pwc-archive/papers-with-abstracts huggingface dataset:
Bayesian Recurrent Neural Networks
Authors: Meire Fortunato, Charles Blundell, Oriol Vinyals
Date: 2017-04-10 00:00:00
Tasks: Image Captioning, Language Modelling

In this work we explore a straightforward variational Bayes scheme for Recurrent Neural Networks. Firstly, we show that a simple adaptation of truncated backpropagation through time can yield good quality uncertainty estimates and superior regularisation at only a small extra computational cost during training, also reducing the amount of parameters by 80\%. Secondly, we demonstrate how a novel kind of posterior approximation yields further improvements to the performance of Bayesian RNNs. We incorporate local gradient information into the approximate posterior to sharpen it around the current batch statistics. We show how this technique is not exclusive to recurrent neural networks and can be applied more widely to train Bayesian neural networks. We also empirically demonstrate how Bayesian RNNs are superior to traditional RNNs on a language modelling benchmark and an image captioning task, as well as showing how each of these methods improve our model over a variety of other schemes for training them. We also introduce a new benchmark for studying uncertainty for language models so future methods can be easily compared.
Reservoir computing approaches for representation and classification of multivariate time series
Authors: Filippo Maria Bianchi, Simone Scardapane, Sigurd Løkse, Robert Jenssen
Date: 2018-03-21 00:00:00
Tasks: Dimensionality Reduction, General Classification, Time Series, Time Series Analysis

Classification of multivariate time series (MTS) has been tackled with a large variety of methodologies and applied to a wide range of scenarios. Reservoir Computing (RC) provides efficient tools to generate a vectorial, fixed-size representation of the MTS that can be further processed by standard classifiers. Despite their unrivaled training speed, MTS classifiers based on a standard RC architecture fail to achieve the same accuracy of fully trainable neural networks. In this paper we introduce the reservoir model space, an unsupervised approach based on RC to learn vectorial representations of MTS. Each MTS is encoded within the parameters of a linear model trained to predict a low-dimensional embedding of the reservoir dynamics. Compared to other RC methods, our model space yields better representations and attains comparable computational performance, thanks to an intermediate dimensionality reduction procedure. As a second contribution we propose a modular RC framework for MTS classification, with an associated open-source Python library. The framework provides different modules to seamlessly implement advanced RC architectures. The architectures are compared to other MTS classifiers, including deep learning models and time series kernels. Results obtained on benchmark and real-world MTS datasets show that RC classifiers are dramatically faster and, when implemented using our proposed representation, also achieve superior classification accuracy.
Scalable Generalized Dynamic Topic Models
Authors: Patrick Jähnichen, Florian Wenzel, Marius Kloft, Stephan Mandt
Date: 2018-03-21 00:00:00
Tasks: Event Detection, Gaussian Processes, Topic Models, Variational Inference

Dynamic topic models (DTMs) model the evolution of prevalent themes in
literature, online media, and other forms of text over time. DTMs assume that
word co-occurrence statistics change continuously and therefore impose
continuous stochastic process priors on their model parameters. These dynamical
priors make inference much harder than in regular topic models, and also limit
scalability. In this paper, we present several new results around DTMs. First,
we extend the class of tractable priors from Wiener processes to the generic
class of Gaussian processes (GPs). This allows us to explore topics that
develop smoothly over time, that have a long-term memory or are temporally
concentrated (for event detection). Second, we show how to perform scalable
approximate inference in these models based on ideas around stochastic
variational inference and sparse Gaussian processes. This way we can train a
rich family of DTMs to massive data. Our experiments on several large-scale
datasets show that our generalized model allows us to find interesting patterns
that were not accessible by previous approaches.
Political Homophily in Independence Movements: Analysing and Classifying Social Media Users by National Identity
Authors: Arkaitz Zubiaga, Bo wang, Maria Liakata, Rob Procter
Date: 2017-02-27 00:00:00
Tasks: General Classification

