pwc-archive/papers-with-abstracts huggingface dataset:
Game-Theoretic Design of Secure and Resilient Distributed Support Vector Machines with Adversaries
Authors: Rui Zhang, Quanyan Zhu
Date: 2017-10-12 00:00:00

With a large number of sensors and control units in networked systems,
distributed support vector machines (DSVMs) play a fundamental role in scalable
and efficient multi-sensor classification and prediction tasks. However, DSVMs
are vulnerable to adversaries who can modify and generate data to deceive the
system to misclassification and misprediction. This work aims to design defense
strategies for DSVM learner against a potential adversary. We establish a
game-theoretic framework to capture the conflicting interests between the DSVM
learner and the attacker. The Nash equilibrium of the game allows predicting
the outcome of learning algorithms in adversarial environments, and enhancing
the resilience of the machine learning through dynamic distributed learning
algorithms. We show that the DSVM learner is less vulnerable when he uses a
balanced network with fewer nodes and higher degree. We also show that adding
more training samples is an efficient defense strategy against an attacker. We
present secure and resilient DSVM algorithms with verification method and
rejection method, and show their resiliency against adversary with numerical
experiments.
Progressive Representation Adaptation for Weakly Supervised Object Localization
Authors: Dong Li, Jia-Bin Huang, Ya-Li Li, Shengjin Wang, Ming-Hsuan Yang
Date: 2017-10-12 00:00:00
Tasks: Classification, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Multiple Instance Learning, Object, Object Localization, Weakly-Supervised Object Localization

We address the problem of weakly supervised object localization where only
image-level annotations are available for training object detectors. Numerous
methods have been proposed to tackle this problem through mining object
proposals. However, a substantial amount of noise in object proposals causes
ambiguities for learning discriminative object models. Such approaches are
sensitive to model initialization and often converge to undesirable local
minimum solutions. In this paper, we propose to overcome these drawbacks by
progressive representation adaptation with two main steps: 1) classification
adaptation and 2) detection adaptation. In classification adaptation, we
transfer a pre-trained network to a multi-label classification task for
recognizing the presence of a certain object in an image. Through the
classification adaptation step, the network learns discriminative
representations that are specific to object categories of interest. In
detection adaptation, we mine class-specific object proposals by exploiting two
scoring strategies based on the adapted classification network. Class-specific
proposal mining helps remove substantial noise from the background clutter and
potential confusion from similar objects. We further refine these proposals
using multiple instance learning and segmentation cues. Using these refined
object bounding boxes, we fine-tune all the layer of the classification network
and obtain a fully adapted detection network. We present detailed experimental
validation on the PASCAL VOC and ILSVRC datasets. Experimental results
demonstrate that our progressive representation adaptation algorithm performs
favorably against the state-of-the-art methods.
Analysis of planar ornament patterns via motif asymmetry assumption and local connections
Authors: Venera Adanova, Sibel Tari
Date: 2017-10-12 00:00:00

Planar ornaments, a.k.a. wallpapers, are regular repetitive patterns which
exhibit translational symmetry in two independent directions. There are exactly
$17$ distinct planar symmetry groups. We present a fully automatic method for
complete analysis of planar ornaments in $13$ of these groups, specifically,
the groups called $p6m, \, p6, \, p4g, \,p4m, \,p4, \, p31m, \,p3m, \, p3, \,
cmm, \, pgg, \, pg, \, p2$ and $p1$. Given the image of an ornament fragment,
we present a method to simultaneously classify the input into one of the $13$
groups and extract the so called fundamental domain (FD), the minimum region
that is sufficient to reconstruct the entire ornament. A nice feature of our
method is that even when the given ornament image is a small portion such that
it does not contain multiple translational units, the symmetry group as well as
the fundamental domain can still be defined. This is because, in contrast to
common approach, we do not attempt to first identify a global translational
repetition lattice. Though the presented constructions work for quite a wide
range of ornament patterns, a key assumption we make is that the perceivable
motifs (shapes that repeat) alone do not provide clues for the underlying
symmetries of the ornament. In this sense, our main target is the planar
arrangements of asymmetric interlocking shapes, as in the symmetry art of
Escher.
On orthogonality and learning recurrent networks with long term dependencies
Authors: Eugene Vorontsov, Chiheb Trabelsi, Samuel Kadoury, Chris Pal
Date: 2017-01-31 00:00:00

