pwc-archive/papers-with-abstracts huggingface dataset:
Dating Ancient Paintings of Mogao Grottoes Using Deeply Learnt Visual Codes
Authors: Qingquan Li, Qin Zou, De Ma, Qian Wang, Song Wang
Date: 2018-10-22 00:00:00
Tasks: General Classification

Cultural heritage is the asset of all the peoples of the world. The
preservation and inheritance of cultural heritage is conducive to the progress
of human civilization. In northwestern China, there is a world heritage site --
Mogao Grottoes -- that has a plenty of mural paintings showing the historical
cultures of ancient China. To study these historical cultures, one critical
procedure is to date the mural paintings, i.e., determining the era when they
were created. Until now, most mural paintings at Mogao Grottoes have been dated
by directly referring to the mural texts or historical documents. However, some
are still left with creation-era undetermined due to the lack of reference
materials. Considering that the drawing style of mural paintings was changing
along the history and the drawing style can be learned and quantified through
painting data, we formulate the problem of mural-painting dating into a problem
of drawing-style classification. In fact, drawing styles can be expressed not
only in color or curvature, but also in some unknown forms -- the forms that
have not been observed. To this end, besides sophisticated color and shape
descriptors, a deep convolution neural network is designed to encode the
implicit drawing styles. 3860 mural paintings collected from 194 different
grottoes with determined creation-era labels are used to train the
classification model and build the dating method. In experiments, the proposed
dating method is applied to seven mural paintings which were previously dated
with controversies, and the exciting new dating results are approved by the
Dunhuang expert.
Optimal arrangements of hyperplanes for multiclass classification
Authors: Víctor Blanco, Alberto Japón, Justo Puerto
Date: 2018-10-22 00:00:00
Tasks: Classification, General Classification

In this paper, we present a novel approach to construct multiclass
classifiers by means of arrangements of hyperplanes. We propose different mixed
integer (linear and non linear) programming formulations for the problem using
extensions of widely used measures for misclassifying observations where the
\textit{kernel trick} can be adapted to be applicable. Some dimensionality
reductions and variable fixing strategies are also developed for these models.
An extensive battery of experiments has been run which reveal the powerfulness
of our proposal as compared with other previously proposed methodologies.
Ensemble Method for Censored Demand Prediction
Authors: Evgeniy M. Ozhegov, Daria Teterina
Date: 2018-10-22 00:00:00
Tasks: BIG-bench Machine Learning, Management, Prediction, quantile regression, regression

Many economic applications including optimal pricing and inventory management
requires prediction of demand based on sales data and estimation of sales
reaction to a price change. There is a wide range of econometric approaches
which are used to correct a bias in estimates of demand parameters on censored
sales data. These approaches can also be applied to various classes of machine
learning models to reduce the prediction error of sales volume. In this study
we construct two ensemble models for demand prediction with and without
accounting for demand censorship. Accounting for sales censorship is based on
the idea of censored quantile regression method where the model estimation is
splitted on two separate parts: a) prediction of zero sales by classification
model; and b) prediction of non-zero sales by regression model. Models with and
without accounting for censorship are based on the predictions aggregations of
Least squares, Ridge and Lasso regressions and Random Forest model. Having
estimated the predictive properties of both models, we empirically test the
best predictive power of the model that takes into account the censored nature
of demand. We also show that machine learning method with censorship accounting
provide bias corrected estimates of demand sensitivity for price change similar
to econometric models.
Named Entity Disambiguation using Deep Learning on Graphs
Authors: Alberto Cetoli, Mohammad Akbari, Stefano Bragaglia, Andrew D. O'Harney, Marc Sloan
Date: 2018-10-22 00:00:00
Tasks: Deep Learning, Entity Disambiguation

We tackle \ac{NED} by comparing entities in short sentences with \wikidata{}
graphs. Creating a context vector from graphs through deep learning is a
challenging problem that has never been applied to \ac{NED}. Our main
contribution is to present an experimental study of recent neural techniques,
as well as a discussion about which graph features are most important for the
disambiguation task. In addition, a new dataset (\wikidatadisamb{}) is created
to allow a clean and scalable evaluation of \ac{NED} with \wikidata{} entries,
and to be used as a reference in future research. In the end our results show
that a \ac{Bi-LSTM} encoding of the graph triplets performs best, improving
upon the baseline models and scoring an \rm{F1} value of $91.6\%$ on the
\wikidatadisamb{} test set
Exploring Correlations in Multiple Facial Attributes through Graph Attention Network
Authors: Yan Zhang, Li Sun
Date: 2018-10-22 00:00:00
Tasks: Attribute, Graph Attention, Multi-Task Learning

