pwc-archive/papers-with-abstracts huggingface dataset:
You are your Metadata: Identification and Obfuscation of Social Media Users using Metadata Information
Authors: Beatrice Perez, Mirco Musolesi, Gianluca Stringhini
Date: 2018-03-27 00:00:00

Metadata are associated to most of the information we produce in our daily
interactions and communication in the digital world. Yet, surprisingly,
metadata are often still catergorized as non-sensitive. Indeed, in the past,
researchers and practitioners have mainly focused on the problem of the
identification of a user from the content of a message.
  In this paper, we use Twitter as a case study to quantify the uniqueness of
the association between metadata and user identity and to understand the
effectiveness of potential obfuscation strategies. More specifically, we
analyze atomic fields in the metadata and systematically combine them in an
effort to classify new tweets as belonging to an account using different
machine learning algorithms of increasing complexity. We demonstrate that
through the application of a supervised learning algorithm, we are able to
identify any user in a group of 10,000 with approximately 96.7% accuracy.
Moreover, if we broaden the scope of our search and consider the 10 most likely
candidates we increase the accuracy of the model to 99.22%. We also found that
data obfuscation is hard and ineffective for this type of data: even after
perturbing 60% of the training data, it is still possible to classify users
with an accuracy higher than 95%. These results have strong implications in
terms of the design of metadata obfuscation strategies, for example for data
set release, not only for Twitter, but, more generally, for most social media
platforms.
Triclustering of Gene Expression Microarray data using Evolutionary Approach
Authors: Shreya Mishra, Swati Vipsita
Date: 2018-05-14 00:00:00
Tasks: Clustering

In Tri-clustering, a sub-matrix is being created, which exhibit highly
similar behavior with respect to genes, conditions and time-points. In this
technique, genes with same expression values are discovered across some
fragment of time points, under certain conditions. In this paper, triclustering
using evolutionary algorithm is implemented using a new fitness function
consisting of 3D Mean Square residue (MSR) and Least Square approximation
(LSL). The primary objective is to find triclusters with minimum overlapping,
low MSR, low LSL and covering almost every element of expression matrix, thus
minimizing the overall fitness value. To improve the results of algorithm, new
fitness function is introduced to find good quality triclusters. It is observed
from experiments that, triclustering using EA yielded good quality triclusters.
The experiment was implemented on yeast Saccharomyces dataset. Index
Terms-Tri-clustering, Genetic Algorithm, Mean squared residue, Volume, Weights,
Least square approximation.
Arbitrary Style Transfer with Deep Feature Reshuffle
Authors: Shuyang Gu, Congliang Chen, Jing Liao, Lu Yuan
Date: 2018-05-10 00:00:00
Proceeding: CVPR 2018 6
Tasks: Style Transfer

This paper introduces a novel method by reshuffling deep features (i.e.,
permuting the spacial locations of a feature map) of the style image for
arbitrary style transfer. We theoretically prove that our new style loss based
on reshuffle connects both global and local style losses respectively used by
most parametric and non-parametric neural style transfer methods. This simple
idea can effectively address the challenging issues in existing style transfer
methods. On one hand, it can avoid distortions in local style patterns, and
allow semantic-level transfer, compared with neural parametric methods. On the
other hand, it can preserve globally similar appearance to the style image, and
avoid wash-out artifacts, compared with neural non-parametric methods. Based on
the proposed loss, we also present a progressive feature-domain optimization
approach. The experiments show that our method is widely applicable to various
styles, and produces better quality than existing methods.
A Deep Learning Approach with an Attention Mechanism for Automatic Sleep Stage Classification
Authors: Martin Längkvist, Amy Loutfi
Date: 2018-05-14 00:00:00
Tasks: Automatic Sleep Stage Classification, Dimensionality Reduction, General Classification, Sleep Staging