It is well known that it is challenging to train deep neural networks and
recurrent neural networks for tasks that exhibit long term dependencies. The
vanishing or exploding gradient problem is a well known issue associated with
these challenges. One approach to addressing vanishing and exploding gradients
is to use either soft or hard constraints on weight matrices so as to encourage
or enforce orthogonality. Orthogonal matrices preserve gradient norm during
backpropagation and may therefore be a desirable property. This paper explores
issues with optimization convergence, speed and gradient stability when
encouraging or enforcing orthogonality. To perform this analysis, we propose a
weight matrix factorization and parameterization strategy through which we can
bound matrix norms and therein control the degree of expansivity induced during
backpropagation. We find that hard constraints on orthogonality can negatively
affect the speed of convergence and model performance.
Multiplicative LSTM for sequence modelling
Authors: Ben Krause, Liang Lu, Iain Murray, Steve Renals
Date: 2016-09-26 00:00:00
Tasks: Density Estimation, Language Modelling

We introduce multiplicative LSTM (mLSTM), a recurrent neural network
architecture for sequence modelling that combines the long short-term memory
(LSTM) and multiplicative recurrent neural network architectures. mLSTM is
characterised by its ability to have different recurrent transition functions
for each possible input, which we argue makes it more expressive for
autoregressive density estimation. We demonstrate empirically that mLSTM
outperforms standard LSTM and its deep variants for a range of character level
language modelling tasks. In this version of the paper, we regularise mLSTM to
achieve 1.27 bits/char on text8 and 1.24 bits/char on Hutter Prize. We also
apply a purely byte-level mLSTM on the WikiText-2 dataset to achieve a
character level entropy of 1.26 bits/char, corresponding to a word level
perplexity of 88.8, which is comparable to word level LSTMs regularised in
similar ways on the same task.
Auto Analysis of Customer Feedback using CNN and GRU Network
Authors: Deepak Gupta, Pabitra Lenka, Harsimran Bedi, Asif Ekbal, Pushpak Bhattacharyya
Date: 2017-10-12 00:00:00
Tasks: Marketing

Analyzing customer feedback is the best way to channelize the data into new
marketing strategies that benefit entrepreneurs as well as customers. Therefore
an automated system which can analyze the customer behavior is in great demand.
Users may write feedbacks in any language, and hence mining appropriate
information often becomes intractable. Especially in a traditional
feature-based supervised model, it is difficult to build a generic system as
one has to understand the concerned language for finding the relevant features.
In order to overcome this, we propose deep Convolutional Neural Network (CNN)
and Recurrent Neural Network (RNN) based approaches that do not require
handcrafting of features. We evaluate these techniques for analyzing customer
feedback sentences in four languages, namely English, French, Japanese and
Spanish. Our empirical analysis shows that our models perform well in all the
four languages on the setups of IJCNLP Shared Task on Customer Feedback
Analysis. Our model achieved the second rank in French, with an accuracy of
71.75% and third ranks for all the other languages.
Is Epicurus the father of Reinforcement Learning?
Authors: Eleni Vasilaki
Date: 2017-10-12 00:00:00
Tasks: Philosophy, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The Epicurean Philosophy is commonly thought as simplistic and hedonistic.
Here I discuss how this is a misconception and explore its link to
Reinforcement Learning. Based on the letters of Epicurus, I construct an
objective function for hedonism which turns out to be equivalent of the
Reinforcement Learning objective function when omitting the discount factor. I
then discuss how Plato and Aristotle 's views that can be also loosely linked
to Reinforcement Learning, as well as their weaknesses in relationship to it.
Finally, I emphasise the close affinity of the Epicurean views and the Bellman
equation.
Additivity of Information in Multilayer Networks via Additive Gaussian Noise Transforms
Authors: Galen Reeves
Date: 2017-10-12 00:00:00

