pwc-archive/papers-with-abstracts huggingface dataset:
Learning Knowledge Graph Embeddings with Type Regularizer
Authors: Bhushan Kotnis, Vivi Nastase
Date: 2017-06-28 00:00:00
Tasks: Knowledge Graph Embeddings, Vocal Bursts Type Prediction

Learning relations based on evidence from knowledge bases relies on
processing the available relation instances. Many relations, however, have
clear domain and range, which we hypothesize could help learn a better, more
generalizing, model. We include such information in the RESCAL model in the
form of a regularization factor added to the loss function that takes into
account the types (categories) of the entities that appear as arguments to
relations in the knowledge base. We note increased performance compared to the
baseline model in terms of mean reciprocal rank and hits@N, N = 1, 3, 10.
Furthermore, we discover scenarios that significantly impact the effectiveness
of the type regularizer.
Analysis of the Impact of Negative Sampling on Link Prediction in Knowledge Graphs
Authors: Bhushan Kotnis, Vivi Nastase
Date: 2017-08-22 00:00:00
Tasks: Knowledge Graph Embeddings, Knowledge Graphs, Link Prediction

Knowledge graphs are large, useful, but incomplete knowledge repositories.
They encode knowledge through entities and relations which define each other
through the connective structure of the graph. This has inspired methods for
the joint embedding of entities and relations in continuous low-dimensional
vector spaces, that can be used to induce new edges in the graph, i.e., link
prediction in knowledge graphs. Learning these representations relies on
contrasting positive instances with negative ones. Knowledge graphs include
only positive relation instances, leaving the door open for a variety of
methods for selecting negative examples. In this paper we present an empirical
study on the impact of negative sampling on the learned embeddings, assessed
through the task of link prediction. We use state-of-the-art knowledge graph
embeddings -- \rescal , TransE, DistMult and ComplEX -- and evaluate on
benchmark datasets -- FB15k and WN18. We compare well known methods for
negative sampling and additionally propose embedding based sampling methods. We
note a marked difference in the impact of these sampling methods on the two
datasets, with the "traditional" corrupting positives method leading to best
results on WN18, while embedding based methods benefiting the task on FB15k.
Optimal Warping Paths are unique for almost every Pair of Time Series
Authors: Brijnesh J. Jain, David Schultz
Date: 2017-05-16 00:00:00
Tasks: Dynamic Time Warping, Time Series, Time Series Analysis

Update rules for learning in dynamic time warping spaces are based on optimal
warping paths between parameter and input time series. In general, optimal
warping paths are not unique resulting in adverse effects in theory and
practice. Under the assumption of squared error local costs, we show that no
two warping paths have identical costs almost everywhere in a measure-theoretic
sense. Two direct consequences of this result are: (i) optimal warping paths
are unique almost everywhere, and (ii) the set of all pairs of time series with
multiple equal-cost warping paths coincides with the union of exponentially
many zero sets of quadratic forms. One implication of the proposed results is
that typical distance-based cost functions such as the k-means objective are
differentiable almost everywhere and can be minimized by subgradient methods.
Detecting non-causal artifacts in multivariate linear regression models
Authors: Dominik Janzing, Bernhard Schoelkopf
Date: 2018-03-02 00:00:00
Proceeding: ICML 2018 7
Tasks: regression

We consider linear models where $d$ potential causes $X_1,...,X_d$ are
correlated with one target quantity $Y$ and propose a method to infer whether
the association is causal or whether it is an artifact caused by overfitting or
hidden common causes. We employ the idea that in the former case the vector of
regression coefficients has 'generic' orientation relative to the covariance
matrix $\Sigma_{XX}$ of $X$. Using an ICA based model for confounding, we show
that both confounding and overfitting yield regression vectors that concentrate
mainly in the space of low eigenvalues of $\Sigma_{XX}$.
Unsupervised Deep Single-Image Intrinsic Decomposition using Illumination-Varying Image Sequences
Authors: Louis Lettry, Kenneth Vanhoey, Luc van Gool
Date: 2018-03-02 00:00:00

