pwc-archive/papers-with-abstracts huggingface dataset:
Recognizing Birds from Sound - The 2018 BirdCLEF Baseline System
Authors: Stefan Kahl, Thomas Wilhelm-Stein, Holger Klinck, Danny Kowerko, Maximilian Eibl
Date: 2018-04-19 00:00:00
Tasks: BIG-bench Machine Learning, Bird Audio Detection

Reliable identification of bird species in recorded audio files would be a
transformative tool for researchers, conservation biologists, and birders. In
recent years, artificial neural networks have greatly improved the detection
quality of machine learning systems for bird species recognition. We present a
baseline system using convolutional neural networks. We publish our code base
as reference for participants in the 2018 LifeCLEF bird identification task and
discuss our experiments and potential improvements.
Unsupervised Probabilistic Deformation Modeling for Robust Diffeomorphic Registration
Authors: Julian Krebs, Tommaso Mansi, Boris Mailhé, Nicholas Ayache, Hervé Delingette
Date: 2018-04-19 00:00:00

We propose a deformable registration algorithm based on unsupervised learning
of a low-dimensional probabilistic parameterization of deformations. We model
registration in a probabilistic and generative fashion, by applying a
conditional variational autoencoder (CVAE) network. This model enables to also
generate normal or pathological deformations of any new image based on the
probabilistic latent space. Most recent learning-based registration algorithms
use supervised labels or deformation models, that miss important properties
such as diffeomorphism and sufficiently regular deformation fields. In this
work, we constrain transformations to be diffeomorphic by using a
differentiable exponentiation layer with a symmetric loss function. We
evaluated our method on 330 cardiac MR sequences and demonstrate robust
intra-subject registration results comparable to two state-of-the-art methods
but with more regular deformation fields compared to a recent learning-based
algorithm. Our method reached a mean DICE score of 78.3% and a mean Hausdorff
distance of 7.9mm. In two preliminary experiments, we illustrate the model's
abilities to transport pathological deformations to healthy subjects and to
cluster five diseases in the unsupervised deformation encoding space with a
classification performance of 70%.
Large-scale Nonlinear Variable Selection via Kernel Random Features
Authors: Magda Gregorová, Jason Ramapuram, Alexandros Kalousis, Stéphane Marchand-Maillet
Date: 2018-04-19 00:00:00
Tasks: Additive models, regression, Variable Selection

We propose a new method for input variable selection in nonlinear regression.
The method is embedded into a kernel regression machine that can model general
nonlinear functions, not being a priori limited to additive models. This is the
first kernel-based variable selection method applicable to large datasets. It
sidesteps the typical poor scaling properties of kernel methods by mapping the
inputs into a relatively low-dimensional space of random features. The
algorithm discovers the variables relevant for the regression task together
with learning the prediction model through learning the appropriate nonlinear
random feature maps. We demonstrate the outstanding performance of our method
on a set of large-scale synthetic and real datasets.
Instance Selection Improves Geometric Mean Accuracy: A Study on Imbalanced Data Classification
Authors: Ludmila I. Kuncheva, Álvar Arnaiz-González, José-Francisco Díez-Pastor, Iain A. D. Gunn
Date: 2018-04-19 00:00:00
Tasks: General Classification

A natural way of handling imbalanced data is to attempt to equalise the class
frequencies and train the classifier of choice on balanced data. For two-class
imbalanced problems, the classification success is typically measured by the
geometric mean (GM) of the true positive and true negative rates. Here we prove
that GM can be improved upon by instance selection, and give the theoretical
conditions for such an improvement. We demonstrate that GM is non-monotonic
with respect to the number of retained instances, which discourages systematic
instance selection. We also show that balancing the distribution frequencies is
inferior to a direct maximisation of GM. To verify our theoretical findings, we
carried out an experimental study of 12 instance selection methods for
imbalanced data, using 66 standard benchmark data sets. The results reveal
possible room for new instance selection methods for imbalanced data.
SRL4ORL: Improving Opinion Role Labeling using Multi-task Learning with Semantic Role Labeling
Authors: Ana Marasović, Anette Frank
Date: 2017-11-02 00:00:00
Proceeding: NAACL 2018 6
Tasks: Fine-Grained Opinion Analysis, Multi-Task Learning