Social media and data mining are increasingly being used to analyse political
and societal issues. Here we undertake the classification of social media users
as supporting or opposing ongoing independence movements in their territories.
Independence movements occur in territories whose citizens have conflicting
national identities; users with opposing national identities will then support
or oppose the sense of being part of an independent nation that differs from
the officially recognised country. We describe a methodology that relies on
users' self-reported location to build large-scale datasets for three
territories -- Catalonia, the Basque Country and Scotland. An analysis of these
datasets shows that homophily plays an important role in determining who people
connect with, as users predominantly choose to follow and interact with others
from the same national identity. We show that a classifier relying on users'
follow networks can achieve accurate, language-independent classification
performances ranging from 85% to 97% for the three territories.
Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR
Authors: Sandra Wachter, Brent Mittelstadt, Chris Russell
Date: 2017-11-01 00:00:00
Tasks: counterfactual, Decision Making

There has been much discussion of the right to explanation in the EU General
Data Protection Regulation, and its existence, merits, and disadvantages.
Implementing a right to explanation that opens the black box of algorithmic
decision-making faces major legal and technical barriers. Explaining the
functionality of complex algorithmic decision-making systems and their
rationale in specific cases is a technically challenging problem. Some
explanations may offer little meaningful information to data subjects, raising
questions around their value. Explanations of automated decisions need not
hinge on the general public understanding how algorithmic systems function.
Even though such interpretability is of great importance and should be pursued,
explanations can, in principle, be offered without opening the black box.
Looking at explanations as a means to help a data subject act rather than
merely understand, one could gauge the scope and content of explanations
according to the specific goal or action they are intended to support. From the
perspective of individuals affected by automated decision-making, we propose
three aims for explanations: (1) to inform and help the individual understand
why a particular decision was reached, (2) to provide grounds to contest the
decision if the outcome is undesired, and (3) to understand what would need to
change in order to receive a desired result in the future, based on the current
decision-making model. We assess how each of these goals finds support in the
GDPR. We suggest data controllers should offer a particular type of
explanation, unconditional counterfactual explanations, to support these three
aims. These counterfactual explanations describe the smallest change to the
world that can be made to obtain a desirable outcome, or to arrive at the
closest possible world, without needing to explain the internal logic of the
system.
Modeling and interpolation of the ambient magnetic field by Gaussian processes
Authors: Arno Solin, Manon Kok, Niklas Wahlström, Thomas B. Schön, Simo Särkkä
Date: 2015-09-15 00:00:00
Tasks: Gaussian Processes

Anomalies in the ambient magnetic field can be used as features in indoor
positioning and navigation. By using Maxwell's equations, we derive and present
a Bayesian non-parametric probabilistic modeling approach for interpolation and
extrapolation of the magnetic field. We model the magnetic field components
jointly by imposing a Gaussian process (GP) prior on the latent scalar
potential of the magnetic field. By rewriting the GP model in terms of a
Hilbert space representation, we circumvent the computational pitfalls
associated with GP modeling and provide a computationally efficient and
physically justified modeling tool for the ambient magnetic field. The model
allows for sequential updating of the estimate and time-dependent changes in
the magnetic field. The model is shown to work well in practice in different
applications: we demonstrate mapping of the magnetic field both with an
inexpensive Raspberry Pi powered robot and on foot using a standard smartphone.
Efficient Sampling and Structure Learning of Bayesian Networks
Authors: Jack Kuipers, Polina Suter, Giusi Moffa
Date: 2018-03-21 00:00:00
Tasks: Causal Discovery

