pwc-archive/papers-with-abstracts huggingface dataset:
Linking Image and Text with 2-Way Nets
Authors: Aviv Eisenschtat, Lior Wolf
Date: 2016-08-29 00:00:00
Proceeding: CVPR 2017 7
Tasks: Sentence

Linking two data sources is a basic building block in numerous computer
vision problems. Canonical Correlation Analysis (CCA) achieves this by
utilizing a linear optimizer in order to maximize the correlation between the
two views. Recent work makes use of non-linear models, including deep learning
techniques, that optimize the CCA loss in some feature space. In this paper, we
introduce a novel, bi-directional neural network architecture for the task of
matching vectors from two data sources. Our approach employs two tied neural
network channels that project the two views into a common, maximally correlated
space using the Euclidean loss. We show a direct link between the
correlation-based loss and Euclidean loss, enabling the use of Euclidean loss
for correlation maximization. To overcome common Euclidean regression
optimization problems, we modify well-known techniques to our problem,
including batch normalization and dropout. We show state of the art results on
a number of computer vision matching tasks including MNIST image matching and
sentence-image matching on the Flickr8k, Flickr30k and COCO datasets.
Generative Mixture of Networks
Authors: Ershad Banijamali, Ali Ghodsi, Pascal Poupart
Date: 2017-02-10 00:00:00
Tasks: Clustering

A generative model based on training deep architectures is proposed. The
model consists of K networks that are trained together to learn the underlying
distribution of a given data set. The process starts with dividing the input
data into K clusters and feeding each of them into a separate network. After
few iterations of training networks separately, we use an EM-like algorithm to
train the networks together and update the clusters of the data. We call this
model Mixture of Networks. The provided model is a platform that can be used
for any deep structure and be trained by any conventional objective function
for distribution modeling. As the components of the model are neural networks,
it has high capability in characterizing complicated data distributions as well
as clustering data. We apply the algorithm on MNIST hand-written digits and
Yale face datasets. We also demonstrate the clustering ability of the model
using some real-world and toy examples.
Universal Dependencies to Logical Forms with Negation Scope
Authors: Federico Fancellu, Siva Reddy, Adam Lopez, Bonnie Webber
Date: 2017-02-10 00:00:00
Tasks: Negation

Many language technology applications would benefit from the ability to
represent negation and its scope on top of widely-used linguistic resources. In
this paper, we investigate the possibility of obtaining a first-order logic
representation with negation scope marked using Universal Dependencies. To do
so, we enhance UDepLambda, a framework that converts dependency graphs to
logical forms. The resulting UDepLambda$\lnot$ is able to handle phenomena
related to scope by means of an higher-order type theory, relevant not only to
negation but also to universal quantification and other complex semantic
phenomena. The initial conversion we did for English is promising, in that one
can represent the scope of negation also in the presence of more complex
phenomena such as universal quantifiers.
Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy
Authors: Danica J. Sutherland, Hsiao-Yu Tung, Heiko Strathmann, Soumyajit De, Aaditya Ramdas, Alex Smola, Arthur Gretton
Date: 2016-11-14 00:00:00

We propose a method to optimize the representation and distinguishability of samples from two probability distributions, by maximizing the estimated power of a statistical test based on the maximum mean discrepancy (MMD). This optimized MMD is applied to the setting of unsupervised learning by generative adversarial networks (GAN), in which a model attempts to generate realistic samples, and a discriminator attempts to tell these apart from data samples. In this context, the MMD may be used in two roles: first, as a discriminator, either directly on the samples, or on features of the samples. Second, the MMD can be used to evaluate the performance of a generative model, by testing the model's samples against a reference data set. In the latter role, the optimized MMD is particularly helpful, as it gives an interpretable indication of how the model and data distributions differ, even in cases where individual model samples are not easily distinguished either by eye or by classifier.
Dual-Tree Wavelet Scattering Network with Parametric Log Transformation for Object Classification
Authors: Amarjot Singh, Nick Kingsbury
Date: 2017-02-10 00:00:00
Tasks: Computational Efficiency, General Classification, Translation