Multilayer (or deep) networks are powerful probabilistic models based on
multiple stages of a linear transform followed by a non-linear (possibly
random) function. In general, the linear transforms are defined by matrices and
the non-linear functions are defined by information channels. These models have
gained great popularity due to their ability to characterize complex
probabilistic relationships arising in a wide variety of inference problems.
The contribution of this paper is a new method for analyzing the fundamental
limits of statistical inference in settings where the model is known. The
validity of our method can be established in a number of settings and is
conjectured to hold more generally. A key assumption made throughout is that
the matrices are drawn randomly from orthogonally invariant distributions.
  Our method yields explicit formulas for 1) the mutual information; 2) the
minimum mean-squared error (MMSE); 3) the existence and locations of certain
phase-transitions with respect to the problem parameters; and 4) the stationary
points for the state evolution of approximate message passing algorithms. When
applied to the special case of models with multivariate Gaussian channels our
method is rigorous and has close connections to free probability theory for
random matrices. When applied to the general case of non-Gaussian channels, our
method provides a simple alternative to the replica method from statistical
physics. A key observation is that the combined effects of the individual
components in the model (namely the matrices and the channels) are additive
when viewed in a certain transform domain.
CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training
Authors: Jianmin Bao, Dong Chen, Fang Wen, Houqiang Li, Gang Hua
Date: 2017-03-29 00:00:00
Proceeding: ICCV 2017 10
Tasks: Attribute, Data Augmentation, Face Recognition, Generative Adversarial Network, Image Generation, Image Inpainting, Super-Resolution

We present variational generative adversarial networks, a general learning
framework that combines a variational auto-encoder with a generative
adversarial network, for synthesizing images in fine-grained categories, such
as faces of a specific person or objects in a category. Our approach models an
image as a composition of label and latent attributes in a probabilistic model.
By varying the fine-grained category label fed into the resulting generative
model, we can generate images in a specific category with randomly drawn values
on a latent attribute vector. Our approach has two novel aspects. First, we
adopt a cross entropy loss for the discriminative and classifier network, but a
mean discrepancy objective for the generative network. This kind of asymmetric
loss function makes the GAN training more stable. Second, we adopt an encoder
network to learn the relationship between the latent space and the real image
space, and use pairwise feature matching to keep the structure of generated
images. We experiment with natural images of faces, flowers, and birds, and
demonstrate that the proposed models are capable of generating realistic and
diverse samples with fine-grained category labels. We further show that our
models can be applied to other tasks, such as image inpainting,
super-resolution, and data augmentation for training better face recognition
models.
New efficient algorithms for multiple change-point detection with kernels
Authors: Alain Celisse, Guillemette Marot, Morgane Pierre-Jean, Guillem Rigaill
Date: 2017-10-12 00:00:00
Tasks: Change Point Detection

Several statistical approaches based on reproducing kernels have been
proposed to detect abrupt changes arising in the full distribution of the
observations and not only in the mean or variance. Some of these approaches
enjoy good statistical properties (oracle inequality, \ldots). Nonetheless,
they have a high computational cost both in terms of time and memory. This
makes their application difficult even for small and medium sample sizes ($n<
10^4$). This computational issue is addressed by first describing a new
efficient and exact algorithm for kernel multiple change-point detection with
an improved worst-case complexity that is quadratic in time and linear in
space. It allows dealing with medium size signals (up to $n \approx 10^5$).
Second, a faster but approximation algorithm is described. It is based on a
low-rank approximation to the Gram matrix. It is linear in time and space. This
approximation algorithm can be applied to large-scale signals ($n \geq 10^6$).
These exact and approximation algorithms have been implemented in \texttt{R}
and \texttt{C} for various kernels. The computational and statistical
performances of these new algorithms have been assessed through empirical
experiments. The runtime of the new algorithms is observed to be faster than
that of other considered procedures. Finally, simulations confirmed the higher
statistical accuracy of kernel-based approaches to detect changes that are not
only in the mean. These simulations also illustrate the flexibility of
kernel-based approaches to analyze complex biological profiles made of DNA copy
number and allele B frequencies. An R package implementing the approach will be
made available on github.
Hierarchical Convolutional-Deconvolutional Neural Networks for Automatic Liver and Tumor Segmentation
Authors: Yading Yuan
Date: 2017-10-12 00:00:00
Tasks: Automatic Liver And Tumor Segmentation, Liver Segmentation, Prognosis, Segmentation, Tumor Segmentation

