pwc-archive/papers-with-abstracts huggingface dataset:
Coherent Motion Segmentation in Moving Camera Videos using Optical Flow Orientations
Authors: Manjunath Narayana, Allen Hanson, Erik Learned-Miller
Date: 2015-11-05 00:00:00
Tasks: Motion Segmentation, Optical Flow Estimation, Segmentation

In moving camera videos, motion segmentation is commonly performed using the
image plane motion of pixels, or optical flow. However, objects that are at
different depths from the camera can exhibit different optical flows even if
they share the same real-world motion. This can cause a depth-dependent
segmentation of the scene. Our goal is to develop a segmentation algorithm that
clusters pixels that have similar real-world motion irrespective of their depth
in the scene. Our solution uses optical flow orientations instead of the
complete vectors and exploits the well-known property that under camera
translation, optical flow orientations are independent of object depth. We
introduce a probabilistic model that automatically estimates the number of
observed independent motions and results in a labeling that is consistent with
real-world motion in the scene. The result of our system is that static objects
are correctly identified as one segment, even if they are at different depths.
Color features and information from previous frames in the video sequence are
used to correct occasional errors due to the orientation-based segmentation. We
present results on more than thirty videos from different benchmarks. The
system is particularly robust on complex background scenes containing objects
at significantly different depths
Color Aesthetics and Social Networks in Complete Tang Poems: Explorations and Discoveries
Authors: Chao-Lin Liu, Hongsu Wang, Wen-Huei Cheng, Chu-Ting Hsu, Wei-Yun Chiu
Date: 2015-11-05 00:00:00
Proceeding: PACLIC 2015 10

The Complete Tang Poems (CTP) is the most important source to study Tang
poems. We look into CTP with computational tools from specific linguistic
perspectives, including distributional semantics and collocational analysis.
From such quantitative viewpoints, we compare the usage of "wind" and "moon" in
the poems of Li Bai and Du Fu. Colors in poems function like sounds in movies,
and play a crucial role in the imageries of poems. Thus, words for colors are
studied, and "white" is the main focus because it is the most frequent color in
CTP. We also explore some cases of using colored words in antithesis pairs that
were central for fostering the imageries of the poems. CTP also contains useful
historical information, and we extract person names in CTP to study the social
networks of the Tang poets. Such information can then be integrated with the
China Biographical Database of Harvard University.
Ripple Down Rules for Question Answering
Authors: Dat Quoc Nguyen, Dai Quoc Nguyen, Son Bao Pham
Date: 2014-12-12 00:00:00
Tasks: Question Answering

Recent years have witnessed a new trend of building ontology-based question
answering systems. These systems use semantic web information to produce more
precise answers to users' queries. However, these systems are mostly designed
for English. In this paper, we introduce an ontology-based question answering
system named KbQAS which, to the best of our knowledge, is the first one made
for Vietnamese. KbQAS employs our question analysis approach that
systematically constructs a knowledge base of grammar rules to convert each
input question into an intermediate representation element. KbQAS then takes
the intermediate representation element with respect to a target ontology and
applies concept-matching techniques to return an answer. On a wide range of
Vietnamese questions, experimental results show that the performance of KbQAS
is promising with accuracies of 84.1% and 82.4% for analyzing input questions
and retrieving output answers, respectively. Furthermore, our question analysis
approach can easily be applied to new domains and new languages, thus saving
time and human effort.
Mining Local Gazetteers of Literary Chinese with CRF and Pattern based Methods for Biographical Information in Chinese History
Authors: Chao-Lin Liu, Chih-Kai Huang, Hongsu Wang, Peter K. Bol
Date: 2015-11-04 00:00:00
Tasks: Language Modeling, Language Modelling

