pwc-archive/papers-with-abstracts huggingface dataset:
Boosted Zero-Shot Learning with Semantic Correlation Regularization
Authors: Te Pi, Xi Li, Zhongfei, Zhang
Date: 2017-07-25 00:00:00
Tasks: Model Selection, Transfer Learning, zero-shot-classification, Zero-Shot Learning

We study zero-shot learning (ZSL) as a transfer learning problem, and focus
on the two key aspects of ZSL, model effectiveness and model adaptation. For
effective modeling, we adopt the boosting strategy to learn a zero-shot
classifier from weak models to a strong model. For adaptable knowledge
transfer, we devise a Semantic Correlation Regularization (SCR) approach to
regularize the boosted model to be consistent with the inter-class semantic
correlations. With SCR embedded in the boosting objective, and with a
self-controlled sample selection for learning robustness, we propose a unified
framework, Boosted Zero-shot classification with Semantic Correlation
Regularization (BZ-SCR). By balancing the SCR-regularized boosted model
selection and the self-controlled sample selection, BZ-SCR is capable of
capturing both discriminative and adaptable feature-to-class semantic
alignments, while ensuring the reliability and adaptability of the learned
samples. The experiments on two ZSL datasets show the superiority of BZ-SCR
over the state-of-the-arts.
Unmasking the abnormal events in video
Authors: Radu Tudor Ionescu, Sorina Smeureanu, Bogdan Alexe, Marius Popescu
Date: 2017-05-23 00:00:00
Proceeding: ICCV 2017 10
Tasks: Abnormal Event Detection In Video, Anomaly Detection, Authorship Verification, Event Detection

We propose a novel framework for abnormal event detection in video that
requires no training sequences. Our framework is based on unmasking, a
technique previously used for authorship verification in text documents, which
we adapt to our task. We iteratively train a binary classifier to distinguish
between two consecutive video sequences while removing at each step the most
discriminant features. Higher training accuracy rates of the intermediately
obtained classifiers represent abnormal events. To the best of our knowledge,
this is the first work to apply unmasking for a computer vision task. We
compare our method with several state-of-the-art supervised and unsupervised
methods on four benchmark data sets. The empirical results indicate that our
abnormal event detection framework can achieve state-of-the-art results, while
running in real-time at 20 frames per second.
Towards Evolutional Compression
Authors: Yunhe Wang, Chang Xu, Jiayan Qiu, Chao Xu, DaCheng Tao
Date: 2017-07-25 00:00:00

Compressing convolutional neural networks (CNNs) is essential for
transferring the success of CNNs to a wide variety of applications to mobile
devices. In contrast to directly recognizing subtle weights or filters as
redundant in a given CNN, this paper presents an evolutionary method to
automatically eliminate redundant convolution filters. We represent each
compressed network as a binary individual of specific fitness. Then, the
population is upgraded at each evolutionary iteration using genetic operations.
As a result, an extremely compact CNN is generated using the fittest
individual. In this approach, either large or small convolution filters can be
redundant, and filters in the compressed network are more distinct. In
addition, since the number of filters in each convolutional layer is reduced,
the number of filter channels and the size of feature maps are also decreased,
naturally improving both the compression and speed-up ratios. Experiments on
benchmark deep CNN models suggest the superiority of the proposed algorithm
over the state-of-the-art compression methods.
Un modèle pour la représentation des connaissances temporelles dans les documents historiques
Authors: Sahar Aljalbout, Gilles Falquet
Date: 2017-07-25 00:00:00

Processing and publishing the data of the historical sciences in the semantic
web is an interesting challenge in which the representation of temporal aspects
plays a key role. We propose in this paper a model of temporal knowledge
representation adapted to work on historical documents. This model is based on
the notion of fluent that is represented in RDF graphs. We show how this model
allows to represent the knowledge necessary to the historians and how it can be
used to reason on this knowledge using the SWRL and SPARQL languages. This
model is being used in a project to digitize, study and publish the manuscripts
of linguist Ferdinand de Saussure.
Evidence combination for a large number of sources
Authors: Kuang Zhou, Arnaud Martin, Quan Pan
Date: 2017-07-25 00:00:00