Estimating multiple attributes from a single facial image gives comprehensive
descriptions on the high level semantics of the face. It is naturally regarded
as a multi-task supervised learning problem with a single deep CNN, in which
lower layers are shared, and higher ones are task-dependent with the
multi-branch structure. Within the traditional deep multi-task learning (DMTL)
framework, this paper intends to fully exploit the correlations among different
attributes by constructing a graph. The node in graph represents the feature
vector from a particular branch for a given attribute, and the edge can be
defined by either the prior knowledge or the similarity between two nodes in
the embedding with a fully data-driven manner. We analyze that the attention
mechanism actually takes effect in the latter case, and utilize the Graph
Attention Layer (GAL) for exploring on the most relevant attribute feature and
refining the task-dependant feature by considering other attributes.
Experiments show that by mining the correlations among attributes, our method
can improve the recognition accuracy on CelebA and LFWA dataset. And it also
achieves competitive performance.
A Simple Baseline Algorithm for Graph Classification
Authors: Nathan de Lara, Edouard Pineau
Date: 2018-10-22 00:00:00
Tasks: BIG-bench Machine Learning, Classification, General Classification, Graph Classification, Graph Embedding

Graph classification has recently received a lot of attention from various
fields of machine learning e.g. kernel methods, sequential modeling or graph
embedding. All these approaches offer promising results with different
respective strengths and weaknesses. However, most of them rely on complex
mathematics and require heavy computational power to achieve their best
performance. We propose a simple and fast algorithm based on the spectral
decomposition of graph Laplacian to perform graph classification and get a
first reference score for a dataset. We show that this method obtains
competitive results compared to state-of-the-art algorithms.
A Dual-Attention Hierarchical Recurrent Neural Network for Dialogue Act Classification
Authors: Ruizhe Li, Chenghua Lin, Matthew Collinson, Xiao Li, Guanyi Chen
Date: 2018-10-22 00:00:00
Proceeding: CONLL 2019 11
Tasks: Classification, Dialogue Act Classification, Dialogue Generation, General Classification, Intent Detection

Recognising dialogue acts (DA) is important for many natural language processing tasks such as dialogue generation and intention recognition. In this paper, we propose a dual-attention hierarchical recurrent neural network for DA classification. Our model is partially inspired by the observation that conversational utterances are normally associated with both a DA and a topic, where the former captures the social act and the latter describes the subject matter. However, such a dependency between DAs and topics has not been utilised by most existing systems for DA classification. With a novel dual task-specific attention mechanism, our model is able, for utterances, to capture information about both DAs and topics, as well as information about the interactions between them. Experimental results show that by modelling topic as an auxiliary task, our model can significantly improve DA classification, yielding better or comparable performance to the state-of-the-art method on three public datasets.
Mean-based Heuristic Search for Real-Time Planning
Authors: Damien Pellier, Bruno Bouzy, Marc Métivier
Date: 2018-10-22 00:00:00
Tasks: Heuristic Search

In this paper, we introduce a new heuristic search algorithm based on mean
values for real-time planning, called MHSP. It consists in associating the
principles of UCT, a bandit-based algorithm which gave very good results in
computer games, and especially in Computer Go, with heuristic search in order
to obtain a real-time planner in the context of classical planning. MHSP is
evaluated on different planning problems and compared to existing algorithms
performing on-line search and learning. Besides, our results highlight the
capacity of MHSP to return plans in a real-time manner which tend to an optimal
plan over the time which is faster and of better quality compared to existing
algorithms in the literature.
Mining useful Macro-actions in Planning
Authors: Sandra Castellanos-Paez, Damien Pellier, Humbert Fiorino, Sylvie Pesty
Date: 2018-10-22 00:00:00

Planning has achieved significant progress in recent years. Among the various
approaches to scale up plan synthesis, the use of macro-actions has been widely
explored. As a first stage towards the development of a solution to learn
on-line macro-actions, we propose an algorithm to identify useful macro-actions
based on data mining techniques. The integration in the planning search of
these learned macro-actions shows significant improvements over six classical
planning benchmarks.
DNN-based Source Enhancement to Increase Objective Sound Quality Assessment Score
Authors: Yuma Koizumi, Kenta Niwa, Yusuke Hioka, Kazunori Kobayashi, Yoichi Haneda
Date: 2018-10-22 00:00:00