Person names and location names are essential building blocks for identifying
events and social networks in historical documents that were written in
literary Chinese. We take the lead to explore the research on algorithmically
recognizing named entities in literary Chinese for historical studies with
language-model based and conditional-random-field based methods, and extend our
work to mining the document structures in historical documents. Practical
evaluations were conducted with texts that were extracted from more than 220
volumes of local gazetteers (Difangzhi). Difangzhi is a huge and the single
most important collection that contains information about officers who served
in local government in Chinese history. Our methods performed very well on
these realistic tests. Thousands of names and addresses were identified from
the texts. A good portion of the extracted names match the biographical
information currently recorded in the China Biographical Database (CBDB) of
Harvard University, and many others can be verified by historians and will
become as new additions to CBDB.
Optimal Rates for Random Fourier Features
Authors: Bharath K. Sriperumbudur, Zoltan Szabo
Date: 2015-06-06 00:00:00
Proceeding: NeurIPS 2015 12

Kernel methods represent one of the most powerful tools in machine learning
to tackle problems expressed in terms of function values and derivatives due to
their capability to represent and model complex relations. While these methods
show good versatility, they are computationally intensive and have poor
scalability to large data as they require operations on Gram matrices. In order
to mitigate this serious computational limitation, recently randomized
constructions have been proposed in the literature, which allow the application
of fast linear algorithms. Random Fourier features (RFF) are among the most
popular and widely applied constructions: they provide an easily computable,
low-dimensional feature representation for shift-invariant kernels. Despite the
popularity of RFFs, very little is understood theoretically about their
approximation quality. In this paper, we provide a detailed finite-sample
theoretical analysis about the approximation quality of RFFs by (i)
establishing optimal (in terms of the RFF dimension, and growing set size)
performance guarantees in uniform norm, and (ii) presenting guarantees in $L^r$
($1\le r<\infty$) norms. We also propose an RFF approximation to derivatives of
a kernel with a theoretical study on its approximation quality.
Regularization and Bayesian Learning in Dynamical Systems: Past, Present and Future
Authors: A. Chiuso
Date: 2015-11-04 00:00:00
Tasks: Econometrics

Regularization and Bayesian methods for system identification have been
repopularized in the recent years, and proved to be competitive w.r.t.
classical parametric approaches. In this paper we shall make an attempt to
illustrate how the use of regularization in system identification has evolved
over the years, starting from the early contributions both in the Automatic
Control as well as Econometrics and Statistics literature. In particular we
shall discuss some fundamental issues such as compound estimation problems and
exchangeability which play and important role in regularization and Bayesian
approaches, as also illustrated in early publications in Statistics. The
historical and foundational issues will be given more emphasis (and space), at
the expense of the more recent developments which are only briefly discussed.
The main reason for such a choice is that, while the recent literature is
readily available, and surveys have already been published on the subject, in
the author's opinion a clear link with past work had not been completely
clarified.
Mean-field inference of Hawkes point processes
Authors: Emmanuel Bacry, Stéphane Gaïffas, Iacopo Mastromatteo, Jean-François Muzy
Date: 2015-11-04 00:00:00
Tasks: Point Processes, valid

We propose a fast and efficient estimation method that is able to accurately
recover the parameters of a d-dimensional Hawkes point-process from a set of
observations. We exploit a mean-field approximation that is valid when the
fluctuations of the stochastic intensity are small. We show that this is
notably the case in situations when interactions are sufficiently weak, when
the dimension of the system is high or when the fluctuations are self-averaging
due to the large number of past events they involve. In such a regime the
estimation of a Hawkes process can be mapped on a least-squares problem for
which we provide an analytic solution. Though this estimator is biased, we show
that its precision can be comparable to the one of the Maximum Likelihood
Estimator while its computation speed is shown to be improved considerably. We
give a theoretical control on the accuracy of our new approach and illustrate
its efficiency using synthetic datasets, in order to assess the statistical
estimation error of the parameters.
Enhancing Feature Tracking With Gyro Regularization
Authors: Bryan Poling, Gilad Lerman
Date: 2015-11-04 00:00:00
Tasks: Optical Flow Estimation

