pwc-archive/papers-with-abstracts huggingface dataset:
A Computational Framework for Multi-Modal Social Action Identification
Authors: Jason Anastasopoulos, Jake Ryland Williams
Date: 2017-10-20 00:00:00
Tasks: BIG-bench Machine Learning, Event Detection

We create a computational framework for understanding social action and
demonstrate how this framework can be used to build an open-source event
detection tool with scalable statistical machine learning algorithms and a
subsampled database of over 600 million geo-tagged Tweets from around the
world. These Tweets were collected between April 1st, 2014 and April 30th,
2015, most notably when the Black Lives Matter movement began. We demonstrate
how these methods can be used diagnostically-by researchers, government
officials and the public-to understand peaceful and violent collective action
at very fine-grained levels of time and geography.
"Parallel Training Considered Harmful?": Comparing series-parallel and parallel feedforward network training
Authors: Antônio H. Ribeiro, Luis A. Aguirre
Date: 2017-06-21 00:00:00

Neural network models for dynamic systems can be trained either in parallel
or in series-parallel configurations. Influenced by early arguments, several
papers justify the choice of series-parallel rather than parallel configuration
claiming it has a lower computational cost, better stability properties during
training and provides more accurate results. Other published results, on the
other hand, defend parallel training as being more robust and capable of
yielding more accu- rate long-term predictions. The main contribution of this
paper is to present a study comparing both methods under the same unified
framework. We focus on three aspects: i) robustness of the estimation in the
presence of noise; ii) computational cost; and, iii) convergence. A unifying
mathematical framework and simulation studies show situations where each
training method provides better validation results, being parallel training
better in what is believed to be more realistic scenarios. An example using
measured data seems to reinforce such claim. We also show, with a novel
complexity analysis and numerical examples, that both methods have similar
computational cost, being series series-parallel training, however, more
amenable to parallelization. Some informal discussion about stability and
convergence properties is presented and explored in the examples.
A Bayesian Method for Joint Clustering of Vectorial Data and Network Data
Authors: Yunchuan Kong, Xiaodan Fan
Date: 2017-10-24 00:00:00
Tasks: Bayesian Inference, Clustering, Stochastic Block Model

We present a new model-based integrative method for clustering objects given
both vectorial data, which describes the feature of each object, and network
data, which indicates the similarity of connected objects. The proposed general
model is able to cluster the two types of data simultaneously within one
integrative probabilistic model, while traditional methods can only handle one
data type or depend on transforming one data type to another. Bayesian
inference of the clustering is conducted based on a Markov chain Monte Carlo
algorithm. A special case of the general model combining the Gaussian mixture
model and the stochastic block model is extensively studied. We used both
synthetic data and real data to evaluate this new method and compare it with
alternative methods. The results show that our simultaneous clustering method
performs much better. This improvement is due to the power of the model-based
probabilistic approach for efficiently integrating information.
Deep Triphone Embedding Improves Phoneme Recognition
Authors: Mohit Yadav, Vivek Tyagi
Date: 2017-10-22 00:00:00
Tasks: Dimensionality Reduction, General Classification, Phoneme Recognition

In this paper, we present a novel Deep Triphone Embedding (DTE)
representation derived from Deep Neural Network (DNN) to encapsulate the
discriminative information present in the adjoining speech frames. DTEs are
generated using a four hidden layer DNN with 3000 nodes in each hidden layer at
the first-stage. This DNN is trained with the tied-triphone classification
accuracy as an optimization criterion. Thereafter, we retain the activation
vectors (3000) of the last hidden layer, for each speech MFCC frame, and
perform dimension reduction to further obtain a 300 dimensional representation,
which we termed as DTE. DTEs along with MFCC features are fed into a
second-stage four hidden layer DNN, which is subsequently trained for the task
of tied-triphone classification. Both DNNs are trained using tri-phone labels
generated from a tied-state triphone HMM-GMM system, by performing a
forced-alignment between the transcriptions and MFCC feature frames. We conduct
the experiments on publicly available TED-LIUM speech corpus. The results show
that the proposed DTE method provides an improvement of absolute 2.11% in
phoneme recognition, when compared with a competitive hybrid tied-state
triphone HMM-DNN system.
Algorithmic infeasibility of community detection in higher-order networks
Authors: Tatsuro Kawamoto
Date: 2017-10-24 00:00:00
Tasks: Community Detection

