pwc-archive/papers-with-abstracts huggingface dataset:
Detecting Changes Between Optical Images of Different Spatial and Spectral Resolutions: a Fusion-Based Approach
Authors: Vinicius Ferraris, Nicolas Dobigeon, Qi Wei, Marie Chabert
Date: 2016-09-20 00:00:00
Tasks: Change Detection

Change detection is one of the most challenging issues when analyzing
remotely sensed images. Comparing several multi-date images acquired through
the same kind of sensor is the most common scenario. Conversely, designing
robust, flexible and scalable algorithms for change detection becomes even more
challenging when the images have been acquired by two different kinds of
sensors. This situation arises in case of emergency under critical constraints.
This paper presents, to the best of authors' knowledge, the first strategy to
deal with optical images characterized by dissimilar spatial and spectral
resolutions. Typical considered scenarios include change detection between
panchromatic or multispectral and hyperspectral images. The proposed strategy
consists of a 3-step procedure: i) inferring a high spatial and spectral
resolution image by fusion of the two observed images characterized one by a
low spatial resolution and the other by a low spectral resolution, ii)
predicting two images with respectively the same spatial and spectral
resolutions as the observed images by degradation of the fused one and iii)
implementing a decision rule to each pair of observed and predicted images
characterized by the same spatial and spectral resolutions to identify changes.
The performance of the proposed framework is evaluated on real images with
simulated realistic changes.
Budget-Constrained Item Cold-Start Handling in Collaborative Filtering Recommenders via Optimal Design
Authors: Oren Anava, Shahar Golan, Nadav Golbandi, Zohar Karnin, Ronny Lempel, Oleg Rokhlenko, Oren Somekh
Date: 2014-06-10 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

It is well known that collaborative filtering (CF) based recommender systems
provide better modeling of users and items associated with considerable rating
history. The lack of historical ratings results in the user and the item
cold-start problems. The latter is the main focus of this work. Most of the
current literature addresses this problem by integrating content-based
recommendation techniques to model the new item. However, in many cases such
content is not available, and the question arises is whether this problem can
be mitigated using CF techniques only. We formalize this problem as an
optimization problem: given a new item, a pool of available users, and a budget
constraint, select which users to assign with the task of rating the new item
in order to minimize the prediction error of our model. We show that the
objective function is monotone-supermodular, and propose efficient optimal
design based algorithms that attain an approximation to its optimum. Our
findings are verified by an empirical study using the Netflix dataset, where
the proposed algorithms outperform several baselines for the problem at hand.
Early Visual Concept Learning with Unsupervised Deep Learning
Authors: Irina Higgins, Loic Matthey, Xavier Glorot, Arka Pal, Benigno Uria, Charles Blundell, Shakir Mohamed, Alexander Lerchner
Date: 2016-06-17 00:00:00
Tasks: Deep Learning

Automated discovery of early visual concepts from raw image data is a major
open challenge in AI research. Addressing this problem, we propose an
unsupervised approach for learning disentangled representations of the
underlying factors of variation. We draw inspiration from neuroscience, and
show how this can be achieved in an unsupervised generative model by applying
the same learning pressures as have been suggested to act in the ventral visual
stream in the brain. By enforcing redundancy reduction, encouraging statistical
independence, and exposure to data with transform continuities analogous to
those to which human infants are exposed, we obtain a variational autoencoder
(VAE) framework capable of learning disentangled factors. Our approach makes
few assumptions and works well across a wide variety of datasets. Furthermore,
our solution has useful emergent properties, such as zero-shot inference and an
intuitive understanding of "objectness".
The Digital Synaptic Neural Substrate: A New Approach to Computational Creativity
Authors: Azlan Iqbal, Matej Guid, Simon Colton, Jana Krivec, Shazril Azman, Boshra Haghighi
Date: 2015-07-25 00:00:00
Tasks: Open-Ended Question Answering