We introduce a ScatterNet that uses a parametric log transformation with
Dual-Tree complex wavelets to extract translation invariant representations
from a multi-resolution image. The parametric transformation aids the OLS
pruning algorithm by converting the skewed distributions into relatively
mean-symmetric distributions while the Dual-Tree wavelets improve the
computational efficiency of the network. The proposed network is shown to
outperform Mallat's ScatterNet on two image datasets, both for classification
accuracy and computational efficiency. The advantages of the proposed network
over other supervised and some unsupervised methods are also presented using
experiments performed on different training dataset sizes.
Sparse Convex Clustering
Authors: Binhuan Wang, Yilong Zhang, Will Wei Sun, Yixin Fang
Date: 2016-01-18 00:00:00
Tasks: Clustering, feature selection

Convex clustering, a convex relaxation of k-means clustering and hierarchical
clustering, has drawn recent attentions since it nicely addresses the
instability issue of traditional nonconvex clustering methods. Although its
computational and statistical properties have been recently studied, the
performance of convex clustering has not yet been investigated in the
high-dimensional clustering scenario, where the data contains a large number of
features and many of them carry no information about the clustering structure.
In this paper, we demonstrate that the performance of convex clustering could
be distorted when the uninformative features are included in the clustering. To
overcome it, we introduce a new clustering method, referred to as Sparse Convex
Clustering, to simultaneously cluster observations and conduct feature
selection. The key idea is to formulate convex clustering in a form of
regularization, with an adaptive group-lasso penalty term on cluster centers.
In order to optimally balance the tradeoff between the cluster fitting and
sparsity, a tuning criterion based on clustering stability is developed. In
theory, we provide an unbiased estimator for the degrees of freedom of the
proposed sparse convex clustering method. Finally, the effectiveness of the
sparse convex clustering is examined through a variety of numerical experiments
and a real data application.
$L_2$Boosting for Economic Applications
Authors: Ye Luo, Martin Spindler
Date: 2017-02-10 00:00:00
Tasks: Attribute

In the recent years more and more high-dimensional data sets, where the
number of parameters $p$ is high compared to the number of observations $n$ or
even larger, are available for applied researchers. Boosting algorithms
represent one of the major advances in machine learning and statistics in
recent years and are suitable for the analysis of such data sets. While Lasso
has been applied very successfully for high-dimensional data sets in Economics,
boosting has been underutilized in this field, although it has been proven very
powerful in fields like Biostatistics and Pattern Recognition. We attribute
this to missing theoretical results for boosting. The goal of this paper is to
fill this gap and show that boosting is a competitive method for inference of a
treatment effect or instrumental variable (IV) estimation in a high-dimensional
setting. First, we present the $L_2$Boosting with componentwise least squares
algorithm and variants which are tailored for regression problems which are the
workhorse for most Econometric problems. Then we show how $L_2$Boosting can be
used for estimation of treatment effects and IV estimation. We highlight the
methods and illustrate them with simulations and empirical examples. For
further results and technical details we refer to Luo and Spindler (2016, 2017)
and to the online supplement of the paper.
Learning what matters - Sampling interesting patterns
Authors: Vladimir Dzyuba, Matthijs van Leeuwen
Date: 2017-02-07 00:00:00
Tasks: Diversity, Learning-To-Rank

In the field of exploratory data mining, local structure in data can be
described by patterns and discovered by mining algorithms. Although many
solutions have been proposed to address the redundancy problems in pattern
mining, most of them either provide succinct pattern sets or take the interests
of the user into account-but not both. Consequently, the analyst has to invest
substantial effort in identifying those patterns that are relevant to her
specific interests and goals. To address this problem, we propose a novel
approach that combines pattern sampling with interactive data mining. In
particular, we introduce the LetSIP algorithm, which builds upon recent
advances in 1) weighted sampling in SAT and 2) learning to rank in interactive
pattern mining. Specifically, it exploits user feedback to directly learn the
parameters of the sampling distribution that represents the user's interests.
We compare the performance of the proposed algorithm to the state-of-the-art in
interactive pattern mining by emulating the interests of a user. The resulting
system allows efficient and interleaved learning and sampling, thus
user-specific anytime data exploration. Finally, LetSIP demonstrates favourable
trade-offs concerning both quality-diversity and exploitation-exploration when
compared to existing methods.
Compressive K-means
Authors: Nicolas Keriven, Nicolas Tremblay, Yann Traonmilin, Rémi Gribonval
Date: 2016-10-27 00:00:00
Tasks: Clustering, General Classification