For over a decade, machine learning has been used to extract
opinion-holder-target structures from text to answer the question "Who
expressed what kind of sentiment towards what?". Recent neural approaches do
not outperform the state-of-the-art feature-based models for Opinion Role
Labeling (ORL). We suspect this is due to the scarcity of labeled training data
and address this issue using different multi-task learning (MTL) techniques
with a related task which has substantially more data, i.e. Semantic Role
Labeling (SRL). We show that two MTL models improve significantly over the
single-task model for labeling of both holders and targets, on the development
and the test sets. We found that the vanilla MTL model which makes predictions
using only shared ORL and SRL features, performs the best. With deeper analysis
we determine what works and what might be done to make further improvements for
ORL.
Compressing Neural Networks using the Variational Information Bottleneck
Authors: Bin Dai, Chen Zhu, David Wipf
Date: 2018-02-28 00:00:00
Proceeding: ICML 2018

Neural networks can be compressed to reduce memory and computational
requirements, or to increase accuracy by facilitating the use of a larger base
architecture. In this paper we focus on pruning individual neurons, which can
simultaneously trim model size, FLOPs, and run-time memory. To improve upon the
performance of existing compression algorithms we utilize the information
bottleneck principle instantiated via a tractable variational bound.
Minimization of this information theoretic bound reduces the redundancy between
adjacent layers by aggregating useful information into a subset of neurons that
can be preserved. In contrast, the activations of disposable neurons are shut
off via an attractive form of sparse regularization that emerges naturally from
this framework, providing tangible advantages over traditional sparsity
penalties without contributing additional tuning parameters to the energy
landscape. We demonstrate state-of-the-art compression rates across an array of
datasets and network architectures.
Real Time Emulation of Parametric Guitar Tube Amplifier With Long Short Term Memory Neural Network
Authors: Thomas Schmitz, Jean-Jacques Embrechts
Date: 2018-04-19 00:00:00

Numerous audio systems for musicians are expensive and bulky. Therefore, it
could be advantageous to model them and to replace them by computer emulation.
In guitar players' world, audio systems could have a desirable nonlinear
behavior (distortion effects). It is thus difficult to find a simple model to
emulate them in real time. Volterra series model and its subclass are usual
ways to model nonlinear systems. Unfortunately, these systems are difficult to
identify in an analytic way. In this paper we propose to take advantage of the
new progress made in neural networks to emulate them in real time. We show that
an accurate emulation can be reached with less than 1% of root mean square
error between the signal coming from a tube amplifier and the output of the
neural network. Moreover, the research has been extended to model the Gain
parameter of the amplifier.
Human Activity Recognition using Recurrent Neural Networks
Authors: Deepika Singh, Erinc Merdivan, Ismini Psychoula, Johannes Kropf, Sten Hanke, Matthieu Geist, Andreas Holzinger
Date: 2018-04-19 00:00:00
Tasks: Activity Recognition, BIG-bench Machine Learning, Human Activity Recognition

Human activity recognition using smart home sensors is one of the bases of
ubiquitous computing in smart environments and a topic undergoing intense
research in the field of ambient assisted living. The increasingly large amount
of data sets calls for machine learning methods. In this paper, we introduce a
deep learning model that learns to classify human activities without using any
prior knowledge. For this purpose, a Long Short Term Memory (LSTM) Recurrent
Neural Network was applied to three real world smart home datasets. The results
of these experiments show that the proposed approach outperforms the existing
ones in terms of accuracy and performance.
Liveness Detection Using Implicit 3D Features
Authors: J. Matias Di Martino, Qiang Qiu, Trishul Nagenalli, Guillermo Sapiro
Date: 2018-04-18 00:00:00
Tasks: 3D Reconstruction, Face Recognition

