pwc-archive/papers-with-abstracts huggingface dataset:
Page Stream Segmentation with Convolutional Neural Nets Combining Textual and Visual Features
Authors: Gregor Wiedemann, Gerhard Heyer
Date: 2017-10-09 00:00:00
Proceeding: LREC 2018 5
Tasks: Optical Character Recognition, Optical Character Recognition (OCR), Page Stream Segmentation

In recent years, (retro-)digitizing paper-based files became a major
undertaking for private and public archives as well as an important task in
electronic mailroom applications. As a first step, the workflow involves
scanning and Optical Character Recognition (OCR) of documents. Preservation of
document contexts of single page scans is a major requirement in this context.
To facilitate workflows involving very large amounts of paper scans, page
stream segmentation (PSS) is the task to automatically separate a stream of
scanned images into multi-page documents. In a digitization project together
with a German federal archive, we developed a novel approach based on
convolutional neural networks (CNN) combining image and text features to
achieve optimal document separation results. Evaluation shows that our PSS
architecture achieves an accuracy up to 93 % which can be regarded as a new
state-of-the-art for this task.
Learning Sparse Wavelet Representations
Authors: Daniel Recoskie, Richard Mann
Date: 2018-02-08 00:00:00

In this work we propose a method for learning wavelet filters directly from
data. We accomplish this by framing the discrete wavelet transform as a
modified convolutional neural network. We introduce an autoencoder wavelet
transform network that is trained using gradient descent. We show that the
model is capable of learning structured wavelet filters from synthetic and real
data. The learned wavelets are shown to be similar to traditional wavelets that
are derived using Fourier methods. Our method is simple to implement and easily
incorporated into neural network architectures. A major advantage to our model
is that we can learn from raw audio data.
TSViz: Demystification of Deep Learning Models for Time-Series Analysis
Authors: Shoaib Ahmed Siddiqui, Dominik Mercier, Mohsin Munir, Andreas Dengel, Sheraz Ahmed
Date: 2018-02-08 00:00:00
Tasks: Clustering, Deep Learning, Self-Driving Cars, Time Series, Time Series Analysis

This paper presents a novel framework for demystification of convolutional deep learning models for time-series analysis. This is a step towards making informed/explainable decisions in the domain of time-series, powered by deep learning. There have been numerous efforts to increase the interpretability of image-centric deep neural network models, where the learned features are more intuitive to visualize. Visualization in time-series domain is much more complicated as there is no direct interpretation of the filters and inputs as compared to the image modality. In addition, little or no concentration has been devoted for the development of such tools in the domain of time-series in the past. TSViz provides possibilities to explore and analyze a network from different dimensions at different levels of abstraction which includes identification of parts of the input that were responsible for a prediction (including per filter saliency), importance of different filters present in the network for a particular prediction, notion of diversity present in the network through filter clustering, understanding of the main sources of variation learnt by the network through inverse optimization, and analysis of the network's robustness against adversarial noise. As a sanity check for the computed influence values, we demonstrate results regarding pruning of neural networks based on the computed influence information. These representations allow to understand the network features so that the acceptability of deep networks for time-series data can be enhanced. This is extremely important in domains like finance, industry 4.0, self-driving cars, health-care, counter-terrorism etc., where reasons for reaching a particular prediction are equally important as the prediction itself. We assess the proposed framework for interpretability with a set of desirable properties essential for any method.
Users Constraints in Itemset Mining
Authors: Christian Bessiere, Nadjib Lazaar, Yahia Lebbah, Mehdi Maamar
Date: 2017-12-31 00:00:00

Discovering significant itemsets is one of the fundamental problems in data
mining. It has recently been shown that constraint programming is a flexible
way to tackle data mining tasks. With a constraint programming approach, we can
easily express and efficiently answer queries with users constraints on items.
However, in many practical cases it is possible that queries also express users
constraints on the dataset itself. For instance, asking for a particular
itemset in a particular part of the dataset. This paper presents a general
constraint programming model able to handle any kind of query on the items or
the dataset for itemset mining.
Active learning machine learns to create new quantum experiments
Authors: Alexey A. Melnikov, Hendrik Poulsen Nautrup, Mario Krenn, Vedran Dunjko, Markus Tiersch, Anton Zeilinger, Hans J. Briegel
Date: 2017-06-02 00:00:00
Tasks: Active Learning