Machine learning based Single Image Intrinsic Decomposition (SIID) methods
decompose a captured scene into its albedo and shading images by using the
knowledge of a large set of known and realistic ground truth decompositions.
Collecting and annotating such a dataset is an approach that cannot scale to
sufficient variety and realism. We free ourselves from this limitation by
training on unannotated images.
  Our method leverages the observation that two images of the same scene but
with different lighting provide useful information on their intrinsic
properties: by definition, albedo is invariant to lighting conditions, and
cross-combining the estimated albedo of a first image with the estimated
shading of a second one should lead back to the second one's input image. We
transcribe this relationship into a siamese training scheme for a deep
convolutional neural network that decomposes a single image into albedo and
shading. The siamese setting allows us to introduce a new loss function
including such cross-combinations, and to train solely on (time-lapse) images,
discarding the need for any ground truth annotations.
  As a result, our method has the good properties of i) taking advantage of the
time-varying information of image sequences in the (pre-computed) training
step, ii) not requiring ground truth data to train on, and iii) being able to
decompose single images of unseen scenes at runtime. To demonstrate and
evaluate our work, we additionally propose a new rendered dataset containing
illumination-varying scenes and a set of quantitative metrics to evaluate SIID
algorithms. Despite its unsupervised nature, our results compete with state of
the art methods, including supervised and non data-driven methods.
Speeding up the Köhler's method of contrast thresholding
Authors: Guillaume Noyel
Date: 2017-07-17 00:00:00

K{\"o}hler's method is a useful multi-thresholding technique based on
boundary contrast. However, the direct algorithm has a too high complexity-O(N
2) i.e. quadratic with the pixel numbers N-to process images at a sufficient
speed for practical applications. In this paper, a new algorithm to speed up
K{\"o}hler's method is introduced with a complexity in O(N M), M is the number
of grey levels. The proposed algorithm is designed for parallelisation and
vector processing , which are available in current processors, using OpenMP
(Open Multi-Processing) and SIMD instructions (Single Instruction on Multiple
Data). A fast implementation allows a gain factor of 405 in an image of 18
million pixels and a video processing in real time (gain factor of 96).
Automated Map Reading: Image Based Localisation in 2-D Maps Using Binary Semantic Descriptors
Authors: Pilailuck Panphattarasap, Andrew Calway
Date: 2018-03-02 00:00:00

We describe a novel approach to image based localisation in urban
environments using semantic matching between images and a 2-D map. It contrasts
with the vast majority of existing approaches which use image to image database
matching. We use highly compact binary descriptors to represent semantic
features at locations, significantly increasing scalability compared with
existing methods and having the potential for greater invariance to variable
imaging conditions. The approach is also more akin to human map reading, making
it more suited to human-system interaction. The binary descriptors indicate the
presence or not of semantic features relating to buildings and road junctions
in discrete viewing directions. We use CNN classifiers to detect the features
in images and match descriptor estimates with a database of location tagged
descriptors derived from the 2-D map. In isolation, the descriptors are not
sufficiently discriminative, but when concatenated sequentially along a route,
their combination becomes highly distinctive and allows localisation even when
using non-perfect classifiers. Performance is further improved by taking into
account left or right turns over a route. Experimental results obtained using
Google StreetView and OpenStreetMap data show that the approach has
considerable potential, achieving localisation accuracy of around 85% using
routes corresponding to approximately 200 meters.
Instance Optimal Decoding and the Restricted Isometry Property
Authors: Nicolas Keriven, Rémi Gribonval
Date: 2018-02-27 00:00:00
Tasks: Compressive Sensing, Decoder