In principle, higher-order networks that have multiple edge types are more
informative than their lower-order counterparts. In practice, however,
excessively rich information may be algorithmically infeasible to extract. It
requires an algorithm that assumes a high-dimensional model and such an
algorithm may perform poorly or be extremely sensitive to the initial estimate
of the model parameters. Herein, we address this problem of community detection
through a detectability analysis. We focus on the expectation-maximization (EM)
algorithm with belief propagation (BP), and analytically derive its algorithmic
detectability threshold, i.e., the limit of the modular structure strength
below which the algorithm can no longer detect any modular structures. The
results indicate the existence of a phase in which the community detection of a
lower-order network outperforms its higher-order counterpart.
Markov Properties for Graphical Models with Cycles and Latent Variables
Authors: Patrick Forré, Joris M. Mooij
Date: 2017-10-24 00:00:00

We investigate probabilistic graphical models that allow for both cycles and
latent variables. For this we introduce directed graphs with hyperedges
(HEDGes), generalizing and combining both marginalized directed acyclic graphs
(mDAGs) that can model latent (dependent) variables, and directed mixed graphs
(DMGs) that can model cycles. We define and analyse several different Markov
properties that relate the graphical structure of a HEDG with a probability
distribution on a corresponding product space over the set of nodes, for
example factorization properties, structural equations properties,
ordered/local/global Markov properties, and marginal versions of these. The
various Markov properties for HEDGes are in general not equivalent to each
other when cycles or hyperedges are present, in contrast with the simpler case
of directed acyclic graphical (DAG) models (also known as Bayesian networks).
We show how the Markov properties for HEDGes - and thus the corresponding
graphical Markov models - are logically related to each other.
A Linear-Time Kernel Goodness-of-Fit Test
Authors: Wittawat Jitkrittum, Wenkai Xu, Zoltan Szabo, Kenji Fukumizu, Arthur Gretton
Date: 2017-05-22 00:00:00
Proceeding: NeurIPS 2017 12

We propose a novel adaptive test of goodness-of-fit, with computational cost
linear in the number of samples. We learn the test features that best indicate
the differences between observed samples and a reference model, by minimizing
the false negative rate. These features are constructed via Stein's method,
meaning that it is not necessary to compute the normalising constant of the
model. We analyse the asymptotic Bahadur efficiency of the new test, and prove
that under a mean-shift alternative, our test always has greater relative
efficiency than a previous linear-time kernel test, regardless of the choice of
parameters for that test. In experiments, the performance of our method exceeds
that of the earlier linear-time test, and matches or exceeds the power of a
quadratic-time kernel test. In high dimensions and where model structure may be
exploited, our goodness of fit test performs far better than a quadratic-time
two-sample test based on the Maximum Mean Discrepancy, with samples drawn from
the model.
A Correction Method of a Binary Classifier Applied to Multi-label Pairwise Models
Authors: Pawel Trajdos, Marek Kurzynski
Date: 2017-10-24 00:00:00
Tasks: General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

In this work, we addressed the issue of applying a stochastic classifier and
a local, fuzzy confusion matrix under the framework of multi-label
classification. We proposed a novel solution to the problem of correcting label
pairwise ensembles. The main step of the correction procedure is to compute
classifier- specific competence and cross-competence measures, which estimates
error pattern of the underlying classifier. We considered two improvements of
the method of obtaining confusion matrices. The first one is aimed to deal with
imbalanced labels. The other utilizes double labelled instances which are
usually removed during the pairwise transformation. The proposed methods were
evaluated using 29 benchmark datasets. In order to assess the efficiency of the
introduced models, they were compared against 1 state-of-the-art approach and
the correction scheme based on the original method of confusion matrix
estimation. The comparison was performed using four different multi-label
evaluation measures: macro and micro-averaged F1 loss, zero-one loss and
Hamming loss. Additionally, we investigated relations between classification
quality, which is expressed in terms of different quality criteria, and
characteristics of multi-label datasets such as average imbalance ratio or
label density. The experimental study reveals that the correction approaches
significantly outperforms the reference method only in terms of zero-one loss.
Greater data science at baccalaureate institutions
Authors: Amelia McNamara, Nicholas J. Horton, Benjamin S. Baumer
Date: 2017-10-24 00:00:00