How useful can machine learning be in a quantum laboratory? Here we raise the
question of the potential of intelligent machines in the context of scientific
research. A major motivation for the present work is the unknown reachability
of various entanglement classes in quantum experiments. We investigate this
question by using the projective simulation model, a physics-oriented approach
to artificial intelligence. In our approach, the projective simulation system
is challenged to design complex photonic quantum experiments that produce
high-dimensional entangled multiphoton states, which are of high interest in
modern quantum experiments. The artificial intelligence system learns to create
a variety of entangled states, and improves the efficiency of their
realization. In the process, the system autonomously (re)discovers experimental
techniques which are only now becoming standard in modern quantum optical
experiments - a trait which was not explicitly demanded from the system but
emerged through the process of learning. Such features highlight the
possibility that machines could have a significantly more creative role in
future research.
DisMo: A Morphosyntactic, Disfluency and Multi-Word Unit Annotator. An Evaluation on a Corpus of French Spontaneous and Read Speech
Authors: George Christodoulides, Mathieu Avanzi, Jean-Philippe Goldman
Date: 2018-02-08 00:00:00
Proceeding: LREC 2014 5
Tasks: Part-Of-Speech Tagging, POS, POS Tagging, TAG

We present DisMo, a multi-level annotator for spoken language corpora that
integrates part-of-speech tagging with basic disfluency detection and
annotation, and multi-word unit recognition. DisMo is a hybrid system that uses
a combination of lexical resources, rules, and statistical models based on
Conditional Random Fields (CRF). In this paper, we present the first public
version of DisMo for French. The system is trained and its performance
evaluated on a 57k-token corpus, including different varieties of French spoken
in three countries (Belgium, France and Switzerland). DisMo supports a
multi-level annotation scheme, in which the tokenisation to minimal word units
is complemented with multi-word unit groupings (each having associated POS
tags), as well as separate levels for annotating disfluencies and discourse
phenomena. We present the system's architecture, linguistic resources and its
hierarchical tag-set. Results show that DisMo achieves a precision of 95%
(finest tag-set) to 96.8% (coarse tag-set) in POS-tagging non-punctuated,
sound-aligned transcriptions of spoken French, while also offering substantial
possibilities for automated multi-level annotation.
Generative Cooperative Net for Image Generation and Data Augmentation
Authors: Qiangeng Xu, Zengchang Qin, Tao Wan
Date: 2017-05-08 00:00:00
Tasks: Data Augmentation, Facial expression generation, Image Generation

How to build a good model for image generation given an abstract concept is a
fundamental problem in computer vision. In this paper, we explore a generative
model for the task of generating unseen images with desired features. We
propose the Generative Cooperative Net (GCN) for image generation. The idea is
similar to generative adversarial networks except that the generators and
discriminators are trained to work accordingly. Our experiments on hand-written
digit generation and facial expression generation show that GCN's two
cooperative counterparts (the generator and the classifier) can work together
nicely and achieve promising results. We also discovered a usage of such
generative model as an data-augmentation tool. Our experiment of applying this
method on a recognition task shows that it is very effective comparing to other
existing methods. It is easy to set up and could help generate a very large
synthesized dataset.
Spectral State Compression of Markov Processes
Authors: Anru Zhang, Mengdi Wang
Date: 2018-02-08 00:00:00
Tasks: Clustering

Model reduction of Markov processes is a basic problem in modeling state-transition systems. Motivated by the state aggregation approach rooted in control theory, we study the statistical state compression of a discrete-state Markov chain from empirical trajectories. Through the lens of spectral decomposition, we study the rank and features of Markov processes, as well as properties like representability, aggregability, and lumpability. We develop spectral methods for estimating the transition matrix of a low-rank Markov model, estimating the leading subspace spanned by Markov features, and recovering latent structures like state aggregation and lumpable partition of the state space. We prove statistical upper bounds for the estimation errors and nearly matching minimax lower bounds. Numerical studies are performed on synthetic data and a dataset of New York City taxi trips.
Praaline: Integrating Tools for Speech Corpus Research
Authors: George Christodoulides
Date: 2018-02-08 00:00:00
Proceeding: LREC 2014 5