The Lloyd-Max algorithm is a classical approach to perform K-means
clustering. Unfortunately, its cost becomes prohibitive as the training dataset
grows large. We propose a compressive version of K-means (CKM), that estimates
cluster centers from a sketch, i.e. from a drastically compressed
representation of the training dataset. We demonstrate empirically that CKM
performs similarly to Lloyd-Max, for a sketch size proportional to the number
of cen-troids times the ambient dimension, and independent of the size of the
original dataset. Given the sketch, the computational complexity of CKM is also
independent of the size of the dataset. Unlike Lloyd-Max which requires several
replicates, we further demonstrate that CKM is almost insensitive to
initialization. For a large dataset of 10^7 data points, we show that CKM can
run two orders of magnitude faster than five replicates of Lloyd-Max, with
similar clustering performance on artificial data. Finally, CKM achieves lower
classification errors on handwritten digits classification.
Arabic Language Sentiment Analysis on Health Services
Authors: Abdulaziz M. Alayba, Vasile Palade, Matthew England, Rahat Iqbal
Date: 2017-02-10 00:00:00
Tasks: Sentiment Analysis

The social media network phenomenon leads to a massive amount of valuable
data that is available online and easy to access. Many users share images,
videos, comments, reviews, news and opinions on different social networks
sites, with Twitter being one of the most popular ones. Data collected from
Twitter is highly unstructured, and extracting useful information from tweets
is a challenging task. Twitter has a huge number of Arabic users who mostly
post and write their tweets using the Arabic language. While there has been a
lot of research on sentiment analysis in English, the amount of researches and
datasets in Arabic language is limited. This paper introduces an Arabic
language dataset which is about opinions on health services and has been
collected from Twitter. The paper will first detail the process of collecting
the data from Twitter and also the process of filtering, pre-processing and
annotating the Arabic text in order to build a big sentiment analysis dataset
in Arabic. Several Machine Learning algorithms (Naive Bayes, Support Vector
Machine and Logistic Regression) alongside Deep and Convolutional Neural
Networks were utilized in our experiments of sentiment analysis on our health
dataset.
A clustering approach to heterogeneous change detection
Authors: Luigi Tommaso Luppino, Stian Normann Anfinsen, Gabriele Moser, Robert Jenssen, Filippo Maria Bianchi, Sebastiano Serpico, Gregoire Mercier
Date: 2017-02-10 00:00:00
Tasks: Change Detection, Clustering, Earth Observation

Change detection in heterogeneous multitemporal satellite images is a
challenging and still not much studied topic in remote sensing and earth
observation. This paper focuses on comparison of image pairs covering the same
geographical area and acquired by two different sensors, one optical radiometer
and one synthetic aperture radar, at two different times. We propose a
clustering-based technique to detect changes, identified as clusters that split
or merge in the different images. To evaluate potentials and limitations of our
method, we perform experiments on real data. Preliminary results confirm the
relationship between splits and merges of clusters and the occurrence of
changes. However, it becomes evident that it is necessary to incorporate prior,
ancillary, or application-specific information to improve the interpretation of
clustering results and to identify unambiguously the areas of change.
Pose for Action - Action for Pose
Authors: Umar Iqbal, Martin Garbade, Juergen Gall
Date: 2016-03-13 00:00:00
Tasks: Action Recognition, Pose Estimation, Skeleton Based Action Recognition, Temporal Action Localization