We present a deeply integrated method of exploiting low-cost gyroscopes to
improve general purpose feature tracking. Most previous methods use gyroscopes
to initialize and bound the search for features. In contrast, we use them to
regularize the tracking energy function so that they can directly assist in the
tracking of ambiguous and poor-quality features. We demonstrate that our simple
technique offers significant improvements in performance over conventional
template-based tracking methods, and is in fact competitive with more complex
and computationally expensive state-of-the-art trackers, but at a fraction of
the computational cost. Additionally, we show that the practice of initializing
template-based feature trackers like KLT (Kanade-Lucas-Tomasi) using
gyro-predicted optical flow offers no advantage over using a careful
optical-only initialization method, suggesting that some deeper level of
integration, like the method we propose, is needed in order to realize a
genuine improvement in tracking performance from these inertial sensors.
High-Dimensional Asymptotics of Prediction: Ridge Regression and Classification
Authors: Edgar Dobriban, Stefan Wager
Date: 2015-07-10 00:00:00
Tasks: Classification, General Classification, regression, Vocal Bursts Intensity Prediction

We provide a unified analysis of the predictive risk of ridge regression and
regularized discriminant analysis in a dense random effects model. We work in a
high-dimensional asymptotic regime where $p, n \to \infty$ and $p/n \to \gamma
\in (0, \, \infty)$, and allow for arbitrary covariance among the features. For
both methods, we provide an explicit and efficiently computable expression for
the limiting predictive risk, which depends only on the spectrum of the
feature-covariance matrix, the signal strength, and the aspect ratio $\gamma$.
Especially in the case of regularized discriminant analysis, we find that
predictive accuracy has a nuanced dependence on the eigenvalue distribution of
the covariance matrix, suggesting that analyses based on the operator norm of
the covariance matrix may not be sharp. Our results also uncover several
qualitative insights about both methods: for example, with ridge regression,
there is an exact inverse relation between the limiting predictive risk and the
limiting estimation risk given a fixed signal strength. Our analysis builds on
recent advances in random matrix theory.
Approximation of the truncated Zeta distribution and Zipf's law
Authors: Maurizio Naldi
Date: 2015-11-04 00:00:00
Tasks: Form

Zipf's law appears in many application areas but does not have a closed form
expression, which may make its use cumbersome. Since it coincides with the
truncated version of the Zeta distribution, in this paper we propose three
approximate closed form expressions for the truncated Zeta distribution, which
may be employed for Zipf's law as well. The three approximations are based on
the replacement of the sum occurring in Zipf's law with an integral, and are
named respectively the integral approximation, the average integral
approximation, and the trapezoidal approximation. While the first one is shown
to be of little use, the trapezoidal approximation exhibits an error which is
typically lower than 1\%, but is as low as 0.1\% for the range of values of the
Zipf parameter below 1.
Semi-supervised Sequence Learning
Authors: Andrew M. Dai, Quoc V. Le
Date: 2015-11-04 00:00:00
Proceeding: NeurIPS 2015 12
Tasks: Language Modeling, Language Modelling, Text Classification

We present two approaches that use unlabeled data to improve sequence
learning with recurrent networks. The first approach is to predict what comes
next in a sequence, which is a conventional language model in natural language
processing. The second approach is to use a sequence autoencoder, which reads
the input sequence into a vector and predicts the input sequence again. These
two algorithms can be used as a "pretraining" step for a later supervised
sequence learning algorithm. In other words, the parameters obtained from the
unsupervised step can be used as a starting point for other supervised training
models. In our experiments, we find that long short term memory recurrent
networks after being pretrained with the two approaches are more stable and
generalize better. With pretraining, we are able to train long short term
memory recurrent networks up to a few hundred timesteps, thereby achieving
strong performance in many text classification tasks, such as IMDB, DBpedia and
20 Newsgroups.
Turing Computation with Recurrent Artificial Neural Networks
Authors: Giovanni S Carmantini, Peter beim Graben, Mathieu Desroches, Serafim Rodrigues
Date: 2015-11-04 00:00:00