In this paper, we address the question of information preservation in
ill-posed, non-linear inverse problems, assuming that the measured data is
close to a low-dimensional model set. We provide necessary and sufficient
conditions for the existence of a so-called instance optimal decoder, i.e.,
that is robust to noise and modelling error. Inspired by existing results in
compressive sensing, our analysis is based on a (Lower) Restricted Isometry
Property (LRIP), formulated in a non-linear fashion. We also provide sufficient
conditions for non-uniform recovery with random measurement operators, with a
new formulation of the LRIP. We finish by describing typical strategies to
prove the LRIP in both linear and non-linear cases, and illustrate our results
by studying the invertibility of a one-layer neural net with random weights.
Aspl{ü}nd's metric defined in the Logarithmic Image Processing (LIP) framework for colour and multivariate images
Authors: Guillaume Noyel, Michel Jourlin
Date: 2018-03-02 00:00:00

Aspl{\"u}nd's metric, which is useful for pattern matching, consists in a
double-sided probing, i.e. the over-graph and the sub-graph of a function are
probed jointly. It has previously been defined for grey-scale images using the
Logarithmic Image Processing (LIP) framework. LIP is a non-linear model to
perform operations between images while being consistent with the human visual
system. Our contribution consists in extending the Aspl{\"u}nd's metric to
colour and multivariate images using the LIP framework. Aspl{\"u}nd's metric is
insensitive to lighting variations and we propose a colour variant which is
robust to noise.
Universal discrete-time reservoir computers with stochastic inputs and linear readouts using non-homogeneous state-affine systems
Authors: Lyudmila Grigoryeva, Juan-Pablo Ortega
Date: 2017-12-03 00:00:00

A new class of non-homogeneous state-affine systems is introduced for use in
reservoir computing. Sufficient conditions are identified that guarantee first,
that the associated reservoir computers with linear readouts are causal,
time-invariant, and satisfy the fading memory property and second, that a
subset of this class is universal in the category of fading memory filters with
stochastic almost surely uniformly bounded inputs. This means that any
discrete-time filter that satisfies the fading memory property with random
inputs of that type can be uniformly approximated by elements in the
non-homogeneous state-affine family.
A self-organizing neural network architecture for learning human-object interactions
Authors: Luiza Mici, German I. Parisi, Stefan Wermter
Date: 2017-10-05 00:00:00
Tasks: Human-Object Interaction Detection, Object

The visual recognition of transitive actions comprising human-object
interactions is a key component for artificial systems operating in natural
environments. This challenging task requires jointly the recognition of
articulated body actions as well as the extraction of semantic elements from
the scene such as the identity of the manipulated objects. In this paper, we
present a self-organizing neural network for the recognition of human-object
interactions from RGB-D videos. Our model consists of a hierarchy of
Grow-When-Required (GWR) networks that learn prototypical representations of
body motion patterns and objects, accounting for the development of
action-object mappings in an unsupervised fashion. We report experimental
results on a dataset of daily activities collected for the purpose of this
study as well as on a publicly available benchmark dataset. In line with
neurophysiological studies, our self-organizing architecture exhibits higher
neural activation for congruent action-object pairs learned during training
sessions with respect to synthetically created incongruent ones. We show that
our unsupervised model shows competitive classification results on the
benchmark dataset with respect to strictly supervised approaches.
Convolutional Geometric Matrix Completion
Authors: Kai-Lang Yao, Wu-Jun Li, Jianbo Yang, Xinyan Lu
Date: 2018-03-02 00:00:00
Tasks: Matrix Completion

Geometric matrix completion (GMC) has been proposed for recommendation by integrating the relationship (link) graphs among users/items into matrix completion (MC). Traditional GMC methods typically adopt graph regularization to impose smoothness priors for MC. Recently, geometric deep learning on graphs (GDLG) is proposed to solve the GMC problem, showing better performance than existing GMC methods including traditional graph regularization based methods. To the best of our knowledge, there exists only one GDLG method for GMC, which is called RMGCNN. RMGCNN combines graph convolutional network (GCN) and recurrent neural network (RNN) together for GMC. In the original work of RMGCNN, RMGCNN demonstrates better performance than pure GCN-based method. In this paper, we propose a new GMC method, called convolutional geometric matrix completion (CGMC), for recommendation with graphs among users/items. CGMC is a pure GCN-based method with a newly designed graph convolutional network. Experimental results on real datasets show that CGMC can outperform other state-of-the-art methods including RMGCNN in terms of both accuracy and speed.
Clinically Meaningful Comparisons Over Time: An Approach to Measuring Patient Similarity based on Subsequence Alignment
Authors: Dev Goyal, Zeeshan Syed, Jenna Wiens
Date: 2018-03-02 00:00:00