Automatic sleep staging is a challenging problem and state-of-the-art
algorithms have not yet reached satisfactory performance to be used instead of
manual scoring by a sleep technician. Much research has been done to find good
feature representations that extract the useful information to correctly
classify each epoch into the correct sleep stage. While many useful features
have been discovered, the amount of features have grown to an extent that a
feature reduction step is necessary in order to avoid the curse of
dimensionality. One reason for the need of such a large feature set is that
many features are good for discriminating only one of the sleep stages and are
less informative during other stages. This paper explores how a second feature
representation over a large set of pre-defined features can be learned using an
auto-encoder with a selective attention for the current sleep stage in the
training batch. This selective attention allows the model to learn feature
representations that focuses on the more relevant inputs without having to
perform any dimensionality reduction of the input data. The performance of the
proposed algorithm is evaluated on a large data set of polysomnography (PSG)
night recordings of patients with sleep-disordered breathing. The performance
of the auto-encoder with selective attention is compared with a regular
auto-encoder and previous works using a deep belief network (DBN).
A One-Class Classification Decision Tree Based on Kernel Density Estimation
Authors: Sarah Itani, Fabian Lecron, Philippe Fortemps
Date: 2018-05-14 00:00:00
Tasks: Density Estimation, General Classification, One-Class Classification

One-class Classification (OCC) is an area of machine learning which addresses prediction based on unbalanced datasets. Basically, OCC algorithms achieve training by means of a single class sample, with potentially some additional counter-examples. The current OCC models give satisfaction in terms of performance, but there is an increasing need for the development of interpretable models. In the present work, we propose a one-class model which addresses concerns of both performance and interpretability. Our hybrid OCC method relies on density estimation as part of a tree-based learning algorithm, called One-Class decision Tree (OC-Tree). Within a greedy and recursive approach, our proposal rests on kernel density estimation to split a data subset on the basis of one or several intervals of interest. Thus, the OC-Tree encloses data within hyper-rectangles of interest which can be described by a set of rules. Against state-of-the-art methods such as Cluster Support Vector Data Description (ClusterSVDD), One-Class Support Vector Machine (OCSVM) and isolation Forest (iForest), the OC-Tree performs favorably on a range of benchmark datasets. Furthermore, we propose a real medical application for which the OC-Tree has demonstrated its effectiveness, through the ability to tackle interpretable diagnosis aid based on unbalanced datasets.
Learning Dual Convolutional Neural Networks for Low-Level Vision
Authors: Jinshan Pan, Sifei Liu, Deqing Sun, Jiawei Zhang, Yang Liu, Jimmy Ren, Zechao Li, Jinhui Tang, Huchuan Lu, Yu-Wing Tai, Ming-Hsuan Yang
Date: 2018-05-14 00:00:00
Proceeding: CVPR 2018 6
Tasks: Rain Removal, Super-Resolution

In this paper, we propose a general dual convolutional neural network
(DualCNN) for low-level vision problems, e.g., super-resolution,
edge-preserving filtering, deraining and dehazing. These problems usually
involve the estimation of two components of the target signals: structures and
details. Motivated by this, our proposed DualCNN consists of two parallel
branches, which respectively recovers the structures and details in an
end-to-end manner. The recovered structures and details can generate the target
signals according to the formation model for each particular application. The
DualCNN is a flexible framework for low-level vision tasks and can be easily
incorporated into existing CNNs. Experimental results show that the DualCNN can
be effectively applied to numerous low-level vision tasks with favorable
performance against the state-of-the-art methods.
Improved Training of Generative Adversarial Networks Using Representative Features
Authors: Duhyeon Bang, Hyunjung Shim
Date: 2018-01-28 00:00:00
Proceeding: ICML 2018 7
Tasks: Diversity, Image Generation