We introduce a new artificial intelligence (AI) approach called, the 'Digital
Synaptic Neural Substrate' (DSNS). It uses selected attributes from objects in
various domains (e.g. chess problems, classical music, renowned artworks) and
recombines them in such a way as to generate new attributes that can then, in
principle, be used to create novel objects of creative value to humans relating
to any one of the source domains. This allows some of the burden of creative
content generation to be passed from humans to machines. The approach was
tested in the domain of chess problem composition. We used it to automatically
compose numerous sets of chess problems based on attributes extracted and
recombined from chess problems and tournament games by humans, renowned
paintings, computer-evolved abstract art, photographs of people, and classical
music tracks. The quality of these generated chess problems was then assessed
automatically using an existing and experimentally-validated computational
chess aesthetics model. They were also assessed by human experts in the domain.
The results suggest that attributes collected and recombined from chess and
other domains using the DSNS approach can indeed be used to automatically
generate chess problems of reasonably high aesthetic quality. In particular, a
low quality chess source (i.e. tournament game sequences between weak players)
used in combination with actual photographs of people was able to produce
three-move chess problems of comparable quality or better to those generated
using a high quality chess source (i.e. published compositions by human
experts), and more efficiently as well. Why information from a foreign domain
can be integrated and functional in this way remains an open question for now.
The DSNS approach is, in principle, scalable and applicable to any domain in
which objects have attributes that can be represented using real numbers.
Automatic Quality Assessment for Speech Translation Using Joint ASR and MT Features
Authors: Ngoc-Tien Le, Benjamin Lecouteux, Laurent Besacier
Date: 2016-09-20 00:00:00
Tasks: Speech-to-Text, Translation

This paper addresses automatic quality assessment of spoken language
translation (SLT). This relatively new task is defined and formalized as a
sequence labeling problem where each word in the SLT hypothesis is tagged as
good or bad according to a large feature set. We propose several word
confidence estimators (WCE) based on our automatic evaluation of transcription
(ASR) quality, translation (MT) quality, or both (combined ASR+MT). This
research work is possible because we built a specific corpus which contains
6.7k utterances for which a quintuplet containing: ASR output, verbatim
transcript, text translation, speech translation and post-edition of
translation is built. The conclusion of our multiple experiments using joint
ASR and MT features for WCE is that MT features remain the most influent while
ASR feature can bring interesting complementary information. Our robust quality
estimators for SLT can be used for re-scoring speech translation graphs or for
providing feedback to the user in interactive speech translation or
computer-assisted speech-to-text scenarios.
A very fast iterative algorithm for TV-regularized image reconstruction with applications to low-dose and few-view CT
Authors: Hiroyuki Kudo, Fukashi Yamazaki, Takuya Nemoto, Keita Takaki
Date: 2016-09-20 00:00:00
Tasks: Image Reconstruction

This paper concerns iterative reconstruction for low-dose and few-view CT by
minimizing a data-fidelity term regularized with the Total Variation (TV)
penalty. We propose a very fast iterative algorithm to solve this problem. The
algorithm derivation is outlined as follows. First, the original minimization
problem is reformulated into the saddle point (primal-dual) problem by using
the Lagrangian duality, to which we apply the first-order primal-dual iterative
methods. Second, we precondition the iteration formula using the ramp flter of
Filtered Backprojection (FBP) reconstruction algorithm in such a way that the
problem solution is not altered. The resulting algorithm resembles the
structure of so-called iterative FBP algorithm, and it converges to the exact
minimizer of cost function very fast.
Style-Transfer via Texture-Synthesis
Authors: Michael Elad, Peyman Milanfar
Date: 2016-09-10 00:00:00
Tasks: Style Transfer, Texture Synthesis