Longitudinal patient data has the potential to improve clinical risk
stratification models for disease. However, chronic diseases that progress
slowly over time are often heterogeneous in their clinical presentation.
Patients may progress through disease stages at varying rates. This leads to
pathophysiological misalignment over time, making it difficult to consistently
compare patients in a clinically meaningful way. Furthermore, patients present
clinically for the first time at different stages of disease. This eliminates
the possibility of simply aligning patients based on their initial
presentation. Finally, patient data may be sampled at different rates due to
differences in schedules or missed visits. To address these challenges, we
propose a robust measure of patient similarity based on subsequence alignment.
Compared to global alignment techniques that do not account for
pathophysiological misalignment, focusing on the most relevant subsequences
allows for an accurate measure of similarity between patients. We demonstrate
the utility of our approach in settings where longitudinal data, while useful,
are limited and lack a clear temporal alignment for comparison. Applied to the
task of stratifying patients for risk of progression to probable Alzheimer's
Disease, our approach outperforms models that use only snapshot data (AUROC of
0.839 vs. 0.812) and models that use global alignment techniques (AUROC of
0.822). Our results support the hypothesis that patients' trajectories are
useful for quantifying inter-patient similarities and that using subsequence
matching and can help account for heterogeneity and misalignment in
longitudinal data.
Fusion of multispectral satellite imagery using a cluster of graphics processing unit
Authors: Anas M. Al-Oraiqat, E. A. Bashkov, V. Babkov, C. Titarenko
Date: 2018-03-02 00:00:00
Tasks: CPU, GPU

The paper presents a parallel implementation of existing image fusion methods
on a graphical cluster. Parallel implementations of methods based on discrete
wavelet transformation (Haars and Daubechies discrete wavelet transform) are
developed. Experiments were performed on a cluster using GPU and CPU and
performance gains were estimated for the use of the developed parallel
implementations to process satellite images from satellite Landsat 7. The
implementation on a graphic cluster provides performance improvement from 2 to
18 times. The quality of the considered methods was evaluated by ERGAS and QNR
metrics. The results show performance gains and retaining of quality with the
cluster of GPU compared to the results obtained by the authors and other
researchers for a CPU and single GPU.
Representing Verbs as Argument Concepts
Authors: Yu Gong, Kaiqi Zhao, Kenny Q. Zhu
Date: 2018-03-02 00:00:00
Tasks: Object

Verbs play an important role in the understanding of natural language text.
This paper studies the problem of abstracting the subject and object arguments
of a verb into a set of noun concepts, known as the "argument concepts". This
set of concepts, whose size is parameterized, represents the fine-grained
semantics of a verb. For example, the object of "enjoy" can be abstracted into
time, hobby and event, etc. We present a novel framework to automatically infer
human readable and machine computable action concepts with high accuracy.
Contextually Customized Video Summaries via Natural Language
Authors: Jinsoo Choi, Tae-Hyun Oh, In So Kweon
Date: 2017-02-06 00:00:00

The best summary of a long video differs among different people due to its
highly subjective nature. Even for the same person, the best summary may change
with time or mood. In this paper, we introduce the task of generating
customized video summaries through simple text. First, we train a deep
architecture to effectively learn semantic embeddings of video frames by
leveraging the abundance of image-caption data via a progressive and residual
manner. Given a user-specific text description, our algorithm is able to select
semantically relevant video segments and produce a temporally aligned video
summary. In order to evaluate our textually customized video summaries, we
conduct experimental comparison with baseline methods that utilize ground-truth
information. Despite the challenging baselines, our method still manages to
show comparable or even exceeding performance. We also show that our method is
able to generate semantically diverse video summaries by only utilizing the
learned visual embeddings.
Proceedings 6th International Workshop on Theorem proving components for Educational software
Authors: Pedro Quaresma, Walther Neuper
Date: 2018-03-02 00:00:00
Tasks: Automated Theorem Proving