Despite the success of generative adversarial networks (GANs) for image
generation, the trade-off between visual quality and image diversity remains a
significant issue. This paper achieves both aims simultaneously by improving
the stability of training GANs. The key idea of the proposed approach is to
implicitly regularize the discriminator using representative features. Focusing
on the fact that standard GAN minimizes reverse Kullback-Leibler (KL)
divergence, we transfer the representative feature, which is extracted from the
data distribution using a pre-trained autoencoder (AE), to the discriminator of
standard GANs. Because the AE learns to minimize forward KL divergence, our GAN
training with representative features is influenced by both reverse and forward
KL divergence. Consequently, the proposed approach is verified to improve
visual quality and diversity of state of the art GANs using extensive
evaluations.
Training Recurrent Neural Networks as a Constraint Satisfaction Problem
Authors: Hamid Khodabandehlou, M. Sami Fadali
Date: 2018-03-20 00:00:00

This paper presents a new approach for training artificial neural networks
using techniques for solving the constraint satisfaction problem (CSP). The
quotient gradient system (QGS) is a trajectory-based method for solving the
CSP. This study converts the training set of a neural network into a CSP and
uses the QGS to find its solutions. The QGS finds the global minimum of the
optimization problem by tracking trajectories of a nonlinear dynamical system
and does not stop at a local minimum of the optimization problem. Lyapunov
theory is used to prove the asymptotic stability of the solutions with and
without the presence of measurement errors. Numerical examples illustrate the
effectiveness of the proposed methodology and compare it to a genetic algorithm
and error backpropagation.
Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers
Authors: Fred Hohman, Minsuk Kahng, Robert Pienta, Duen Horng Chau
Date: 2018-01-21 00:00:00
Tasks: Decision Making, Deep Learning, Survey

Deep learning has recently seen rapid development and received significant
attention due to its state-of-the-art performance on previously-thought hard
problems. However, because of the internal complexity and nonlinear structure
of deep neural networks, the underlying decision making processes for why these
models are achieving such performance are challenging and sometimes mystifying
to interpret. As deep learning spreads across domains, it is of paramount
importance that we equip users of deep learning with tools for understanding
when a model works correctly, when it fails, and ultimately how to improve its
performance. Standardized toolkits for building neural networks have helped
democratize deep learning; visual analytics systems have now been developed to
support model explanation, interpretation, debugging, and improvement. We
present a survey of the role of visual analytics in deep learning research,
which highlights its short yet impactful history and thoroughly summarizes the
state-of-the-art using a human-centered interrogative framework, focusing on
the Five W's and How (Why, Who, What, How, When, and Where). We conclude by
highlighting research directions and open research problems. This survey helps
researchers and practitioners in both visual analytics and deep learning to
quickly learn key aspects of this young and rapidly growing body of research,
whose impact spans a diverse range of domains.
Mini-Batch Stochastic ADMMs for Nonconvex Nonsmooth Optimization
Authors: Feihu Huang, Songcan Chen
Date: 2018-02-08 00:00:00

With the large rising of complex data, the nonconvex models such as nonconvex loss function and nonconvex regularizer are widely used in machine learning and pattern recognition. In this paper, we propose a class of mini-batch stochastic ADMMs (alternating direction method of multipliers) for solving large-scale nonconvex nonsmooth problems. We prove that, given an appropriate mini-batch size, the mini-batch stochastic ADMM without variance reduction (VR) technique is convergent and reaches a convergence rate of $O(1/T)$ to obtain a stationary point of the nonconvex optimization, where $T$ denotes the number of iterations. Moreover, we extend the mini-batch stochastic gradient method to both the nonconvex SVRG-ADMM and SAGA-ADMM proposed in our initial manuscript \cite{huang2016stochastic}, and prove these mini-batch stochastic ADMMs also reaches the convergence rate of $O(1/T)$ without condition on the mini-batch size. In particular, we provide a specific parameter selection for step size $\eta$ of stochastic gradients and penalty parameter $\rho$ of augmented Lagrangian function. Finally, extensive experimental results on both simulated and real-world data demonstrate the effectiveness of the proposed algorithms.
Replicating Active Appearance Model by Generator Network
Authors: Tian Han, Jiawen Wu, Ying Nian Wu
Date: 2018-05-14 00:00:00
Tasks: Face Recognition, model