Bayesian networks are probabilistic graphical models widely employed to understand dependencies in high dimensional data, and even to facilitate causal discovery. Learning the underlying network structure, which is encoded as a directed acyclic graph (DAG) is highly challenging mainly due to the vast number of possible networks in combination with the acyclicity constraint. Efforts have focussed on two fronts: constraint-based methods that perform conditional independence tests to exclude edges and score and search approaches which explore the DAG space with greedy or MCMC schemes. Here we synthesise these two fields in a novel hybrid method which reduces the complexity of MCMC approaches to that of a constraint-based method. Individual steps in the MCMC scheme only require simple table lookups so that very long chains can be efficiently obtained. Furthermore, the scheme includes an iterative procedure to correct for errors from the conditional independence tests. The algorithm offers markedly superior performance to alternatives, particularly because DAGs can also be sampled from the posterior distribution, enabling full Bayesian model averaging for much larger Bayesian networks.
On-demand Relational Concept Analysis
Authors: Alexandre Bazin, Jessie Carbonnel, Marianne Huchard, Giacomo Kahn
Date: 2018-03-21 00:00:00

Formal Concept Analysis and its associated conceptual structures have been
used to support exploratory search through conceptual navigation. Relational
Concept Analysis (RCA) is an extension of Formal Concept Analysis to process
relational datasets. RCA and its multiple interconnected structures represent
good candidates to support exploratory search in relational datasets, as they
are enabling navigation within a structure as well as between the connected
structures. However, building the entire structures does not present an
efficient solution to explore a small localised area of the dataset, for
instance to retrieve the closest alternatives to a given query. In these cases,
generating only a concept and its neighbour concepts at each navigation step
appears as a less costly alternative. In this paper, we propose an algorithm to
compute a concept and its neighbourhood in extended concept lattices. The
concepts are generated directly from the relational context family, and possess
both formal and relational attributes. The algorithm takes into account two RCA
scaling operators. We illustrate it on an example.
Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network
Authors: Yao Feng, Fan Wu, Xiaohu Shao, Yan-Feng Wang, Xi Zhou
Date: 2018-03-21 00:00:00
Proceeding: ECCV 2018 9
Tasks: 3D Face Reconstruction, Face Alignment, Face Model, Face Reconstruction, Position, regression

We propose a straightforward method that simultaneously reconstructs the 3D
facial structure and provides dense alignment. To achieve this, we design a 2D
representation called UV position map which records the 3D shape of a complete
face in UV space, then train a simple Convolutional Neural Network to regress
it from a single 2D image. We also integrate a weight mask into the loss
function during training to improve the performance of the network. Our method
does not rely on any prior face model, and can reconstruct full facial geometry
along with semantic meaning. Meanwhile, our network is very light-weighted and
spends only 9.8ms to process an image, which is extremely faster than previous
works. Experiments on multiple challenging datasets show that our method
surpasses other state-of-the-art methods on both reconstruction and alignment
tasks by a large margin.
End-to-End Fingerprints Liveness Detection using Convolutional Networks with Gram module
Authors: Eunsoo Park, Xuenan Cui, Weonjin Kim, Hakil Kim
Date: 2018-03-21 00:00:00

This paper proposes an end-to-end CNN(Convolutional Neural Networks) model
that uses gram modules with parameters that are approximately 1.2MB in size to
detect fake fingerprints. The proposed method assumes that texture is the most
appropriate characteristic in fake fingerprint detection, and implements the
gram module to extract textures from the CNN. The proposed CNN structure uses
the fire module as the base model and uses the gram module for texture
extraction. Tensors that passed the fire module will be joined with gram
modules to create a gram matrix with the same spatial size. After 3 gram
matrices extracted from different layers are combined with the channel axis, it
becomes the basis for categorizing fake fingerprints. The experiment results
had an average detection error of 2.61% from the LivDet 2011, 2013, 2015 data,
proving that an end-to-end CNN structure with few parameters that is able to be
used in fake fingerprint detection can be designed.
Glyph: Symbolic Regression Tools
Authors: Markus Quade, Julien Gout, Markus Abel
Date: 2018-03-13 00:00:00
Tasks: regression, Symbolic Regression