In this work we propose to utilize information about human actions to improve
pose estimation in monocular videos. To this end, we present a pictorial
structure model that exploits high-level information about activities to
incorporate higher-order part dependencies by modeling action specific
appearance models and pose priors. However, instead of using an additional
expensive action recognition framework, the action priors are efficiently
estimated by our pose estimation framework. This is achieved by starting with a
uniform action prior and updating the action prior during pose estimation. We
also show that learning the right amount of appearance sharing among action
classes improves the pose estimation. We demonstrate the effectiveness of the
proposed method on two challenging datasets for pose estimation and action
recognition with over 80,000 test images.
Modeling Semantic Expectation: Using Script Knowledge for Referent Prediction
Authors: Ashutosh Modi, Ivan Titov, Vera Demberg, Asad Sayeed, Manfred Pinkal
Date: 2017-02-10 00:00:00
Proceeding: TACL 2017 1
Tasks: Common Sense Reasoning, Prediction, Referring Expression

Recent research in psycholinguistics has provided increasing evidence that
humans predict upcoming content. Prediction also affects perception and might
be a key to robustness in human language processing. In this paper, we
investigate the factors that affect human prediction by building a
computational model that can predict upcoming discourse referents based on
linguistic knowledge alone vs. linguistic knowledge jointly with common-sense
knowledge in the form of scripts. We find that script knowledge significantly
improves model estimates of human predictions. In a second study, we test the
highly controversial hypothesis that predictability influences referring
expression type but do not find evidence for such an effect.
Texture Characterization by Using Shape Co-occurrence Patterns
Authors: Gui-Song Xia, Gang Liu, Xiang Bai, Liangpei Zhang
Date: 2017-02-10 00:00:00
Tasks: Descriptive, Texture Classification

Texture characterization is a key problem in image understanding and pattern
recognition. In this paper, we present a flexible shape-based texture
representation using shape co-occurrence patterns. More precisely, texture
images are first represented by tree of shapes, each of which is associated
with several geometrical and radiometric attributes. Then four typical kinds of
shape co-occurrence patterns based on the hierarchical relationship of the
shapes in the tree are learned as codewords. Three different coding methods are
investigated to learn the codewords, with which, any given texture image can be
encoded into a descriptive vector. In contrast with existing works, the
proposed method not only inherits the strong ability to depict geometrical
aspects of textures and the high robustness to variations of imaging conditions
from the shape-based method, but also provides a flexible way to consider shape
relationships and to compute high-order statistics on the tree. To our
knowledge, this is the first time to use co-occurrence patterns of explicit
shapes as a tool for texture analysis. Experiments on various texture datasets
and scene datasets demonstrate the efficiency of the proposed method.
UsingWord Embedding for Cross-Language Plagiarism Detection
Authors: J. Ferrero, F. Agnes, L. Besacier, D. Schwab
Date: 2017-02-10 00:00:00
Tasks: Sentence, Word Embeddings

This paper proposes to use distributed representation of words (word
embeddings) in cross-language textual similarity detection. The main
contributions of this paper are the following: (a) we introduce new
cross-language similarity detection methods based on distributed representation
of words; (b) we combine the different methods proposed to verify their
complementarity and finally obtain an overall F1 score of 89.15% for
English-French similarity detection at chunk level (88.5% at sentence level) on
a very challenging corpus.
Multigrid with rough coefficients and Multiresolution operator decomposition from Hierarchical Information Games
Authors: Houman Owhadi
Date: 2015-03-11 00:00:00

