also analyze different photometric invariant color spaces. Using color as the
only cue, we compare our approach with all the photometric invariant color
spaces and show superior performance over all of them. Combining with other
learned low-level and high-level features, we obtain promising results in
ViPER, Person Re-ID 2011 and CAVIAR4REID datasets.