Spoofing attacks are a threat to modern face recognition systems. In this
work we present a simple yet effective liveness detection approach to enhance
2D face recognition methods and make them robust against spoofing attacks. We
show that the risk to spoofing attacks can be re- duced through the use of an
additional source of light, for example a flash. From a pair of input images
taken under different illumination, we define discriminative features that
implicitly contain facial three-dimensional in- formation. Furthermore, we show
that when multiple sources of light are considered, we are able to validate
which one has been activated. This makes possible the design of a highly secure
active-light authentication framework. Finally, further investigating the use
of 3D features without 3D reconstruction, we introduce an approximated
disparity-based implicit 3D feature obtained from an uncalibrated stereo-pair
of cameras. Valida- tion experiments show that the proposed methods produce
state-of-the-art results in challenging scenarios with nearly no feature
extraction latency.
varrank: an R package for variable ranking based on mutual information with applications to observed systemic datasets
Authors: Gilles Kratzer, Reinhard Furrer
Date: 2018-04-19 00:00:00
Tasks: Dimensionality Reduction

This article describes the R package varrank. It has a flexible
implementation of heuristic approaches which perform variable ranking based on
mutual information. The package is particularly suitable for exploring
multivariate datasets requiring a holistic analysis. The core functionality is
a general implementation of the minimum redundancy maximum relevance (mRMRe)
model. This approach is based on information theory metrics. It is compatible
with discrete and continuous data which are discretised using a large choice of
possible rules. The two main problems that can be addressed by this package are
the selection of the most representative variables for modeling a collection of
variables of interest, i.e., dimension reduction, and variable ranking with
respect to a set of variables of interest.
Specialising Word Vectors for Lexical Entailment
Authors: Ivan Vulić, Nikola Mrkšić
Date: 2017-10-17 00:00:00
Tasks: Lexical Entailment, Relation, Semantic Similarity, Semantic Textual Similarity

We present LEAR (Lexical Entailment Attract-Repel), a novel post-processing
method that transforms any input word vector space to emphasise the asymmetric
relation of lexical entailment (LE), also known as the IS-A or
hyponymy-hypernymy relation. By injecting external linguistic constraints
(e.g., WordNet links) into the initial vector space, the LE specialisation
procedure brings true hyponymy-hypernymy pairs closer together in the
transformed Euclidean space. The proposed asymmetric distance measure adjusts
the norms of word vectors to reflect the actual WordNet-style hierarchy of
concepts. Simultaneously, a joint objective enforces semantic similarity using
the symmetric cosine distance, yielding a vector space specialised for both
lexical relations at once. LEAR specialisation achieves state-of-the-art
performance in the tasks of hypernymy directionality, hypernymy detection, and
graded lexical entailment, demonstrating the effectiveness and robustness of
the proposed asymmetric specialisation model.
Hierarchical Behavioral Repertoires with Unsupervised Descriptors
Authors: Antoine Cully, Yiannis Demiris
Date: 2018-04-19 00:00:00

Enabling artificial agents to automatically learn complex, versatile and
high-performing behaviors is a long-lasting challenge. This paper presents a
step in this direction with hierarchical behavioral repertoires that stack
several behavioral repertoires to generate sophisticated behaviors. Each
repertoire of this architecture uses the lower repertoires to create complex
behaviors as sequences of simpler ones, while only the lowest repertoire
directly controls the agent's movements. This paper also introduces a novel
approach to automatically define behavioral descriptors thanks to an
unsupervised neural network that organizes the produced high-level behaviors.
The experiments show that the proposed architecture enables a robot to learn
how to draw digits in an unsupervised manner after having learned to draw lines
and arcs. Compared to traditional behavioral repertoires, the proposed
architecture reduces the dimensionality of the optimization problems by orders
of magnitude and provides behaviors with a twice better fitness. More
importantly, it enables the transfer of knowledge between robots: a
hierarchical repertoire evolved for a robotic arm to draw digits can be
transferred to a humanoid robot by simply changing the lowest layer of the
hierarchy. This enables the humanoid to draw digits although it has never been
trained for this task.
Visibility graphs for image processing
Authors: Jacopo Iacovacci, Lucas Lacasa
Date: 2018-04-19 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