Style-transfer is a process of migrating a style from a given image to the
content of another, synthesizing a new image which is an artistic mixture of
the two. Recent work on this problem adopting Convolutional Neural-networks
(CNN) ignited a renewed interest in this field, due to the very impressive
results obtained. There exists an alternative path towards handling the
style-transfer task, via generalization of texture-synthesis algorithms. This
approach has been proposed over the years, but its results are typically less
impressive compared to the CNN ones.
  In this work we propose a novel style-transfer algorithm that extends the
texture-synthesis work of Kwatra et. al. (2005), while aiming to get stylized
images that get closer in quality to the CNN ones. We modify Kwatra's algorithm
in several key ways in order to achieve the desired transfer, with emphasis on
a consistent way for keeping the content intact in selected regions, while
producing hallucinated and rich style in others. The results obtained are
visually pleasing and diverse, shown to be competitive with the recent CNN
style-transfer algorithms. The proposed algorithm is fast and flexible, being
able to process any pair of content + style images.
Contextual Relationship-based Activity Segmentation on an Event Stream in the IoT Environment with Multi-user Activities
Authors: Minkyoung Cho, Younggi Kim, Younghee Lee
Date: 2016-09-20 00:00:00
Tasks: Activity Recognition, Human Activity Recognition

The human activity recognition in the IoT environment plays the central role
in the ambient assisted living, where the human activities can be represented
as a concatenated event stream generated from various smart objects. From the
concatenated event stream, each activity should be distinguished separately for
the human activity recognition to provide services that users may need. In this
regard, accurately segmenting the entire stream at the precise boundary of each
activity is indispensable high priority task to realize the activity
recognition. Multiple human activities in an IoT environment generate varying
event stream patterns, and the unpredictability of these patterns makes them
include redundant or missing events. In dealing with this complex segmentation
problem, we figured out that the dynamic and confusing patterns cause major
problems due to: inclusive event stream, redundant events, and shared events.
To address these problems, we exploited the contextual relationships associated
with the activity status about either ongoing or terminated/started. To
discover the intrinsic relationships between the events in a stream, we
utilized the LSTM model by rendering it for the activity segmentation. Then,
the inferred boundaries were revised by our validation algorithm for a bit
shifted boundaries. Our experiments show the surprising result of high accuracy
above 95%, on our own testbed with various smart objects. This is superior to
the prior works that even do not assume the environment with multi-user
activities, where their accuracies are slightly above 80% in their test
environment. It proves that our work is feasible enough to be applied in the
IoT environment.
Proposal of fault-tolerant tomographic image reconstruction
Authors: Hiroyuki Kudo, Keita Takaki, Fukashi Yamazaki, Takuya Nemoto
Date: 2016-09-20 00:00:00
Tasks: Image Reconstruction

This paper deals with tomographic image reconstruction under the situation
where some of projection data bins are contaminated with abnormal data. Such
situations occur in various instances of tomography. We propose a new
reconstruction algorithm called the Fault-Tolerant reconstruction outlined as
follows. The least-squares (L2-norm) error function ||Ax-b||_2^2 used in
ordinary iterative reconstructions is sensitive to the existence of abnormal
data. The proposed algorithm utilizes the L1-norm error function ||Ax-b||_1^1
instead of the L2-norm, and we develop a row-action-type iterative algorithm
using the proximal splitting framework in convex optimization fields. We also
propose an improved version of the L1-norm reconstruction called the L1-TV
reconstruction, in which a weak Total Variation (TV) penalty is added to the
cost function. Simulation results demonstrate that reconstructed images with
the L2-norm were severely damaged by the effect of abnormal bins, whereas
images with the L1-norm and L1-TV reconstructions were robust to the existence
of abnormal bins.
Feed-Forward Networks with Attention Can Solve Some Long-Term Memory Problems
Authors: Colin Raffel, Daniel P. W. Ellis
Date: 2015-12-29 00:00:00

We propose a simplified model of attention which is applicable to
feed-forward neural networks and demonstrate that the resulting model can solve
the synthetic "addition" and "multiplication" long-term memory problems for
sequence lengths which are both longer and more widely varying than the best
published results for these tasks.
A Direct Approach for Sparse Quadratic Discriminant Analysis
Authors: Binyan Jiang, Xiangyu Wang, Chenlei Leng
Date: 2015-10-01 00:00:00
Tasks: General Classification