Automatic segmentation of liver and its tumors is an essential step for
extracting quantitative imaging biomarkers for accurate tumor detection,
diagnosis, prognosis and assessment of tumor response to treatment. MICCAI 2017
Liver Tumor Segmentation Challenge (LiTS) provides a common platform for
comparing different automatic algorithms on contrast-enhanced abdominal CT
images in tasks including 1) liver segmentation, 2) liver tumor segmentation,
and 3) tumor burden estimation. We participate this challenge by developing a
hierarchical framework based on deep fully convolutional-deconvolutional neural
networks (CDNN). A simple CDNN model is firstly trained to provide a quick but
coarse segmentation of the liver on the entire CT volume, then another CDNN is
applied to the liver region for fine liver segmentation. At last, the segmented
liver region, which is enhanced by histogram equalization, is employed as an
additional input to the third CDNN for tumor segmentation. Jaccard distance is
used as loss function when training CDNN models to eliminate the need of sample
re-weighting. Our framework is trained using the 130 challenge training cases
provided by LiTS. The evaluation on the 70 challenge testing cases resulted in
a mean Dice Similarity Coefficient (DSC) of 0.963 for liver segmentation, a
mean DSC of 0.657 for tumor segmentation, and a root mean square error (RMSE)
of 0.017 for tumor burden estimation, which ranked our method in the first,
fifth and third place, respectively
Subjectively Interesting Subgroup Discovery on Real-valued Targets
Authors: Jefrey Lijffijt, Bo Kang, Wouter Duivesteijn, Kai Puolamäki, Emilia Oikarinen, Tijl De Bie
Date: 2017-10-12 00:00:00
Tasks: Subgroup Discovery

Deriving insights from high-dimensional data is one of the core problems in
data mining. The difficulty mainly stems from the fact that there are
exponentially many variable combinations to potentially consider, and there are
infinitely many if we consider weighted combinations, even for linear
combinations. Hence, an obvious question is whether we can automate the search
for interesting patterns and visualizations. In this paper, we consider the
setting where a user wants to learn as efficiently as possible about
real-valued attributes. For example, to understand the distribution of crime
rates in different geographic areas in terms of other (numerical, ordinal
and/or categorical) variables that describe the areas. We introduce a method to
find subgroups in the data that are maximally informative (in the formal
Information Theoretic sense) with respect to a single or set of real-valued
target attributes. The subgroup descriptions are in terms of a succinct set of
arbitrarily-typed other attributes. The approach is based on the Subjective
Interestingness framework FORSIED to enable the use of prior knowledge when
finding most informative non-redundant patterns, and hence the method also
supports iterative data mining.
Convolutional Attention-based Seq2Seq Neural Network for End-to-End ASR
Authors: Dan Lim
Date: 2017-10-12 00:00:00
Tasks: speech-recognition, Speech Recognition

This thesis introduces the sequence to sequence model with Luong's attention
mechanism for end-to-end ASR. It also describes various neural network
algorithms including Batch normalization, Dropout and Residual network which
constitute the convolutional attention-based seq2seq neural network. Finally
the proposed model proved its effectiveness for speech recognition achieving
15.8% phoneme error rate on TIMIT dataset.
Multimodal Observation and Interpretation of Subjects Engaged in Problem Solving
Authors: Thomas Guntz, Raffaella Balzarini, Dominique Vaufreydaz, James L. Crowley
Date: 2017-10-12 00:00:00