We improve the results by Siegelmann & Sontag (1995) by providing a novel and
parsimonious constructive mapping between Turing Machines and Recurrent
Artificial Neural Networks, based on recent developments of Nonlinear Dynamical
Automata. The architecture of the resulting R-ANNs is simple and elegant,
stemming from its transparent relation with the underlying NDAs. These
characteristics yield promise for developments in machine learning methods and
symbolic computation with continuous time dynamical systems. A framework is
provided to directly program the R-ANNs from Turing Machine descriptions, in
absence of network training. At the same time, the network can potentially be
trained to perform algorithmic tasks, with exciting possibilities in the
integration of approaches akin to Google DeepMind's Neural Turing Machines.
Consistent Parameter Estimation for LASSO and Approximate Message Passing
Authors: Ali Mousavi, Arian Maleki, Richard G. Baraniuk
Date: 2015-11-03 00:00:00
Tasks: parameter estimation

We consider the problem of recovering a vector $\beta_o \in \mathbb{R}^p$
from $n$ random and noisy linear observations $y= X\beta_o + w$, where $X$ is
the measurement matrix and $w$ is noise. The LASSO estimate is given by the
solution to the optimization problem $\hat{\beta}_{\lambda} = \arg \min_{\beta}
\frac{1}{2} \|y-X\beta\|_2^2 + \lambda \| \beta \|_1$. Among the iterative
algorithms that have been proposed for solving this optimization problem,
approximate message passing (AMP) has attracted attention for its fast
convergence. Despite significant progress in the theoretical analysis of the
estimates of LASSO and AMP, little is known about their behavior as a function
of the regularization parameter $\lambda$, or the thereshold parameters
$\tau^t$. For instance the following basic questions have not yet been studied
in the literature: (i) How does the size of the active set
$\|\hat{\beta}^\lambda\|_0/p$ behave as a function of $\lambda$? (ii) How does
the mean square error $\|\hat{\beta}_{\lambda} - \beta_o\|_2^2/p$ behave as a
function of $\lambda$? (iii) How does $\|\beta^t - \beta_o \|_2^2/p$ behave as
a function of $\tau^1, \ldots, \tau^{t-1}$? Answering these questions will help
in addressing practical challenges regarding the optimal tuning of $\lambda$ or
$\tau^1, \tau^2, \ldots$. This paper answers these questions in the asymptotic
setting and shows how these results can be employed in deriving simple and
theoretically optimal approaches for tuning the parameters $\tau^1, \ldots,
\tau^t$ for AMP or $\lambda$ for LASSO. It also explores the connection between
the optimal tuning of the parameters of AMP and the optimal tuning of LASSO.
Supervised Learning for Dynamical System Learning
Authors: Ahmed Hefny, Carlton Downey, Geoffrey Gordon
Date: 2015-05-20 00:00:00
Proceeding: NeurIPS 2015 12
Tasks: regression

Recently there has been substantial interest in spectral methods for learning
dynamical systems. These methods are popular since they often offer a good
tradeoff between computational and statistical efficiency. Unfortunately, they
can be difficult to use and extend in practice: e.g., they can make it
difficult to incorporate prior information such as sparsity or structure. To
address this problem, we present a new view of dynamical system learning: we
show how to learn dynamical systems by solving a sequence of ordinary
supervised learning problems, thereby allowing users to incorporate prior
knowledge via standard techniques such as L1 regularization. Many existing
spectral methods are special cases of this new framework, using linear
regression as the supervised learner. We demonstrate the effectiveness of our
framework by showing examples where nonlinear regression or lasso let us learn
better state representations than plain linear regression does; the correctness
of these instances follows directly from our general analysis.
Dictionary descent in optimization
Authors: Vladimir Temlyakov
Date: 2015-11-04 00:00:00

The problem of convex optimization is studied. Usually in convex optimization
the minimization is over a d-dimensional domain. Very often the convergence
rate of an optimization algorithm depends on the dimension d. The algorithms
studied in this paper utilize dictionaries instead of a canonical basis used in
the coordinate descent algorithms. We show how this approach allows us to
reduce dimensionality of the problem. Also, we investigate which properties of
a dictionary are beneficial for the convergence rate of typical greedy-type
algorithms.
A proposal project for a blind image quality assessment by learning distortions from the full reference image quality assessments
Authors: Stéfane Paris
Date: 2015-11-04 00:00:00
Tasks: Blind Image Quality Assessment, Image Quality Assessment