Donoho's JCGS (in press) paper is a spirited call to action for
statisticians, who he points out are losing ground in the field of data science
by refusing to accept that data science is its own domain. (Or, at least, a
domain that is becoming distinctly defined.) He calls on writings by John
Tukey, Bill Cleveland, and Leo Breiman, among others, to remind us that
statisticians have been dealing with data science for years, and encourages
acceptance of the direction of the field while also ensuring that statistics is
tightly integrated.
  As faculty at baccalaureate institutions (where the growth of undergraduate
statistics programs has been dramatic), we are keen to ensure statistics has a
place in data science and data science education. In his paper, Donoho is
primarily focused on graduate education. At our undergraduate institutions, we
are considering many of the same questions.
Clickbait Identification using Neural Networks
Authors: Philippe Thomas
Date: 2017-10-24 00:00:00
Tasks: Clickbait Detection

This paper presents the results of our participation in the Clickbait
Detection Challenge 2017. The system relies on a fusion of neural networks,
incorporating different types of available informations. It does not require
any linguistic preprocessing, and hence generalizes more easily to new domains
and languages. The final combined model achieves a mean squared error of
0.0428, an accuracy of 0.826, and a F1 score of 0.564. According to the
official evaluation metric the system ranked 6th of the 13 participating teams.
Continuous Multimodal Emotion Recognition Approach for AVEC 2017
Authors: Narotam Singh, Nittin Singh, Abhinav Dhall
Date: 2017-09-18 00:00:00
Tasks: Emotion Recognition, Multimodal Emotion Recognition

This paper reports the analysis of audio and visual features in predicting
the continuous emotion dimensions under the seventh Audio/Visual Emotion
Challenge (AVEC 2017), which was done as part of a B.Tech. 2nd year internship
project. For visual features we used the HOG (Histogram of Gradients) features,
Fisher encodings of SIFT (Scale-Invariant Feature Transform) features based on
Gaussian mixture model (GMM) and some pretrained Convolutional Neural Network
layers as features; all these extracted for each video clip. For audio features
we used the Bag-of-audio-words (BoAW) representation of the LLDs (low-level
descriptors) generated by openXBOW provided by the organisers of the event.
Then we trained fully connected neural network regression model on the dataset
for all these different modalities. We applied multimodal fusion on the output
models to get the Concordance correlation coefficient on Development set as
well as Test set.
Automated Audio Captioning with Recurrent Neural Networks
Authors: Konstantinos Drossos, Sharath Adavanne, Tuomas Virtanen
Date: 2017-06-30 00:00:00
Tasks: Audio captioning, Decoder, General Classification, Image Captioning, Machine Translation, Translation

We present the first approach to automated audio captioning. We employ an
encoder-decoder scheme with an alignment model in between. The input to the
encoder is a sequence of log mel-band energies calculated from an audio file,
while the output is a sequence of words, i.e. a caption. The encoder is a
multi-layered, bi-directional gated recurrent unit (GRU) and the decoder a
multi-layered GRU with a classification layer connected to the last GRU of the
decoder. The classification layer and the alignment model are fully connected
layers with shared weights between timesteps. The proposed method is evaluated
using data drawn from a commercial sound effects library, ProSound Effects. The
resulting captions were rated through metrics utilized in machine translation
and image captioning fields. Results from metrics show that the proposed method
can predict words appearing in the original caption, but not always correctly
ordered.
Beat by Beat: Classifying Cardiac Arrhythmias with Recurrent Neural Networks
Authors: Patrick Schwab, Gaetano Scebba, Jia Zhang, Marco Delai, Walter Karlen
Date: 2017-10-17 00:00:00
Tasks: General Classification, Rhythm