A recent Cell paper [Chang and Tsao, 2017] reports an interesting discovery.
For the face stimuli generated by a pre-trained active appearance model (AAM),
the responses of neurons in the areas of the primate brain that are responsible
for face recognition exhibit strong linear relationship with the shape
variables and appearance variables of the AAM that generates the face stimuli.
In this paper, we show that this behavior can be replicated by a deep
generative model called the generator network, which assumes that the observed
signals are generated by latent random variables via a top-down convolutional
neural network. Specifically, we learn the generator network from the face
images generated by a pre-trained AAM model using variational auto-encoder, and
we show that the inferred latent variables of the learned generator network
have strong linear relationship with the shape and appearance variables of the
AAM model that generates the face images. Unlike the AAM model that has an
explicit shape model where the shape variables generate the control points or
landmarks, the generator network has no such shape model and shape variables.
Yet the generator network can learn the shape knowledge in the sense that some
of the latent variables of the learned generator network capture the shape
variations in the face images generated by AAM.
Exact alignment recovery for correlated Erdős-Rényi graphs
Authors: Daniel Cullina, Negar Kiyavash
Date: 2017-11-18 00:00:00

We consider the problem of perfectly recovering the vertex correspondence
between two correlated Erd\H{o}s-R\'enyi (ER) graphs on the same vertex set.
The correspondence between the vertices can be obscured by randomly permuting
the vertex labels of one of the graphs. We determine the information-theoretic
threshold for exact recovery, i.e. the conditions under which the entire vertex
correspondence can be correctly recovered given unbounded computational
resources.
Deep Decision Trees for Discriminative Dictionary Learning with Adversarial Multi-Agent Trajectories
Authors: Tharindu Fernando, Sridha Sridharan, Clinton Fookes, Simon Denman
Date: 2018-05-14 00:00:00
Tasks: Clustering, Dictionary Learning

With the explosion in the availability of spatio-temporal tracking data in
modern sports, there is an enormous opportunity to better analyse, learn and
predict important events in adversarial group environments. In this paper, we
propose a deep decision tree architecture for discriminative dictionary
learning from adversarial multi-agent trajectories. We first build up a
hierarchy for the tree structure by adding each layer and performing feature
weight based clustering in the forward pass. We then fine tune the player role
weights using back propagation. The hierarchical architecture ensures the
interpretability and the integrity of the group representation. The resulting
architecture is a decision tree, with leaf-nodes capturing a dictionary of
multi-agent group interactions. Due to the ample volume of data available, we
focus on soccer tracking data, although our approach can be used in any
adversarial multi-agent domain. We present applications of proposed method for
simulating soccer games as well as evaluating and quantifying team strategies.
Evaluating Hospital Case Cost Prediction Models Using Azure Machine Learning Studio
Authors: Alexei Botchkarev
Date: 2018-04-04 00:00:00
Tasks: BIG-bench Machine Learning, Gaussian Processes, Management, regression

Ability for accurate hospital case cost modelling and prediction is critical
for efficient health care financial management and budgetary planning. A
variety of regression machine learning algorithms are known to be effective for
health care cost predictions. The purpose of this experiment was to build an
Azure Machine Learning Studio tool for rapid assessment of multiple types of
regression models. The tool offers environment for comparing 14 types of
regression models in a unified experiment: linear regression, Bayesian linear
regression, decision forest regression, boosted decision tree regression,
neural network regression, Poisson regression, Gaussian processes for
regression, gradient boosted machine, nonlinear least squares regression,
projection pursuit regression, random forest regression, robust regression,
robust regression with mm-type estimators, support vector regression. The tool
presents assessment results arranged by model accuracy in a single table using
five performance metrics. Evaluation of regression machine learning models for
performing hospital case cost prediction demonstrated advantage of robust
regression model, boosted decision tree regression and decision forest
regression. The operational tool has been published to the web and openly
available for experiments and extensions.
Boosting up Scene Text Detectors with Guided CNN
Authors: Xiaoyu Yue, Zhanghui Kuang, Zhaoyang Zhang, Zhenfang Chen, Pan He, Yu Qiao, Wei zhang
Date: 2018-05-10 00:00:00
Tasks: Text Detection