The family of image visibility graphs (IVGs) have been recently introduced as
simple algorithms by which scalar fields can be mapped into graphs. Here we
explore the usefulness of such operator in the scenario of image processing and
image classification. We demonstrate that the link architecture of the image
visibility graphs encapsulates relevant information on the structure of the
images and we explore their potential as image filters and compressors. We
introduce several graph features, including the novel concept of Visibility
Patches, and show through several examples that these features are highly
informative, computationally efficient and universally applicable for general
pattern recognition and image classification tasks.
Finite Biased Teaching with Infinite Concept Classes
Authors: Jose Hernandez-Orallo, Jan Arne Telle
Date: 2018-04-19 00:00:00

We investigate the teaching of infinite concept classes through the effect of
the learning bias (which is used by the learner to prefer some concepts over
others and by the teacher to devise the teaching examples) and the sampling
bias (which determines how the concepts are sampled from the class). We analyse
two important classes: Turing machines and finite-state machines. We derive
bounds for the biased teaching dimension when the learning bias is derived from
a complexity measure (Kolmogorov complexity and minimal number of states
respectively) and analyse the sampling distributions that lead to finite
expected biased teaching dimensions. We highlight the existing trade-off
between the bound and the representativeness of the sample, and its
implications for the understanding of what teaching rich concepts to machines
entails.
Estimation of Tissue Oxygen Saturation from RGB Images based on Pixel-level Image Translation
Authors: Qing-Biao Li, Xiao-Yun Zhou, Jianyu Lin, Jian-Qing Zheng, Neil T. Clancy, Daniel S. Elson
Date: 2018-04-19 00:00:00
Tasks: Diagnostic, Image-to-Image Translation, Translation

Intra-operative measurement of tissue oxygen saturation (StO2) has been
widely explored by pulse oximetry or hyperspectral imaging (HSI) to assess the
function and viability of tissue. In this paper we propose a pixel- level
image-to-image translation approach based on conditional Generative Adversarial
Networks (cGAN) to estimate tissue oxygen saturation (StO2) directly from RGB
images. The real-time performance and non-reliance on additional hardware,
enable a seamless integration of the proposed method into surgical and
diagnostic workflows with standard endoscope systems. For validation, RGB
images and StO2 ground truth were simulated and estimated from HSI images
collected by a liquid crystal tuneable filter (LCTF) endoscope for three tissue
types (porcine bowel, lamb uterus and rabbit uterus). The result show that the
proposed method can achieve visually identical images with comparable accuracy.
Learning Sparse Latent Representations with the Deep Copula Information Bottleneck
Authors: Aleksander Wieczorek, Mario Wieser, Damian Murezzan, Volker Roth
Date: 2018-04-17 00:00:00
Proceeding: ICLR 2018 1
Tasks: Disentanglement, Representation Learning

Deep latent variable models are powerful tools for representation learning.
In this paper, we adopt the deep information bottleneck model, identify its
shortcomings and propose a model that circumvents them. To this end, we apply a
copula transformation which, by restoring the invariance properties of the
information bottleneck method, leads to disentanglement of the features in the
latent space. Building on that, we show how this transformation translates to
sparsity of the latent space in the new model. We evaluate our method on
artificial and real data.
Dictionary learning -- from local towards global and adaptive
Authors: Marie Christine Pali, Karin Schnass
Date: 2018-04-19 00:00:00
Tasks: Dictionary Learning

This paper studies the convergence behaviour of dictionary learning via the Iterative Thresholding and K-residual Means (ITKrM) algorithm. On one hand it is proved that ITKrM is a contraction under much more relaxed conditions than previously necessary. On the other hand it is shown that there seem to exist stable fixed points that do not correspond to the generating dictionary, which can be characterised as very coherent. Based on an analysis of the residuals using these bad dictionaries, replacing coherent atoms with carefully designed replacement candidates is proposed. In experiments on synthetic data this outperforms random or no replacement and always leads to full dictionary recovery. Finally the question how to learn dictionaries without knowledge of the correct dictionary size and sparsity level is addressed. Decoupling the replacement strategy of coherent or unused atoms into pruning and adding, and slowly carefully increasing the sparsity level, leads to an adaptive version of ITKrM. In several experiments this adaptive dictionary learning algorithm is shown to recover a generating dictionary from randomly initialised dictionaries of various sizes on synthetic data and to learn meaningful dictionaries on image data.
Loop Restricted Existential Rules and First-order Rewritability for Query Answering
Authors: Vernon Asuncion, Yan Zhang, Heng Zhang, Yun Bai, Weisheng Si
Date: 2018-04-19 00:00:00