With tens of thousands of electrocardiogram (ECG) records processed by mobile
cardiac event recorders every day, heart rhythm classification algorithms are
an important tool for the continuous monitoring of patients at risk. We utilise
an annotated dataset of 12,186 single-lead ECG recordings to build a diverse
ensemble of recurrent neural networks (RNNs) that is able to distinguish
between normal sinus rhythms, atrial fibrillation, other types of arrhythmia
and signals that are too noisy to interpret. In order to ease learning over the
temporal dimension, we introduce a novel task formulation that harnesses the
natural segmentation of ECG signals into heartbeats to drastically reduce the
number of time steps per sequence. Additionally, we extend our RNNs with an
attention mechanism that enables us to reason about which heartbeats our RNNs
focus on to make their decisions. Through the use of attention, our model
maintains a high degree of interpretability, while also achieving
state-of-the-art classification performance with an average F1 score of 0.79 on
an unseen test set (n=3,658).
How Important is Syntactic Parsing Accuracy? An Empirical Evaluation on Rule-Based Sentiment Analysis
Authors: Carlos Gómez-Rodríguez, Iago Alonso-Alonso, David Vilares
Date: 2017-06-07 00:00:00
Tasks: Sentiment Analysis

Syntactic parsing, the process of obtaining the internal structure of
sentences in natural languages, is a crucial task for artificial intelligence
applications that need to extract meaning from natural language text or speech.
Sentiment analysis is one example of application for which parsing has recently
proven useful.
  In recent years, there have been significant advances in the accuracy of
parsing algorithms. In this article, we perform an empirical, task-oriented
evaluation to determine how parsing accuracy influences the performance of a
state-of-the-art rule-based sentiment analysis system that determines the
polarity of sentences from their parse trees. In particular, we evaluate the
system using four well-known dependency parsers, including both current models
with state-of-the-art accuracy and more innacurate models which, however,
require less computational resources.
  The experiments show that all of the parsers produce similarly good results
in the sentiment analysis task, without their accuracy having any relevant
influence on the results. Since parsing is currently a task with a relatively
high computational cost that varies strongly between algorithms, this suggests
that sentiment analysis researchers and users should prioritize speed over
accuracy when choosing a parser; and parsing researchers should investigate
models that improve speed further, even at some cost to accuracy.
Experimental comparison of single-pixel imaging algorithms
Authors: Liheng Bian, Jinli Suo, Qionghai Dai, Feng Chen
Date: 2017-07-11 00:00:00
Tasks: Compressive Sensing

Single-pixel imaging (SPI) is a novel technique capturing 2D images using a
photodiode, instead of conventional 2D array sensors. SPI owns high
signal-to-noise ratio, wide spectrum range, low cost, and robustness to light
scattering. Various algorithms have been proposed for SPI reconstruction,
including the linear correlation methods, the alternating projection method
(AP), and the compressive sensing based methods. However, there has been no
comprehensive review discussing respective advantages, which is important for
SPI's further applications and development. In this paper, we reviewed and
compared these algorithms in a unified reconstruction framework. Besides, we
proposed two other SPI algorithms including a conjugate gradient descent based
method (CGD) and a Poisson maximum likelihood based method. Both simulations
and experiments validate the following conclusions: to obtain comparable
reconstruction accuracy, the compressive sensing based total variation
regularization method (TV) requires the least measurements and consumes the
least running time for small-scale reconstruction; the CGD and AP methods run
fastest in large-scale cases; the TV and AP methods are the most robust to
measurement noise. In a word, there are trade-offs between capture efficiency,
computational complexity and robustness to noise among different SPI
algorithms. We have released our source code for non-commercial use.
Improving Accuracy of Nonparametric Transfer Learning via Vector Segmentation
Authors: Vincent Gripon, Ghouthi B. Hacene, Matthias Löwe, Franck Vermet
Date: 2017-10-24 00:00:00
Tasks: Transfer Learning