This paper presents Praaline, an open-source software system for managing,
annotating, analysing and visualising speech corpora. Researchers working with
speech corpora are often faced with multiple tools and formats, and they need
to work with ever-increasing amounts of data in a collaborative way. Praaline
integrates and extends existing time-proven tools for spoken corpora analysis
(Praat, Sonic Visualiser and a bridge to the R statistical package) in a
modular system, facilitating automation and reuse. Users are exposed to an
integrated, user-friendly interface from which to access multiple tools. Corpus
metadata and annotations may be stored in a database, locally or remotely, and
users can define the metadata and annotation structure. Users may run a
customisable cascade of analysis steps, based on plug-ins and scripts, and
update the database with the results. The corpus database may be queried, to
produce aggregated data-sets. Praaline is extensible using Python or C++
plug-ins, while Praat and R scripts may be executed against the corpus data. A
series of visualisations, editors and plug-ins are provided. Praaline is free
software, released under the GPL license.
Spatial Decompositions for Large Scale SVMs
Authors: Philipp Thomann, Ingrid Blaschzyk, Mona Meister, Ingo Steinwart
Date: 2016-12-01 00:00:00

Although support vector machines (SVMs) are theoretically well understood,
their underlying optimization problem becomes very expensive, if, for example,
hundreds of thousands of samples and a non-linear kernel are considered.
Several approaches have been proposed in the past to address this serious
limitation. In this work we investigate a decomposition strategy that learns on
small, spatially defined data chunks. Our contributions are two fold: On the
theoretical side we establish an oracle inequality for the overall learning
method using the hinge loss, and show that the resulting rates match those
known for SVMs solving the complete optimization problem with Gaussian kernels.
On the practical side we compare our approach to learning SVMs on small,
randomly chosen chunks. Here it turns out that for comparable training times
our approach is significantly faster during testing and also reduces the test
error in most cases significantly. Furthermore, we show that our approach
easily scales up to 10 million training samples: including hyper-parameter
selection using cross validation, the entire training only takes a few hours on
a single machine. Finally, we report an experiment on 32 million training
samples. All experiments used liquidSVM (Steinwart and Thomann, 2017).
Metrics for Deep Generative Models
Authors: Nutan Chen, Alexej Klushyn, Richard Kurle, Xueyan Jiang, Justin Bayer, Patrick van der Smagt
Date: 2017-11-03 00:00:00

Neural samplers such as variational autoencoders (VAEs) or generative
adversarial networks (GANs) approximate distributions by transforming samples
from a simple random source---the latent space---to samples from a more complex
distribution represented by a dataset. While the manifold hypothesis implies
that the density induced by a dataset contains large regions of low density,
the training criterions of VAEs and GANs will make the latent space densely
covered. Consequently points that are separated by low-density regions in
observation space will be pushed together in latent space, making stationary
distances poor proxies for similarity. We transfer ideas from Riemannian
geometry to this setting, letting the distance between two points be the
shortest path on a Riemannian manifold induced by the transformation. The
method yields a principled distance measure, provides a tool for visual
inspection of deep generative models, and an alternative to linear
interpolation in latent space. In addition, it can be applied for robot
movement generalization using previously learned skills. The method is
evaluated on a synthetic dataset with known ground truth; on a simulated robot
arm dataset; on human motion capture data; and on a generative model of
handwritten digits.
Online Decomposition of Compressive Streaming Data Using $n$-$\ell_1$ Cluster-Weighted Minimization
Authors: Huynh Van Luong, Nikos Deligiannis, Søren Forchhammer, André Kaup
Date: 2018-02-08 00:00:00
Tasks: Clustering