This short paper presents a perspective plan to build a null reference image
quality assessment. Its main goal is to deliver both the objective score and
the distortion map for a given distorted image without the knowledge of its
reference image.
Towards a tracking algorithm based on the clustering of spatio-temporal clouds of points
Authors: Andrea Cavagna, Chiara Creato, Lorenzo Del Castello, Stefania Melillo, Leonardo Parisi, Massimiliano Viale
Date: 2015-11-04 00:00:00
Tasks: Clustering

The interest in 3D dynamical tracking is growing in fields such as robotics,
biology and fluid dynamics. Recently, a major source of progress in 3D tracking
has been the study of collective behaviour in biological systems, where the
trajectories of individual animals moving within large and dense groups need to
be reconstructed to understand the behavioural interaction rules. Experimental
data in this field are generally noisy and at low spatial resolution, so that
individuals appear as small featureless objects and trajectories must be
retrieved by making use of epipolar information only. Moreover, optical
occlusions often occur: in a multi-camera system one or more objects become
indistinguishable in one view, potentially jeopardizing the conservation of
identity over long-time trajectories. The most advanced 3D tracking algorithms
overcome optical occlusions making use of set-cover techniques, which however
have to solve NP-hard optimization problems. Moreover, current methods are not
able to cope with occlusions arising from actual physical proximity of objects
in 3D space. Here, we present a new method designed to work directly in 3D
space and time, creating (3D+1) clouds of points representing the full
spatio-temporal evolution of the moving targets. We can then use a simple
connected components labeling routine, which is linear in time, to solve
optical occlusions, hence lowering from NP to P the complexity of the problem.
Finally, we use normalized cut spectral clustering to tackle 3D physical
proximity.
Lasso based feature selection for malaria risk exposure prediction
Authors: Bienvenue Kouwayè, Noël Fonton, Fabrice Rossi
Date: 2015-11-04 00:00:00
Tasks: feature selection, Malaria Risk Exposure Prediction, Prediction

In life sciences, the experts generally use empirical knowledge to recode
variables, choose interactions and perform selection by classical approach. The
aim of this work is to perform automatic learning algorithm for variables
selection which can lead to know if experts can be help in they decision or
simply replaced by the machine and improve they knowledge and results. The
Lasso method can detect the optimal subset of variables for estimation and
prediction under some conditions. In this paper, we propose a novel approach
which uses automatically all variables available and all interactions. By a
double cross-validation combine with Lasso, we select a best subset of
variables and with GLM through a simple cross-validation perform predictions.
The algorithm assures the stability and the the consistency of estimators.
Factorizing LambdaMART for cold start recommendations
Authors: Phong Nguyen, Jun Wang, Alexandros Kalousis
Date: 2015-11-04 00:00:00
Tasks: Learning-To-Rank, Matrix Completion, Recommendation Systems

Recommendation systems often rely on point-wise loss metrics such as the mean
squared error. However, in real recommendation settings only few items are
presented to a user. This observation has recently encouraged the use of
rank-based metrics. LambdaMART is the state-of-the-art algorithm in learning to
rank which relies on such a metric. Despite its success it does not have a
principled regularization mechanism relying in empirical approaches to control
model complexity leaving it thus prone to overfitting.
  Motivated by the fact that very often the users' and items' descriptions as
well as the preference behavior can be well summarized by a small number of
hidden factors, we propose a novel algorithm, LambdaMART Matrix Factorization
(LambdaMART-MF), that learns a low rank latent representation of users and
items using gradient boosted trees. The algorithm factorizes lambdaMART by
defining relevance scores as the inner product of the learned representations
of the users and items. The low rank is essentially a model complexity
controller; on top of it we propose additional regularizers to constraint the
learned latent representations that reflect the user and item manifolds as
these are defined by their original feature based descriptors and the
preference behavior. Finally we also propose to use a weighted variant of NDCG
to reduce the penalty for similar items with large rating discrepancy.
  We experiment on two very different recommendation datasets, meta-mining and
movies-users, and evaluate the performance of LambdaMART-MF, with and without
regularization, in the cold start setting as well as in the simpler matrix
completion setting. In both cases it outperforms in a significant manner
current state of the art algorithms.