The 6th International Workshop on Theorem proving components for Educational
software (ThEdu'17) was held in Gothenburg, Sweden, on 6 Aug 2017. It was
associated to the conference CADE26. Topics of interest include: methods of
automated deduction applied to checking students' input; methods of automated
deduction applied to prove post-conditions for particular problem solutions;
combinations of deduction and computation enabling systems to propose next
steps; automated provers specific for dynamic geometry systems; proof and
proving in mathematics education.
  ThEdu'17 was a vibrant workshop, with one invited talk and eight
contributions. It triggered the post-proceedings at hand.
Age Group Classification with Speech and Metadata Multimodality Fusion
Authors: Denys Katerenchuk
Date: 2018-03-02 00:00:00
Proceeding: EACL 2017 4
Tasks: Classification, General Classification

Children comprise a significant proportion of TV viewers and it is worthwhile
to customize the experience for them. However, identifying who is a child in
the audience can be a challenging task. Identifying gender and age from audio
commands is a well-studied problem but is still very challenging to get good
accuracy when the utterances are typically only a couple of seconds long. We
present initial studies of a novel method which combines utterances with user
metadata. In particular, we develop an ensemble of different machine learning
techniques on different subsets of data to improve child detection. Our initial
results show a 9.2\% absolute improvement over the baseline, leading to a
state-of-the-art performance.
Model-Based Clustering and Classification of Functional Data
Authors: Faicel Chamroukhi, Hien D. Nguyen
Date: 2018-03-01 00:00:00
Tasks: Classification, Clustering, General Classification, model, Time Series Analysis

The problem of complex data analysis is a central topic of modern statistical
science and learning systems and is becoming of broader interest with the
increasing prevalence of high-dimensional data. The challenge is to develop
statistical models and autonomous algorithms that are able to acquire knowledge
from raw data for exploratory analysis, which can be achieved through
clustering techniques or to make predictions of future data via classification
(i.e., discriminant analysis) techniques. Latent data models, including mixture
model-based approaches are one of the most popular and successful approaches in
both the unsupervised context (i.e., clustering) and the supervised one (i.e,
classification or discrimination). Although traditionally tools of multivariate
analysis, they are growing in popularity when considered in the framework of
functional data analysis (FDA). FDA is the data analysis paradigm in which the
individual data units are functions (e.g., curves, surfaces), rather than
simple vectors. In many areas of application, the analyzed data are indeed
often available in the form of discretized values of functions or curves (e.g.,
time series, waveforms) and surfaces (e.g., 2d-images, spatio-temporal data).
This functional aspect of the data adds additional difficulties compared to the
case of a classical multivariate (non-functional) data analysis. We review and
present approaches for model-based clustering and classification of functional
data. We derive well-established statistical models along with efficient
algorithmic tools to address problems regarding the clustering and the
classification of these high-dimensional data, including their heterogeneity,
missing information, and dynamical hidden structure. The presented models and
algorithms are illustrated on real-world functional data analysis problems from
several application area.
Raw Multi-Channel Audio Source Separation using Multi-Resolution Convolutional Auto-Encoders
Authors: Emad M. Grais, Dominic Ward, Mark D. Plumbley
Date: 2018-03-02 00:00:00
Tasks: Audio Source Separation

Supervised multi-channel audio source separation requires extracting useful
spectral, temporal, and spatial features from the mixed signals. The success of
many existing systems is therefore largely dependent on the choice of features
used for training. In this work, we introduce a novel multi-channel,
multi-resolution convolutional auto-encoder neural network that works on raw
time-domain signals to determine appropriate multi-resolution features for
separating the singing-voice from stereo music. Our experimental results show
that the proposed method can achieve multi-channel audio source separation
without the need for hand-crafted features or any pre- or post-processing.