The theory of belief functions is an effective tool to deal with the multiple
uncertain information. In recent years, many evidence combination rules have
been proposed in this framework, such as the conjunctive rule, the cautious
rule, the PCR (Proportional Conflict Redistribution) rules and so on. These
rules can be adopted for different types of sources. However, most of these
rules are not applicable when the number of sources is large. This is due to
either the complexity or the existence of an absorbing element (such as the
total conflict mass function for the conjunctive-based rules when applied on
unreliable evidence). In this paper, based on the assumption that the majority
of sources are reliable, a combination rule for a large number of sources,
named LNS (stands for Large Number of Sources), is proposed on the basis of a
simple idea: the more common ideas one source shares with others, the
morereliable the source is. This rule is adaptable for aggregating a large
number of sources among which some are unreliable. It will keep the spirit of
the conjunctive rule to reinforce the belief on the focal elements with which
the sources are in agreement. The mass on the empty set will be kept as an
indicator of the conflict. Moreover, it can be used to elicit the major opinion
among the experts. The experimental results on synthetic mass functionsverify
that the rule can be effectively used to combine a large number of mass
functions and to elicit the major opinion.
Scaled Nuclear Norm Minimization for Low-Rank Tensor Completion
Authors: Morteza Ashraphijuo, Xiaodong Wang
Date: 2017-07-25 00:00:00

Minimizing the nuclear norm of a matrix has been shown to be very efficient
in reconstructing a low-rank sampled matrix. Furthermore, minimizing the sum of
nuclear norms of matricizations of a tensor has been shown to be very efficient
in recovering a low-Tucker-rank sampled tensor. In this paper, we propose to
recover a low-TT-rank sampled tensor by minimizing a weighted sum of nuclear
norms of unfoldings of the tensor. We provide numerical results to show that
our proposed method requires significantly less number of samples to recover to
the original tensor in comparison with simply minimizing the sum of nuclear
norms since the structure of the unfoldings in the TT tensor model is
fundamentally different from that of matricizations in the Tucker tensor model.
Car sharing through the data analysis lens
Authors: Chiara Boldrini, Raffaele Bruno, Haitam Laarabi
Date: 2017-07-25 00:00:00

Car sharing is one the pillars of a smart transportation infrastructure, as
it is expected to reduce traffic congestion, parking demands and pollution in
our cities. From the point of view of demand modelling, car sharing is a weak
signal in the city landscape: only a small percentage of the population uses
it, and thus it is difficult to study reliably with traditional techniques such
as households travel diaries. In this work, we depart from these traditional
approaches and we rely on web-based, digital records about vehicle availability
in 10 European cities for one of the major active car sharing operators. We
discuss how vehicles are used, what are the main characteristics of car sharing
trips, whether events happening in certain areas are predictable or not, and
how the spatio-temporal information about vehicle availability can be used to
infer how different zones in a city are used by customers. We conclude the
paper by presenting a direct application of the analysis of the dataset, aimed
at identifying where to locate maintenance facilities within the car sharing
operational area.
Nested Kriging predictions for datasets with large number of observations
Authors: Didier Rullière, Nicolas Durrande, François Bachoc, Clément Chevalier
Date: 2016-07-19 00:00:00

This work falls within the context of predicting the value of a real function
at some input locations given a limited number of observations of this
function. The Kriging interpolation technique (or Gaussian process regression)
is often considered to tackle such a problem but the method suffers from its
computational burden when the number of observation points is large. We
introduce in this article nested Kriging predictors which are constructed by
aggregating sub-models based on subsets of observation points. This approach is
proven to have better theoretical properties than other aggregation methods
that can be found in the literature. Contrarily to some other methods it can be
shown that the proposed aggregation method is consistent. Finally, the
practical interest of the proposed method is illustrated on simulated datasets
and on an industrial test case with $10^4$ observations in a 6-dimensional
space.
A Decidable Very Expressive Description Logic for Databases (Extended Version)
Authors: Alessandro Artale, Enrico Franconi, Rafael Peñaloza, Francesco Sportelli
Date: 2017-07-25 00:00:00
Tasks: Attribute