We consider a decomposition method for compressive streaming data in the
context of online compressive Robust Principle Component Analysis (RPCA). The
proposed decomposition solves an $n$-$\ell_1$ cluster-weighted minimization to
decompose a sequence of frames (or vectors), into sparse and low-rank
components, from compressive measurements. Our method processes a data vector
of the stream per time instance from a small number of measurements in contrast
to conventional batch RPCA, which needs to access full data. The $n$-$\ell_1$
cluster-weighted minimization leverages the sparse components along with their
correlations with multiple previously-recovered sparse vectors. Moreover, the
proposed minimization can exploit the structures of sparse components via
clustering and re-weighting iteratively. The method outperforms the existing
methods for both numerical data and actual video data.
Online Learning: A Comprehensive Survey
Authors: Steven C. H. Hoi, Doyen Sahoo, Jing Lu, Peilin Zhao
Date: 2018-02-08 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making, Survey

Online learning represents an important family of machine learning
algorithms, in which a learner attempts to resolve an online prediction (or any
type of decision-making) task by learning a model/hypothesis from a sequence of
data instances one at a time. The goal of online learning is to ensure that the
online learner would make a sequence of accurate predictions (or correct
decisions) given the knowledge of correct answers to previous prediction or
learning tasks and possibly additional information. This is in contrast to many
traditional batch learning or offline machine learning algorithms that are
often designed to train a model in batch from a given collection of training
data instances. This survey aims to provide a comprehensive survey of the
online machine learning literatures through a systematic review of basic ideas
and key principles and a proper categorization of different algorithms and
techniques. Generally speaking, according to the learning type and the forms of
feedback information, the existing online learning works can be classified into
three major categories: (i) supervised online learning where full feedback
information is always available, (ii) online learning with limited feedback,
and (iii) unsupervised online learning where there is no feedback available.
Due to space limitation, the survey will be mainly focused on the first
category, but also briefly cover some basics of the other two categories.
Finally, we also discuss some open issues and attempt to shed light on
potential future research directions in this field.
An Information-Theoretic Optimality Principle for Deep Reinforcement Learning
Authors: Felix Leibfried, Jordi Grau-Moya, Haitham Bou-Ammar
Date: 2017-08-06 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Scheduling, valid

We methodologically address the problem of Q-value overestimation in deep
reinforcement learning to handle high-dimensional state spaces efficiently. By
adapting concepts from information theory, we introduce an intrinsic penalty
signal encouraging reduced Q-value estimates. The resultant algorithm
encompasses a wide range of learning outcomes containing deep Q-networks as a
special case. Different learning outcomes can be demonstrated by tuning a
Lagrange multiplier accordingly. We furthermore propose a novel scheduling
scheme for this Lagrange multiplier to ensure efficient and robust learning. In
experiments on Atari, our algorithm outperforms other algorithms (e.g. deep and
double deep Q-networks) in terms of both game-play performance and sample
complexity. These results remain valid under the recently proposed dueling
architecture.
Unsupervised Classification of Galaxies. I. ICA feature selection
Authors: Didier Fraix-Burnet, Tanuka Chattopadhyay, Saptarshi Mondal
Date: 2018-02-08 00:00:00
Tasks: Classification, Diagnostic, feature selection, General Classification

Subjective classification of galaxies can mislead us in the quest of the
origin regarding formation and evolution of galaxies since this is necessarily
limited to a few features. The human mind is not able to apprehend the complex
correlations in a manyfold parameter space, and multivariate analyses are the
best tools to understand the differences among various kinds of objects. In
this series of papers, an objective classification of 362,923 galaxies from the
Value Added Galaxy Catalogue (VAGC) is carried out with the help of two methods
of multivariate analysis. First, Independent Component Analysis (ICA) is used
to determine a set of derived independent components that are linear
combinations of 47 observed features (viz. ionized lines, Lick indices,
photometric and morphological properties, star formation rates etc.) of the
galaxies. Subsequently, a K-means cluster analysis is applied on the nine
independent components to obtain ten distinct and homogeneous groups. In this
first paper, we describe the methods and the main results. It appears that the
nine Independent Components represent a complete physical description of
galaxies (velocity dispersion, ionisation, metallicity, surface brightness and
structure). We find that our ten groups can be essentially placed into
traditional and empirical classes (from colour-magnitude and emission-line
diagnostic diagrams, early- vs late-types) despite the classical corresponding
features (colour, line ratios and morphology) being not significantly
correlated with the nine Independent Components. More detailed physical
interpretation of the groups will be performed in subsequent papers.
Smart energy management as a means towards improved energy efficiency
Authors: Dylan te Lindert, Cláudio Rebelo de Sá, Carlos Soares, Arno J. Knobbe
Date: 2018-02-08 00:00:00
Tasks: energy management, Management