We present Glyph - a Python package for genetic programming based symbolic
regression. Glyph is designed for usage let by numerical simulations let by
real world experiments. For experimentalists, glyph-remote provides a
separation of tasks: a ZeroMQ interface splits the genetic programming
optimization task from the evaluation of an experimental (or numerical) run.
Glyph can be accessed at http://github.com/ambrosys/glyph . Domain experts are
be able to employ symbolic regression in their experiments with ease, even if
they are not expert programmers. The reuse potential is kept high by a generic
interface design. Glyph is available on PyPI and Github.
Expeditious Generation of Knowledge Graph Embeddings
Authors: Tommaso Soru, Stefano Ruberto, Diego Moussallem, André Valdestilhas, Alexander Bigerl, Edgard Marx, Diego Esteves
Date: 2018-03-21 00:00:00
Tasks: Graph Embedding, Knowledge Graph Completion, Knowledge Graph Embedding, Knowledge Graph Embeddings, Link Prediction, Question Answering, Triplet

Knowledge Graph Embedding methods aim at representing entities and relations
in a knowledge base as points or vectors in a continuous vector space. Several
approaches using embeddings have shown promising results on tasks such as link
prediction, entity recommendation, question answering, and triplet
classification. However, only a few methods can compute low-dimensional
embeddings of very large knowledge bases without needing state-of-the-art
computational resources. In this paper, we propose KG2Vec, a simple and fast
approach to Knowledge Graph Embedding based on the skip-gram model. Instead of
using a predefined scoring function, we learn it relying on Long Short-Term
Memories. We show that our embeddings achieve results comparable with the most
scalable approaches on knowledge graph completion as well as on a new metric.
Yet, KG2Vec can embed large graphs in lesser time by processing more than 250
million triples in less than 7 hours on common hardware.
Multi-view Metric Learning in Vector-valued Kernel Spaces
Authors: Riikka Huusari, Hachem Kadri, Cécile Capponi
Date: 2018-03-21 00:00:00
Tasks: Metric Learning

We consider the problem of metric learning for multi-view data and present a
novel method for learning within-view as well as between-view metrics in
vector-valued kernel spaces, as a way to capture multi-modal structure of the
data. We formulate two convex optimization problems to jointly learn the metric
and the classifier or regressor in kernel feature spaces. An iterative
three-step multi-view metric learning algorithm is derived from the
optimization problems. In order to scale the computation to large training
sets, a block-wise Nystr{\"o}m approximation of the multi-view kernel matrix is
introduced. We justify our approach theoretically and experimentally, and show
its performance on real-world datasets against relevant state-of-the-art
methods.
Some Theoretical Properties of GANs
Authors: G. Biau, B. Cadre, M. Sangnier, U. Tanielian
Date: 2018-03-21 00:00:00

Generative Adversarial Networks (GANs) are a class of generative algorithms
that have been shown to produce state-of-the art samples, especially in the
domain of image creation. The fundamental principle of GANs is to approximate
the unknown distribution of a given data set by optimizing an objective
function through an adversarial game between a family of generators and a
family of discriminators. In this paper, we offer a better theoretical
understanding of GANs by analyzing some of their mathematical and statistical
properties. We study the deep connection between the adversarial principle
underlying GANs and the Jensen-Shannon divergence, together with some
optimality characteristics of the problem. An analysis of the role of the
discriminator family via approximation arguments is also provided. In addition,
taking a statistical point of view, we study the large sample properties of the
estimated distribution and prove in particular a central limit theorem. Some of
our results are illustrated with simulated examples.
Patch-based Fake Fingerprint Detection Using a Fully Convolutional Neural Network with a Small Number of Parameters and an Optimal Threshold
Authors: Eunsoo Park, Xuenan Cui, Weonjin Kim, Jinsong Liu, Hakil Kim
Date: 2018-03-21 00:00:00