We introduce $\mathcal{DLR}^+$, an extension of the n-ary propositionally
closed description logic $\mathcal{DLR}$ to deal with attribute-labelled tuples
(generalising the positional notation), projections of relations, and global
and local objectification of relations, able to express inclusion, functional,
key, and external uniqueness dependencies. The logic is equipped with both TBox
and ABox axioms. We show how a simple syntactic restriction on the appearance
of projections sharing common attributes in a $\mathcal{DLR}^+$ knowledge base
makes reasoning in the language decidable with the same computational
complexity as $\mathcal{DLR}$. The obtained $\mathcal{DLR}^\pm$ n-ary
description logic is able to encode more thoroughly conceptual data models such
as EER, UML, and ORM.
Kernel Sequential Monte Carlo
Authors: Ingmar Schuster, Heiko Strathmann, Brooks Paige, Dino Sejdinovic
Date: 2015-10-11 00:00:00

We propose kernel sequential Monte Carlo (KSMC), a framework for sampling
from static target densities. KSMC is a family of sequential Monte Carlo
algorithms that are based on building emulator models of the current particle
system in a reproducing kernel Hilbert space. We here focus on modelling
nonlinear covariance structure and gradients of the target. The emulator's
geometry is adaptively updated and subsequently used to inform local proposals.
Unlike in adaptive Markov chain Monte Carlo, continuous adaptation does not
compromise convergence of the sampler. KSMC combines the strengths of sequental
Monte Carlo and kernel methods: superior performance for multimodal targets and
the ability to estimate model evidence as compared to Markov chain Monte Carlo,
and the emulator's ability to represent targets that exhibit high degrees of
nonlinearity. As KSMC does not require access to target gradients, it is
particularly applicable on targets whose gradients are unknown or prohibitively
expensive. We describe necessary tuning details and demonstrate the benefits of
the the proposed methodology on a series of challenging synthetic and
real-world examples.
Semi-Supervised Deep Learning for Fully Convolutional Networks
Authors: Christoph Baur, Shadi Albarqouni, Nassir Navab
Date: 2017-03-17 00:00:00
Tasks: Deep Learning, Domain Adaptation, Image Segmentation, Lesion Segmentation, Segmentation, Semantic Segmentation

Deep learning usually requires large amounts of labeled training data, but
annotating data is costly and tedious. The framework of semi-supervised
learning provides the means to use both labeled data and arbitrary amounts of
unlabeled data for training. Recently, semi-supervised deep learning has been
intensively studied for standard CNN architectures. However, Fully
Convolutional Networks (FCNs) set the state-of-the-art for many image
segmentation tasks. To the best of our knowledge, there is no existing
semi-supervised learning method for such FCNs yet. We lift the concept of
auxiliary manifold embedding for semi-supervised learning to FCNs with the help
of Random Feature Embedding. In our experiments on the challenging task of MS
Lesion Segmentation, we leverage the proposed framework for the purpose of
domain adaptation and report substantial improvements over the baseline model.
Functional connectivity patterns of autism spectrum disorder identified by deep feature learning
Authors: Hongyoon Choi
Date: 2017-07-25 00:00:00
Tasks: Functional Connectivity

Autism spectrum disorder (ASD) is regarded as a brain disease with globally
disrupted neuronal networks. Even though fMRI studies have revealed abnormal
functional connectivity in ASD, they have not reached a consensus of the
disrupted patterns. Here, a deep learning-based feature extraction method
identifies multivariate and nonlinear functional connectivity patterns of ASD.
Resting-state fMRI data of 972 subjects (465 ASD 507 normal controls) acquired
from the Autism Brain Imaging Data Exchange were used. A functional
connectivity matrix of each subject was generated using 90 predefined brain
regions. As a data-driven feature extraction method without prior knowledge
such as subjects diagnosis, variational autoencoder (VAE) summarized the
functional connectivity matrix into 2 features. Those feature values of ASD
patients were statistically compared with those of controls. A feature was
significantly different between ASD and normal controls. The extracted features
were visualized by VAE-based generator which can produce virtual functional
connectivity matrices. The ASD-related feature was associated with
frontoparietal connections, interconnections of the dorsal medial frontal
cortex and corticostriatal connections. It also showed a trend of negative
correlation with full-scale IQ. A data-driven feature extraction based on deep
learning could identify complex patterns of functional connectivity of ASD.
This approach will help discover complex patterns of abnormalities in brain
connectivity in various brain disorders.
Structural Regularities in Text-based Entity Vector Spaces
Authors: Christophe Van Gysel, Maarten de Rijke, Evangelos Kanoulas
Date: 2017-07-25 00:00:00
Tasks: Clustering, Entity Retrieval, Retrieval, Topic Models