In this paper we present the first results of a pilot experiment in the
capture and interpretation of multimodal signals of human experts engaged in
solving challenging chess problems. Our goal is to investigate the extent to
which observations of eye-gaze, posture, emotion and other physiological
signals can be used to model the cognitive state of subjects, and to explore
the integration of multiple sensor modalities to improve the reliability of
detection of human displays of awareness and emotion. We observed chess players
engaged in problems of increasing difficulty while recording their behavior.
Such recordings can be used to estimate a participant's awareness of the
current situation and to predict ability to respond effectively to challenging
situations. Results show that a multimodal approach is more accurate than a
unimodal one. By combining body posture, visual attention and emotion, the
multimodal approach can reach up to 93% of accuracy when determining player's
chess expertise while unimodal approach reaches 86%. Finally this experiment
validates the use of our equipment as a general and reproducible tool for the
study of participants engaged in screen-based interaction and/or problem
solving.
VOIDD: automatic vessel of intervention dynamic detection in PCI procedures
Authors: Ketan Bacchuwar, Jean Cousty, Régis Vaillant, Laurent Najman
Date: 2017-10-12 00:00:00

In this article, we present the work towards improving the overall workflow
of the Percutaneous Coronary Interventions (PCI) procedures by capacitating the
imaging instruments to precisely monitor the steps of the procedure. In the
long term, such capabilities can be used to optimize the image acquisition to
reduce the amount of dose or contrast media employed during the procedure. We
present the automatic VOIDD algorithm to detect the vessel of intervention
which is going to be treated during the procedure by combining information from
the vessel image with contrast agent injection and images acquired during
guidewire tip navigation. Due to the robust guidewire tip segmentation method,
this algorithm is also able to automatically detect the sequence corresponding
to guidewire navigation. We present an evaluation methodology which
characterizes the correctness of the guide wire tip detection and correct
identification of the vessel navigated during the procedure. On a dataset of
2213 images from 8 sequences of 4 patients, VOIDD identifies
vessel-of-intervention with accuracy in the range of 88% or above and absence
of tip with accuracy in range of 98% or above depending on the test case.
Adapting general-purpose speech recognition engine output for domain-specific natural language question answering
Authors: C. Anantaram, Sunil Kumar Kopparapu
Date: 2017-10-12 00:00:00
Tasks: Question Answering, Sentence, speech-recognition, Speech Recognition

Speech-based natural language question-answering interfaces to enterprise
systems are gaining a lot of attention. General-purpose speech engines can be
integrated with NLP systems to provide such interfaces. Usually,
general-purpose speech engines are trained on large `general' corpus. However,
when such engines are used for specific domains, they may not recognize
domain-specific words well, and may produce erroneous output. Further, the
accent and the environmental conditions in which the speaker speaks a sentence
may induce the speech engine to inaccurately recognize certain words. The
subsequent natural language question-answering does not produce the requisite
results as the question does not accurately represent what the speaker
intended. Thus, the speech engine's output may need to be adapted for a domain
before further natural language processing is carried out. We present two
mechanisms for such an adaptation, one based on evolutionary development and
the other based on machine learning, and show how we can repair the
speech-output to make the subsequent natural language question-answering
better.
The Social Bow Tie
Authors: Heather Mattie, Kenth Engø-Monsen, Rich Ling, Jukka-Pekka Onnela
Date: 2017-10-11 00:00:00

Understanding tie strength in social networks, and the factors that influence
it, have received much attention in a myriad of disciplines for decades.
Several models incorporating indicators of tie strength have been proposed and
used to quantify relationships in social networks, and a standard set of
structural network metrics have been applied to predominantly online social
media sites to predict tie strength. Here, we introduce the concept of the
"social bow tie" framework, a small subgraph of the network that consists of a
collection of nodes and ties that surround a tie of interest, forming a
topological structure that resembles a bow tie. We also define several
intuitive and interpretable metrics that quantify properties of the bow tie. We
use random forests and regression models to predict categorical and continuous
measures of tie strength from different properties of the bow tie, including
nodal attributes. We also investigate what aspects of the bow tie are most
predictive of tie strength in two distinct social networks: a collection of 75
rural villages in India and a nationwide call network of European mobile phone
users. Our results indicate several of the bow tie metrics are highly
predictive of tie strength, and we find the more the social circles of two
individuals overlap, the stronger their tie, consistent with previous findings.
However, we also find that the more tightly-knit their non-overlapping social
circles, the weaker the tie. This new finding complements our current
understanding of what drives the strength of ties in social networks.