We propose a training method for deep neural network (DNN)-based source
enhancement to increase objective sound quality assessment (OSQA) scores such
as the perceptual evaluation of speech quality (PESQ). In many conventional
studies, DNNs have been used as a mapping function to estimate time-frequency
masks and trained to minimize an analytically tractable objective function such
as the mean squared error (MSE). Since OSQA scores have been used widely for
sound-quality evaluation, constructing DNNs to increase OSQA scores would be
better than using the minimum-MSE to create high-quality output signals.
However, since most OSQA scores are not analytically tractable, \textit{i.e.},
they are black boxes, the gradient of the objective function cannot be
calculated by simply applying back-propagation. To calculate the gradient of
the OSQA-based objective function, we formulated a DNN optimization scheme on
the basis of \textit{black-box optimization}, which is used for training a
computer that plays a game. For a black-box-optimization scheme, we adopt the
policy gradient method for calculating the gradient on the basis of a sampling
algorithm. To simulate output signals using the sampling algorithm, DNNs are
used to estimate the probability density function of the output signals that
maximize OSQA scores. The OSQA scores are calculated from the simulated output
signals, and the DNNs are trained to increase the probability of generating the
simulated output signals that achieve high OSQA scores. Through several
experiments, we found that OSQA scores significantly increased by applying the
proposed method, even though the MSE was not minimized.
Do Deep Generative Models Know What They Don't Know?
Authors: Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Gorur, Balaji Lakshminarayanan
Date: 2018-10-22 00:00:00
Proceeding: ICLR 2019 5

A neural network deployed in the wild may be asked to make predictions for
inputs that were drawn from a different distribution than that of the training
data. A plethora of work has demonstrated that it is easy to find or synthesize
inputs for which a neural network is highly confident yet wrong. Generative
models are widely viewed to be robust to such mistaken confidence as modeling
the density of the input features can be used to detect novel,
out-of-distribution inputs. In this paper we challenge this assumption. We find
that the density learned by flow-based models, VAEs, and PixelCNNs cannot
distinguish images of common objects such as dogs, trucks, and horses (i.e.
CIFAR-10) from those of house numbers (i.e. SVHN), assigning a higher
likelihood to the latter when the model is trained on the former. Moreover, we
find evidence of this phenomenon when pairing several popular image data sets:
FashionMNIST vs MNIST, CelebA vs SVHN, ImageNet vs CIFAR-10 / CIFAR-100 / SVHN.
To investigate this curious behavior, we focus analysis on flow-based
generative models in particular since they are trained and evaluated via the
exact marginal likelihood. We find such behavior persists even when we restrict
the flows to constant-volume transformations. These transformations admit some
theoretical analysis, and we show that the difference in likelihoods can be
explained by the location and variances of the data and the model curvature.
Our results caution against using the density estimates from deep generative
models to identify inputs similar to the training distribution until their
behavior for out-of-distribution inputs is better understood.
Unsupervised Detection of Anomalous Sound based on Deep Learning and the Neyman-Pearson Lemma
Authors: Yuma Koizumi, Shoichiro Saito, Hisashi Uematsum Yuta Kawachi, Noboru Harada
Date: 2018-10-22 00:00:00
Tasks: Anomaly Detection, LEMMA, Unsupervised Anomaly Detection, Unsupervised Anomaly Detection In Sound

This paper proposes a novel optimization principle and its implementation for
unsupervised anomaly detection in sound (ADS) using an autoencoder (AE). The
goal of unsupervised-ADS is to detect unknown anomalous sound without training
data of anomalous sound. Use of an AE as a normal model is a state-of-the-art
technique for unsupervised-ADS. To decrease the false positive rate (FPR), the
AE is trained to minimize the reconstruction error of normal sounds and the
anomaly score is calculated as the reconstruction error of the observed sound.
Unfortunately, since this training procedure does not take into account the
anomaly score for anomalous sounds, the true positive rate (TPR) does not
necessarily increase. In this study, we define an objective function based on
the Neyman-Pearson lemma by considering ADS as a statistical hypothesis test.
The proposed objective function trains the AE to maximize the TPR under an
arbitrary low FPR condition. To calculate the TPR in the objective function, we
consider that the set of anomalous sounds is the complementary set of normal
sounds and simulate anomalous sounds by using a rejection sampling algorithm.
Through experiments using synthetic data, we found that the proposed method
improved the performance measures of ADS under low FPR conditions. In addition,
we confirmed that the proposed method could detect anomalous sounds in real
environments.
Risk-Sensitive Reinforcement Learning via Policy Gradient Search
Authors: Prashanth L. A., Michael Fu
Date: 2018-10-22 00:00:00
Tasks: Policy Gradient Methods, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The objective in a traditional reinforcement learning (RL) problem is to find a policy that optimizes the expected value of a performance metric such as the infinite-horizon cumulative discounted or long-run average cost/reward. In practice, optimizing the expected value alone may not be satisfactory, in that it may be desirable to incorporate the notion of risk into the optimization problem formulation, either in the objective or as a constraint. Various risk measures have been proposed in the literature, e.g., exponential utility, variance, percentile performance, chance constraints, value at risk (quantile), conditional value-at-risk, prospect theory and its later enhancement, cumulative prospect theory. In this book, we consider risk-sensitive RL in two settings: one where the goal is to find a policy that optimizes the usual expected value objective while ensuring that a risk constraint is satisfied, and the other where the risk measure is the objective. We survey some of the recent work in this area specifically where policy gradient search is the solution approach. In the first risk-sensitive RL setting, we cover popular risk measures based on variance, conditional value-at-risk, and chance constraints, and present a template for policy gradient-based risk-sensitive RL algorithms using a Lagrangian formulation. For the setting where risk is incorporated directly into the objective function, we consider an exponential utility formulation, cumulative prospect theory, and coherent risk measures. This non-exhaustive survey aims to give a flavor of the challenges involved in solving risk-sensitive RL problems using policy gradient methods, as well as outlining some potential future research directions.
Atrial scars segmentation via potential learning in the graph-cuts framework
Authors: Lei Li, Fuping Wu, Guang Yang, Tom Wong, Raad Mohiaddin, David Firmin, Jenny Keegan, Lingchao Xu, Xiahai Zhuang
Date: 2018-10-22 00:00:00
Tasks: Prognosis