Entity retrieval is the task of finding entities such as people or products
in response to a query, based solely on the textual documents they are
associated with. Recent semantic entity retrieval algorithms represent queries
and experts in finite-dimensional vector spaces, where both are constructed
from text sequences.
  We investigate entity vector spaces and the degree to which they capture
structural regularities. Such vector spaces are constructed in an unsupervised
manner without explicit information about structural aspects. For concreteness,
we address these questions for a specific type of entity: experts in the
context of expert finding. We discover how clusterings of experts correspond to
committees in organizations, the ability of expert representations to encode
the co-author graph, and the degree to which they encode academic rank. We
compare latent, continuous representations created using methods based on
distributional semantics (LSI), topic models (LDA) and neural networks
(word2vec, doc2vec, SERT). Vector spaces created using neural methods, such as
doc2vec and SERT, systematically perform better at clustering than LSI, LDA and
word2vec. When it comes to encoding entity relations, SERT performs best.
Using Empirical Covariance Matrix in Enhancing Prediction Accuracy of Linear Models with Missing Information
Authors: Ahmadreza Moradipari, Sina Shahsavari, Ashkan Esmaeili, Farokh Marvasti
Date: 2016-11-21 00:00:00
Tasks: feature selection, Learning Theory, Matrix Completion

Inference and Estimation in Missing Information (MI) scenarios are important
topics in Statistical Learning Theory and Machine Learning (ML). In ML
literature, attempts have been made to enhance prediction through precise
feature selection methods. In sparse linear models, LASSO is well-known in
extracting the desired support of the signal and resisting against noisy
systems. When sparse models are also suffering from MI, the sparse recovery and
inference of the missing models are taken into account simultaneously. In this
paper, we will introduce an approach which enjoys sparse regression and
covariance matrix estimation to improve matrix completion accuracy, and as a
result enhancing feature selection preciseness which leads to reduction in
prediction Mean Squared Error (MSE). We will compare the effect of employing
covariance matrix in enhancing estimation accuracy to the case it is not used
in feature selection. Simulations show the improvement in the performance as
compared to the case where the covariance matrix estimation is not used.
Machine Translation at Booking.com: Journey and Lessons Learned
Authors: Pavel Levin, Nishikant Dhanuka, Maxim Khalilov
Date: 2017-07-25 00:00:00
Tasks: Machine Translation, NMT, Sentence, Translation

We describe our recently developed neural machine translation (NMT) system
and benchmark it against our own statistical machine translation (SMT) system
as well as two other general purpose online engines (statistical and neural).
We present automatic and human evaluation results of the translation output
provided by each system. We also analyze the effect of sentence length on the
quality of output for SMT and NMT systems.
Partial Transfer Learning with Selective Adversarial Networks
Authors: Zhangjie Cao, Mingsheng Long, Jian-Min Wang, Michael. I. Jordan
Date: 2017-07-25 00:00:00
Proceeding: CVPR 2018 6
Tasks: Transfer Learning

Adversarial learning has been successfully embedded into deep networks to
learn transferable features, which reduce distribution discrepancy between the
source and target domains. Existing domain adversarial networks assume fully
shared label space across domains. In the presence of big data, there is strong
motivation of transferring both classification and representation models from
existing big domains to unknown small domains. This paper introduces partial
transfer learning, which relaxes the shared label space assumption to that the
target label space is only a subspace of the source label space. Previous
methods typically match the whole source domain to the target domain, which are
prone to negative transfer for the partial transfer problem. We present
Selective Adversarial Network (SAN), which simultaneously circumvents negative
transfer by selecting out the outlier source classes and promotes positive
transfer by maximally matching the data distributions in the shared label
space. Experiments demonstrate that our models exceed state-of-the-art results
for partial transfer learning tasks on several benchmark datasets.
CNN-based Segmentation of Medical Imaging Data
Authors: Baris Kayalibay, Grady Jensen, Patrick van der Smagt
Date: 2017-01-11 00:00:00
Tasks: Brain Tumor Segmentation, Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