In ontology-based data access (OBDA), the classical database is enhanced with
an ontology in the form of logical assertions generating new intensional
knowledge. A powerful form of such logical assertions is the tuple-generating
dependencies (TGDs), also called existential rules, where Horn rules are
extended by allowing existential quantifiers to appear in the rule heads. In
this paper we introduce a new language called loop restricted (LR) TGDs
(existential rules), which are TGDs with certain restrictions on the loops
embedded in the underlying rule set. We study the complexity of this new
language. We show that the conjunctive query answering (CQA) under the LR TGDs
is decid- able. In particular, we prove that this language satisfies the
so-called bounded derivation-depth prop- erty (BDDP), which implies that the
CQA is first-order rewritable, and its data complexity is in AC0 . We also
prove that the combined complexity of the CQA is EXPTIME complete, while the
language membership is PSPACE complete. Then we extend the LR TGDs language to
the generalised loop restricted (GLR) TGDs language, and prove that this class
of TGDs still remains to be first-order rewritable and properly contains most
of other first-order rewritable TGDs classes discovered in the literature so
far.
The Higher-Order Prover Leo-III (Extended Version)
Authors: Alexander Steen, Christoph Benzmüller
Date: 2018-02-08 00:00:00

The automated theorem prover Leo-III for classical higher-order logic with
Henkin semantics and choice is presented. Leo-III is based on extensional
higher-order paramodulation and accepts every common TPTP dialect (FOF, TFF,
THF), including their recent extensions to rank-1 polymorphism (TF1, TH1). In
addition, the prover natively supports almost every normal higher-order modal
logic. Leo-III cooperates with first-order reasoning tools using translations
to many-sorted first-order logic and produces verifiable proof certificates.
The prover is evaluated on heterogeneous benchmark sets.
Unsupervised Prostate Cancer Detection on H&E using Convolutional Adversarial Autoencoders
Authors: Wouter Bulten, Geert Litjens
Date: 2018-04-19 00:00:00
Tasks: Clustering

We propose an unsupervised method using self-clustering convolutional
adversarial autoencoders to classify prostate tissue as tumor or non-tumor
without any labeled training data. The clustering method is integrated into the
training of the autoencoder and requires only little post-processing. Our
network trains on hematoxylin and eosin (H&E) input patches and we tested two
different reconstruction targets, H&E and immunohistochemistry (IHC). We show
that antibody-driven feature learning using IHC helps the network to learn
relevant features for the clustering task. Our network achieves a F1 score of
0.62 using only a small set of validation labels to assign classes to clusters.
Putting Question-Answering Systems into Practice: Transfer Learning for Efficient Domain Customization
Authors: Bernhard Kratzwald, Stefan Feuerriegel
Date: 2018-04-19 00:00:00
Tasks: Information Retrieval, Management, Question Answering, Retrieval, Transfer Learning

Traditional information retrieval (such as that offered by web search
engines) impedes users with information overload from extensive result pages
and the need to manually locate the desired information therein. Conversely,
question-answering systems change how humans interact with information systems:
users can now ask specific questions and obtain a tailored answer - both
conveniently in natural language. Despite obvious benefits, their use is often
limited to an academic context, largely because of expensive domain
customizations, which means that the performance in domain-specific
applications often fails to meet expectations. This paper proposes
cost-efficient remedies: (i) we leverage metadata through a filtering
mechanism, which increases the precision of document retrieval, and (ii) we
develop a novel fuse-and-oversample approach for transfer learning in order to
improve the performance of answer extraction. Here knowledge is inductively
transferred from a related, yet different, tasks to the domain-specific
application, while accounting for potential differences in the sample sizes
across both tasks. The resulting performance is demonstrated with actual use
cases from a finance company and the film industry, where fewer than 400
question-answer pairs had to be annotated in order to yield significant
performance gains. As a direct implication to management, this presents a
promising path to better leveraging of knowledge stored in information systems.