Quadratic discriminant analysis (QDA) is a standard tool for classification
due to its simplicity and flexibility. Because the number of its parameters
scales quadratically with the number of the variables, QDA is not practical,
however, when the dimensionality is relatively large. To address this, we
propose a novel procedure named DA-QDA for QDA in analyzing high-dimensional
data. Formulated in a simple and coherent framework, DA-QDA aims to directly
estimate the key quantities in the Bayes discriminant function including
quadratic interactions and a linear index of the variables for classification.
Under appropriate sparsity assumptions, we establish consistency results for
estimating the interactions and the linear index, and further demonstrate that
the misclassification rate of our procedure converges to the optimal Bayes
risk, even when the dimensionality is exponentially high with respect to the
sample size. An efficient algorithm based on the alternating direction method
of multipliers (ADMM) is developed for finding interactions, which is much
faster than its competitor in the literature. The promising performance of
DA-QDA is illustrated via extensive simulation studies and the analysis of four
real datasets.
Deep CTR Prediction in Display Advertising
Authors: Junxuan Chen, Baigui Sun, Hao Li, Hongtao Lu, Xian-Sheng Hua
Date: 2016-09-20 00:00:00
Tasks: Click-Through Rate Prediction, Prediction

Click through rate (CTR) prediction of image ads is the core task of online
display advertising systems, and logistic regression (LR) has been frequently
applied as the prediction model. However, LR model lacks the ability of
extracting complex and intrinsic nonlinear features from handcrafted
high-dimensional image features, which limits its effectiveness. To solve this
issue, in this paper, we introduce a novel deep neural network (DNN) based
model that directly predicts the CTR of an image ad based on raw image pixels
and other basic features in one step. The DNN model employs convolution layers
to automatically extract representative visual features from images, and
nonlinear CTR features are then learned from visual features and other
contextual features by using fully-connected layers. Empirical evaluations on a
real world dataset with over 50 million records demonstrate the effectiveness
and efficiency of this method.
Perfect Recovery Conditions For Non-Negative Sparse Modeling
Authors: Yuki Itoh, Marco F. Duarte, Mario Parente
Date: 2015-12-09 00:00:00

Sparse modeling has been widely and successfully used in many applications
such as computer vision, machine learning, and pattern recognition. Accompanied
with those applications, significant research has studied the theoretical
limits and algorithm design for convex relaxations in sparse modeling. However,
theoretical analyses on non-negative versions of sparse modeling are limited in
the literature either to a noiseless setting or a scenario with a specific
statistical noise model such as Gaussian noise. This paper studies the
performance of non-negative sparse modeling in a more general scenario where
the observed signals have an unknown arbitrary distortion, especially focusing
on non-negativity constrained and L1-penalized least squares, and gives an
exact bound for which this problem can recover the correct signal elements. We
pose two conditions to guarantee the correct signal recovery: minimum
coefficient condition (MCC) and nonlinearity vs. subset coherence condition
(NSCC). The former defines the minimum weight for each of the correct atoms
present in the signal and the latter defines the tolerable deviation from the
linear model relative to the positive subset coherence (PSC), a novel type of
"coherence" metric. We provide rigorous performance guarantees based on these
conditions and experimentally verify their precise predictive power in a
hyperspectral data unmixing application.
Macro-optimization of email recommendation response rates harnessing individual activity levels and group affinity trends
Authors: Mohammed Korayem, Khalifeh Aljadda, Trey Grainger
Date: 2016-09-20 00:00:00
Tasks: Recommendation Systems