Transfer learning using deep neural networks as feature extractors has become
increasingly popular over the past few years. It allows to obtain
state-of-the-art accuracy on datasets too small to train a deep neural network
on its own, and it provides cutting edge descriptors that, combined with
nonparametric learning methods, allow rapid and flexible deployment of
performing solutions in computationally restricted settings. In this paper, we
are interested in showing that the features extracted using deep neural
networks have specific properties which can be used to improve accuracy of
downstream nonparametric learning methods. Namely, we demonstrate that for some
distributions where information is embedded in a few coordinates, segmenting
feature vectors can lead to better accuracy. We show how this model can be
applied to real datasets by performing experiments using three mainstream deep
neural network feature extractors and four databases, in vision and audio.
algcomparison: Comparing the Performance of Graphical Structure Learning Algorithms with TETRAD
Authors: Joseph D. Ramsey, Daniel Malinsky, Kevin V. Bui
Date: 2016-07-27 00:00:00

In this report we describe a tool for comparing the performance of graphical causal structure learning algorithms implemented in the TETRAD freeware suite of causal analysis methods. Currently the tool is available as package in the TETRAD source code (written in Java). Simulations can be done varying the number of runs, sample sizes, and data modalities. Performance on this simulated data can then be compared for a number of algorithms, with parameters varied and with performance statistics as selected, producing a publishable report. The package presented here may also be used to compare structure learning methods across platforms and programming languages, i.e., to compare algorithms implemented in TETRAD with those implemented in MATLAB, Python, or R.
Using Multi-Label Classification for Improved Question Answering
Authors: Ricardo Usbeck, Michael Hoffmann, Michael Röder, Jens Lehmann, Axel-Cyrille Ngonga Ngomo
Date: 2017-10-24 00:00:00
Tasks: Classification, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Question Answering

A plethora of diverse approaches for question answering over RDF data have
been developed in recent years. While the accuracy of these systems has
increased significantly over time, most systems still focus on particular types
of questions or particular challenges in question answering. What is a curse
for single systems is a blessing for the combination of these systems. We show
in this paper how machine learning techniques can be applied to create a more
accurate question answering metasystem by reusing existing systems. In
particular, we develop a multi-label classification-based metasystem for
question answering over 6 existing systems using an innovative set of 14
question features. The metasystem outperforms the best single system by 14%
F-measure on the recent QALD-6 benchmark. Furthermore, we analyzed the
influence and correlation of the underlying features on the metasystem quality.
A Mathematical Theory of Deep Convolutional Neural Networks for Feature Extraction
Authors: Thomas Wiatowski, Helmut Bölcskei
Date: 2015-12-19 00:00:00
Tasks: Atari Games, Image Captioning, Translation

Deep convolutional neural networks have led to breakthrough results in
numerous practical machine learning tasks such as classification of images in
the ImageNet data set, control-policy-learning to play Atari games or the board
game Go, and image captioning. Many of these applications first perform feature
extraction and then feed the results thereof into a trainable classifier. The
mathematical analysis of deep convolutional neural networks for feature
extraction was initiated by Mallat, 2012. Specifically, Mallat considered
so-called scattering networks based on a wavelet transform followed by the
modulus non-linearity in each network layer, and proved translation invariance
(asymptotically in the wavelet scale parameter) and deformation stability of
the corresponding feature extractor. This paper complements Mallat's results by
developing a theory that encompasses general convolutional transforms, or in
more technical parlance, general semi-discrete frames (including
Weyl-Heisenberg filters, curvelets, shearlets, ridgelets, wavelets, and learned
filters), general Lipschitz-continuous non-linearities (e.g., rectified linear
units, shifted logistic sigmoids, hyperbolic tangents, and modulus functions),
and general Lipschitz-continuous pooling operators emulating, e.g.,
sub-sampling and averaging. In addition, all of these elements can be different
in different network layers. For the resulting feature extractor we prove a
translation invariance result of vertical nature in the sense of the features
becoming progressively more translation-invariant with increasing network
depth, and we establish deformation sensitivity bounds that apply to signal
classes such as, e.g., band-limited functions, cartoon functions, and Lipschitz
functions.