We introduce a near-linear complexity (geometric and meshless/algebraic)
multigrid/multiresolution method for PDEs with rough ($L^\infty$) coefficients
with rigorous a-priori accuracy and performance estimates. The method is
discovered through a decision/game theory formulation of the problems of (1)
identifying restriction and interpolation operators (2) recovering a signal
from incomplete measurements based on norm constraints on its image under a
linear operator (3) gambling on the value of the solution of the PDE based on a
hierarchy of nested measurements of its solution or source term. The resulting
elementary gambles form a hierarchy of (deterministic) basis functions of
$H^1_0(\Omega)$ (gamblets) that (1) are orthogonal across subscales/subbands
with respect to the scalar product induced by the energy norm of the PDE (2)
enable sparse compression of the solution space in $H^1_0(\Omega)$ (3) induce
an orthogonal multiresolution operator decomposition. The operating diagram of
the multigrid method is that of an inverted pyramid in which gamblets are
computed locally (by virtue of their exponential decay), hierarchically (from
fine to coarse scales) and the PDE is decomposed into a hierarchy of
independent linear systems with uniformly bounded condition numbers. The
resulting algorithm is parallelizable both in space (via localization) and in
bandwith/subscale (subscales can be computed independently from each other).
Although the method is deterministic it has a natural Bayesian interpretation
under the measure of probability emerging (as a mixed strategy) from the
information game formulation and multiresolution approximations form a
martingale with respect to the filtration induced by the hierarchy of nested
measurements.
A Modified Construction for a Support Vector Classifier to Accommodate Class Imbalances
Authors: Matt Parker, Colin Parker
Date: 2017-02-08 00:00:00
Tasks: Binary Classification

Given a training set with binary classification, the Support Vector Machine
identifies the hyperplane maximizing the margin between the two classes of
training data. This general formulation is useful in that it can be applied
without regard to variance differences between the classes. Ignoring these
differences is not optimal, however, as the general SVM will give the class
with lower variance an unjustifiably wide berth. This increases the chance of
misclassification of the other class and results in an overall loss of
predictive performance. An alternate construction is proposed in which the
margins of the separating hyperplane are different for each class, each
proportional to the standard deviation of its class along the direction
perpendicular to the hyperplane. The construction agrees with the SVM in the
case of equal class variances. This paper will then examine the impact to the
dual representation of the modified constraint equations.
Online People Tracking and Identification with RFID and Kinect
Authors: Xinyu Li, Yanyi Zhang, Ivan Marsic, Randall S. Burd
Date: 2017-02-10 00:00:00
Tasks: Position, TAG

We introduce a novel, accurate and practical system for real-time people
tracking and identification. We used a Kinect V2 sensor for tracking that
generates a body skeleton for up to six people in the view. We perform
identification using both Kinect and passive RFID, by first measuring the
velocity vector of person's skeleton and of their RFID tag using the position
of the RFID reader antennas as reference points and then finding the best match
between skeletons and tags. We introduce a method for synchronizing Kinect
data, which is captured regularly, with irregular or missing RFID data
readouts. Our experiments show centimeter-level people tracking resolution with
80% average identification accuracy for up to six people in indoor
environments, which meets the needs of many applications. Our system can
preserve user privacy and work with different lighting.
Sequential Deep Trajectory Descriptor for Action Recognition with Three-stream CNN
Authors: Yemin Shi, Yonghong Tian, Yao-Wei Wang, Tiejun Huang
Date: 2016-09-10 00:00:00
Tasks: Action Recognition, Temporal Action Localization

Learning the spatial-temporal representation of motion information is crucial
to human action recognition. Nevertheless, most of the existing features or
descriptors cannot capture motion information effectively, especially for
long-term motion. To address this problem, this paper proposes a long-term
motion descriptor called sequential Deep Trajectory Descriptor (sDTD).
Specifically, we project dense trajectories into two-dimensional planes, and
subsequently a CNN-RNN network is employed to learn an effective representation
for long-term motion. Unlike the popular two-stream ConvNets, the sDTD stream
is introduced into a three-stream framework so as to identify actions from a
video sequence. Consequently, this three-stream framework can simultaneously
capture static spatial features, short-term motion and long-term motion in the
video. Extensive experiments were conducted on three challenging datasets: KTH,
HMDB51 and UCF101. Experimental results show that our method achieves
state-of-the-art performance on the KTH and UCF101 datasets, and is comparable
to the state-of-the-art methods on the HMDB51 dataset.