Convolutional neural networks have been applied to a wide variety of computer
vision tasks. Recent advances in semantic segmentation have enabled their
application to medical image segmentation. While most CNNs use two-dimensional
kernels, recent CNN-based publications on medical image segmentation featured
three-dimensional kernels, allowing full access to the three-dimensional
structure of medical images. Though closely related to semantic segmentation,
medical image segmentation includes specific challenges that need to be
addressed, such as the scarcity of labelled data, the high class imbalance
found in the ground truth and the high memory demand of three-dimensional
images. In this work, a CNN-based method with three-dimensional filters is
demonstrated and applied to hand and brain MRI. Two modifications to an
existing CNN architecture are discussed, along with methods on addressing the
aforementioned challenges. While most of the existing literature on medical
image segmentation focuses on soft tissue and the major organs, this work is
validated on data both from the central nervous system as well as the bones of
the hand.
End-to-end Concept Word Detection for Video Captioning, Retrieval, and Question Answering
Authors: Youngjae Yu, Hyungjin Ko, Jongwook Choi, Gunhee Kim
Date: 2016-10-10 00:00:00
Proceeding: CVPR 2017 7
Tasks: Language Modeling, Language Modelling, Multiple-choice, Question Answering, Retrieval, Text Generation, Video Captioning, Video Retrieval

We propose a high-level concept word detector that can be integrated with any
video-to-language models. It takes a video as input and generates a list of
concept words as useful semantic priors for language generation models. The
proposed word detector has two important properties. First, it does not require
any external knowledge sources for training. Second, the proposed word detector
is trainable in an end-to-end manner jointly with any video-to-language models.
To maximize the values of detected words, we also develop a semantic attention
mechanism that selectively focuses on the detected concept words and fuse them
with the word encoding and decoding in the language model. In order to
demonstrate that the proposed approach indeed improves the performance of
multiple video-to-language tasks, we participate in four tasks of LSMDC 2016.
Our approach achieves the best accuracies in three of them, including
fill-in-the-blank, multiple-choice test, and movie retrieval. We also attain
comparable performance for the other task, movie description.
Spatiotemporal Modeling for Crowd Counting in Videos
Authors: Feng Xiong, Xingjian Shi, Dit-yan Yeung
Date: 2017-07-25 00:00:00
Proceeding: ICCV 2017 10
Tasks: Crowd Counting, Transfer Learning

Region of Interest (ROI) crowd counting can be formulated as a regression
problem of learning a mapping from an image or a video frame to a crowd density
map. Recently, convolutional neural network (CNN) models have achieved
promising results for crowd counting. However, even when dealing with video
data, CNN-based methods still consider each video frame independently, ignoring
the strong temporal correlation between neighboring frames. To exploit the
otherwise very useful temporal information in video sequences, we propose a
variant of a recent deep learning model called convolutional LSTM (ConvLSTM)
for crowd counting. Unlike the previous CNN-based methods, our method fully
captures both spatial and temporal dependencies. Furthermore, we extend the
ConvLSTM model to a bidirectional ConvLSTM model which can access long-range
information in both directions. Extensive experiments using four publicly
available datasets demonstrate the reliability of our approach and the
effectiveness of incorporating temporal information to boost the accuracy of
crowd counting. In addition, we also conduct some transfer learning experiments
to show that once our model is trained on one dataset, its learning experience
can be transferred easily to a new dataset which consists of only very few
video frames for model adaptation.
A Simulator for Hedonic Games
Authors: Luke Harold Miles
Date: 2017-06-26 00:00:00

Hedonic games are meant to model how coalitions of people form and break
apart in the real world. However, it is difficult to run simulations when
everything must be done by hand on paper. We present an online software that
allows fast and visual simulation of several types of hedonic games.
http://lukemiles.org/hedonic-games/