Deep CNNs have achieved great success in text detection. Most of existing
methods attempt to improve accuracy with sophisticated network design, while
paying less attention on speed. In this paper, we propose a general framework
for text detection called Guided CNN to achieve the two goals simultaneously.
The proposed model consists of one guidance subnetwork, where a guidance mask
is learned from the input image itself, and one primary text detector, where
every convolution and non-linear operation are conducted only in the guidance
mask. On the one hand, the guidance subnetwork filters out non-text regions
coarsely, greatly reduces the computation complexity. On the other hand, the
primary text detector focuses on distinguishing between text and hard non-text
regions and regressing text bounding boxes, achieves a better detection
accuracy. A training strategy, called background-aware block-wise random
synthesis, is proposed to further boost up the performance. We demonstrate that
the proposed Guided CNN is not only effective but also efficient with two
state-of-the-art methods, CTPN and EAST, as backbones. On the challenging
benchmark ICDAR 2013, it speeds up CTPN by 2.9 times on average, while
improving the F-measure by 1.5%. On ICDAR 2015, it speeds up EAST by 2.0 times
while improving the F-measure by 1.0%.
A Multi-Axis Annotation Scheme for Event Temporal Relations
Authors: Qiang Ning, Hao Wu, Dan Roth
Date: 2018-04-20 00:00:00
Proceeding: ACL 2018 7

Existing temporal relation (TempRel) annotation schemes often have low
inter-annotator agreements (IAA) even between experts, suggesting that the
current annotation task needs a better definition. This paper proposes a new
multi-axis modeling to better capture the temporal structure of events. In
addition, we identify that event end-points are a major source of confusion in
annotation, so we also propose to annotate TempRels based on start-points only.
A pilot expert annotation using the proposed scheme shows significant
improvement in IAA from the conventional 60's to 80's (Cohen's Kappa). This
better-defined annotation scheme further enables the use of crowdsourcing to
alleviate the labor intensity for each annotator. We hope that this work can
foster more interesting studies towards event understanding.
A Multi-Discriminator CycleGAN for Unsupervised Non-Parallel Speech Domain Adaptation
Authors: Ehsan Hosseini-Asl, Yingbo Zhou, Caiming Xiong, Richard Socher
Date: 2018-03-27 00:00:00
Tasks: Domain Adaptation, Generative Adversarial Network, speech-recognition, Speech Recognition

Domain adaptation plays an important role for speech recognition models, in
particular, for domains that have low resources. We propose a novel generative
model based on cyclic-consistent generative adversarial network (CycleGAN) for
unsupervised non-parallel speech domain adaptation. The proposed model employs
multiple independent discriminators on the power spectrogram, each in charge of
different frequency bands. As a result we have 1) better discriminators that
focus on fine-grained details of the frequency features, and 2) a generator
that is capable of generating more realistic domain-adapted spectrogram. We
demonstrate the effectiveness of our method on speech recognition with gender
adaptation, where the model only has access to supervised data from one gender
during training, but is evaluated on the other at test time. Our model is able
to achieve an average of $7.41\%$ on phoneme error rate, and $11.10\%$ word
error rate relative performance improvement as compared to the baseline, on
TIMIT and WSJ dataset, respectively. Qualitatively, our model also generates
more natural sounding speech, when conditioned on data from the other domain.
Discourse Coherence in the Wild: A Dataset, Evaluation and Methods
Authors: Alice Lai, Joel Tetreault
Date: 2018-05-14 00:00:00
Proceeding: WS 2018 7
Tasks: Coherence Evaluation

To date there has been very little work on assessing discourse coherence
methods on real-world data. To address this, we present a new corpus of
real-world texts (GCDC) as well as the first large-scale evaluation of leading
discourse coherence algorithms. We show that neural models, including two that
we introduce here (SentAvg and ParSeq), tend to perform best. We analyze these
performance differences and discuss patterns we observed in low coherence texts
in four domains.