Fingerprint authentication is widely used in biometrics due to its simple
process, but it is vulnerable to fake fingerprints. This study proposes a
patch-based fake fingerprint detection method using a fully convolutional
neural network with a small number of parameters and an optimal threshold to
solve the above-mentioned problem. Unlike the existing methods that classify a
fingerprint as live or fake, the proposed method classifies fingerprints as
fake, live, or background, so preprocessing methods such as segmentation are
not needed. The proposed convolutional neural network (CNN) structure applies
the Fire module of SqueezeNet, and the fewer parameters used require only 2.0
MB of memory. The network that has completed training is applied to the
training data in a fully convolutional way, and the optimal threshold to
distinguish fake fingerprints is determined, which is used in the final test.
As a result of this study experiment, the proposed method showed an average
classification error of 1.35%, demonstrating a fake fingerprint detection
method using a high-performance CNN with a small number of parameters.
Deja Vu: Motion Prediction in Static Images
Authors: Silvia L. Pintea, Jan C. van Gemert, Arnold W. M. Smeulders
Date: 2018-03-19 00:00:00
Tasks: Action Recognition, motion prediction, Prediction, regression, Temporal Action Localization

This paper proposes motion prediction in single still images by learning it
from a set of videos. The building assumption is that similar motion is
characterized by similar appearance. The proposed method learns local motion
patterns given a specific appearance and adds the predicted motion in a number
of applications. This work (i) introduces a novel method to predict motion from
appearance in a single static image, (ii) to that end, extends of the
Structured Random Forest with regression derived from first principles, and
(iii) shows the value of adding motion predictions in different tasks such as:
weak frame-proposals containing unexpected events, action recognition, motion
saliency. Illustrative results indicate that motion prediction is not only
feasible, but also provides valuable information for a number of applications.
Domain Adaptation for Ear Recognition Using Deep Convolutional Neural Networks
Authors: Fevziye Irem Eyiokur, Dogucan Yaman, Hazim Kemal Ekenel
Date: 2018-03-21 00:00:00
Tasks: Domain Adaptation

In this paper, we have extensively investigated the unconstrained ear
recognition problem. We have first shown the importance of domain adaptation,
when deep convolutional neural network models are used for ear recognition. To
enable domain adaptation, we have collected a new ear dataset using the
Multi-PIE face dataset, which we named as Multi-PIE ear dataset. To improve the
performance further, we have combined different deep convolutional neural
network models. We have analyzed in depth the effect of ear image quality, for
example illumination and aspect ratio, on the classification performance.
Finally, we have addressed the problem of dataset bias in the ear recognition
field. Experiments on the UERC dataset have shown that domain adaptation leads
to a significant performance improvement. For example, when VGG-16 model is
used and the domain adaptation is applied, an absolute increase of around 10\%
has been achieved. Combining different deep convolutional neural network models
has further improved the accuracy by 4\%. It has also been observed that image
quality has an influence on the results. In the experiments that we have
conducted to examine the dataset bias, given an ear image, we were able to
classify the dataset that it has come from with 99.71\% accuracy, which
indicates a strong bias among the ear recognition datasets.
Chemi-net: a graph convolutional network for accurate drug property prediction
Authors: Ke Liu, Xiangyan Sun, Lei Jia, Jun Ma, Haoming Xing, Junqiu Wu, Hua Gao, Yax Sun, Florian Boulnois, Jie Fan
Date: 2018-03-16 00:00:00
Tasks: Drug Discovery, Prediction, Property Prediction

Absorption, distribution, metabolism, and excretion (ADME) studies are
critical for drug discovery. Conventionally, these tasks, together with other
chemical property predictions, rely on domain-specific feature descriptors, or
fingerprints. Following the recent success of neural networks, we developed
Chemi-Net, a completely data-driven, domain knowledge-free, deep learning
method for ADME property prediction. To compare the relative performance of
Chemi-Net with Cubist, one of the popular machine learning programs used by
Amgen, a large-scale ADME property prediction study was performed on-site at
Amgen. The results showed that our deep neural network method improved current
methods by a large margin. We foresee that the significantly increased accuracy
of ADME prediction seen with Chemi-Net over Cubist will greatly accelerate drug
discovery.