Recommendation emails are among the best ways to re-engage with customers
after they have left a website. While on-site recommendation systems focus on
finding the most relevant items for a user at the moment (right item), email
recommendations add two critical additional dimensions: who to send
recommendations to (right person) and when to send them (right time). It is
critical that a recommendation email system not send too many emails to too
many users in too short of a time-window, as users may unsubscribe from future
emails or become desensitized and ignore future emails if they receive too
many. Also, email service providers may mark such emails as spam if too many of
their users are contacted in a short time-window. Optimizing email
recommendation systems such that they can yield a maximum response rate for a
minimum number of email sends is thus critical for the long-term performance of
such a system. In this paper, we present a novel recommendation email system
that not only generates recommendations, but which also leverages a combination
of individual user activity data, as well as the behavior of the group to which
they belong, in order to determine each user's likelihood to respond to any
given set of recommendations within a given time period. In doing this, we have
effectively created a meta-recommendation system which recommends sets of
recommendations in order to optimize the aggregate response rate of the entire
system. The proposed technique has been applied successfully within
CareerBuilder's job recommendation email system to generate a 50\% increase in
total conversions while also decreasing sent emails by 72%
Preconditioned Data Sparsification for Big Data with Applications to PCA and K-means
Authors: Farhad Pourkamali-Anaraki, Stephen Becker
Date: 2015-10-31 00:00:00

We analyze a compression scheme for large data sets that randomly keeps a
small percentage of the components of each data sample. The benefit is that the
output is a sparse matrix and therefore subsequent processing, such as PCA or
K-means, is significantly faster, especially in a distributed-data setting.
Furthermore, the sampling is single-pass and applicable to streaming data. The
sampling mechanism is a variant of previous methods proposed in the literature
combined with a randomized preconditioning to smooth the data. We provide
guarantees for PCA in terms of the covariance matrix, and guarantees for
K-means in terms of the error in the center estimators at a given step. We
present numerical evidence to show both that our bounds are nearly tight and
that our algorithms provide a real benefit when applied to standard test data
sets, as well as providing certain benefits over related sampling approaches.
Why (and How) Avoid Orthogonal Procrustes in Regularized Multivariate Analysis
Authors: Sergio Muñoz-Romero, Vanessa Gómez-Verdejo, Jerónimo Arenas-García
Date: 2016-05-09 00:00:00

Multivariate Analysis (MVA) comprises a family of well-known methods for
feature extraction that exploit correlations among input variables of the data
representation. One important property that is enjoyed by most such methods is
uncorrelation among the extracted features. Recently, regularized versions of
MVA methods have appeared in the literature, mainly with the goal to gain
interpretability of the solution. In these cases, the solutions can no longer
be obtained in a closed manner, and it is frequent to recur to the iteration of
two steps, one of them being an orthogonal Procrustes problem. This letter
shows that the Procrustes solution is not optimal from the perspective of the
overall MVA method, and proposes an alternative approach based on the solution
of an eigenvalue problem. Our method ensures the preservation of several
properties of the original methods, most notably the uncorrelation of the
extracted features, as demonstrated theoretically and through a collection of
selected experiments.
Technical Report: Band selection for nonlinear unmixing of hyperspectral images as a maximal clique problem
Authors: Tales Imbiriba, José Carlos Moreira Bermudez, Cédric Richard
Date: 2016-03-01 00:00:00

Kernel-based nonlinear mixing models have been applied to unmix spectral
information of hyperspectral images when the type of mixing occurring in the
scene is too complex or unknown. Such methods, however, usually require the
inversion of matrices of sizes equal to the number of spectral bands. Reducing
the computational load of these methods remains a challenge in large scale
applications. This paper proposes a centralized method for band selection (BS)
in the reproducing kernel Hilbert space (RKHS). It is based upon the coherence
criterion, which sets the largest value allowed for correlations between the
basis kernel functions characterizing the unmixing model. We show that the
proposed BS approach is equivalent to solving a maximum clique problem (MCP),
that is, searching for the biggest complete subgraph in a graph. Furthermore,
we devise a strategy for selecting the coherence threshold and the Gaussian
kernel bandwidth using coherence bounds for linearly independent bases.
Simulation results illustrate the efficiency of the proposed method.