The costs associated with refrigerator equipment often represent more than
half of the total energy costs in supermarkets. This presents a good motivation
for running these systems efficiently. In this study, we investigate different
ways to construct a reference behavior, which can serve as a baseline for
judging the performance of energy consumption. We used 3 distinct learning
models: Multiple Linear Regression, Random Forests, and Artificial Neural
Networks. During our experiments we used a variation of the sliding window
method in combination with learning curves. We applied this approach on five
different supermarkets, across Portugal. We are able to create baselines using
off-the-shelf data mining techniques. Moreover, we found a way to create them
based on short term historical data. We believe that our research will serve as
a base for future studies, for which we provide interesting directions.
Using a reservoir computer to learn chaotic attractors, with applications to chaos synchronisation and cryptography
Authors: Piotr Antonik, Marvyn Gulina, Jaël Pauwels, Serge Massar
Date: 2018-02-08 00:00:00

Using the machine learning approach known as reservoir computing, it is
possible to train one dynamical system to emulate another. We show that such
trained reservoir computers reproduce the properties of the attractor of the
chaotic system sufficiently well to exhibit chaos synchronisation. That is, the
trained reservoir computer, weakly driven by the chaotic system, will
synchronise with the chaotic system. Conversely, the chaotic system, weakly
driven by a trained reservoir computer, will synchronise with the reservoir
computer. We illustrate this behaviour on the Mackey-Glass and Lorenz systems.
We then show that trained reservoir computers can be used to crack chaos based
cryptography and illustrate this on a chaos cryptosystem based on the
Mackey-Glass system. We conclude by discussing why reservoir computers are so
good at emulating chaotic systems.
Pose-based Deep Gait Recognition
Authors: Anna Sokolova, Anton Konushin
Date: 2017-10-17 00:00:00
Tasks: Gait Recognition, Optical Flow Estimation

Human gait or walking manner is a biometric feature that allows
identification of a person when other biometric features such as the face or
iris are not visible. In this paper, we present a new pose-based convolutional
neural network model for gait recognition. Unlike many methods that consider
the full-height silhouette of a moving person, we consider the motion of points
in the areas around human joints. To extract motion information, we estimate
the optical flow between consecutive frames. We propose a deep convolutional
model that computes pose-based gait descriptors. We compare different network
architectures and aggregation methods and experimentally assess various sets of
body parts to determine which are the most important for gait recognition. In
addition, we investigate the generalization ability of the developed algorithms
by transferring them between datasets. The results of these experiments show
that our approach outperforms state-of-the-art methods.
GeniePath: Graph Neural Networks with Adaptive Receptive Paths
Authors: Ziqi Liu, Chaochao Chen, Longfei Li, Jun Zhou, Xiaolong Li, Le Song, Yuan Qi
Date: 2018-02-03 00:00:00

We present, GeniePath, a scalable approach for learning adaptive receptive
fields of neural networks defined on permutation invariant graph data. In
GeniePath, we propose an adaptive path layer consists of two complementary
functions designed for breadth and depth exploration respectively, where the
former learns the importance of different sized neighborhoods, while the latter
extracts and filters signals aggregated from neighbors of different hops away.
Our method works in both transductive and inductive settings, and extensive
experiments compared with competitive methods show that our approaches yield
state-of-the-art results on large graphs.