Late Gadolinium Enhancement Magnetic Resonance Imaging (LGE MRI) emerged as a
routine scan for patients with atrial fibrillation (AF). However, due to the
low image quality automating the quantification and analysis of the atrial
scars is challenging. In this study, we pro-posed a fully automated method
based on the graph-cuts framework, where the potential of the graph is learned
on a surface mesh of the left atrium (LA) using an equidistant projection and a
Deep Neural Network (DNN). For validation, we employed 100 datasets with manual
delineation. The results showed that the performance of the proposed method
improved and converged with respect to the increased size of training patches,
which provide important features of the structural and texture information
learned by the DNN. The segmentation could be further improved when the
contribution from the t-link and n-link is balanced, thanks to
inter-relationship learned by the DNN for the graph-cuts algorithm. Compared
with the published methods which mostly acquired manual delineation of the LA
or LA wall, our method is fully automatic and demonstrated evidently better
results with statistical significance. Finally, the accuracy of quantifying the
scars assessed by the Dice score was 0.570. The results are promising and the
method can be useful in diagnosis and prognosis of AF.
The Bregman chord divergence
Authors: Frank Nielsen, Richard Nock
Date: 2018-10-22 00:00:00

Distances are fundamental primitives whose choice significantly impacts the
performances of algorithms in machine learning and signal processing. However
selecting the most appropriate distance for a given task is an endeavor.
Instead of testing one by one the entries of an ever-expanding dictionary of
{\em ad hoc} distances, one rather prefers to consider parametric classes of
distances that are exhaustively characterized by axioms derived from first
principles. Bregman divergences are such a class. However fine-tuning a Bregman
divergence is delicate since it requires to smoothly adjust a functional
generator. In this work, we propose an extension of Bregman divergences called
the Bregman chord divergences. This new class of distances does not require
gradient calculations, uses two scalar parameters that can be easily tailored
in applications, and generalizes asymptotically Bregman divergences.
An Automatic Method for Complete Brain Matter Segmentation from Multislice CT scan
Authors: Soumi Ray, Vinod Kumar, Chirag Ahuja, Niranjan Khandelwal
Date: 2018-09-11 00:00:00
Tasks: Segmentation

Computed tomography imaging is well accepted for its imaging speed, image
contrast & resolution and cost. Thus it has wide use in detection and diagnosis
of brain diseases. But unfortunately reported works on CT segmentation is not
very significant. In this paper, a robust automatic segmentation system is
presented which is capable of segment complete brain matter from CT slices,
without any lose in information. The proposed method is simple, fast, accurate
and completely automatic. It can handle multislice CT scan in single run. From
a given multislice CT dataset, one slice is selected automatically to form
masks for segmentation. Two types of masks are created to handle nasal slices
in a better way. Masks are created from selected reference slice using
automatic seed point selection and region growing technique. One mask is
designed for brain matter and another includes the skull of the reference
slice. This second mask is used as global reference mask for all slices whereas
the brain matter mask is implemented on only adjacent slices and continuously
modified for better segmentation. Slices in given dataset are divided into two
batches, before reference slice and after reference slice. Each batch segmented
separately. Successive propagation of brain matter mask has demonstrated very
high potential in reported segmentation. Presented result shows highest
sensitivity and more than 96% accuracy in all cases. Resulted segmented images
can be used for any brain disease diagnosis or further image analysis.
Learning to Measure Change: Fully Convolutional Siamese Metric Networks for Scene Change Detection
Authors: Enqiang Guo, Xinsha Fu, Jiawei Zhu, Min Deng, Yu Liu, Qing Zhu, Haifeng Li
Date: 2018-10-22 00:00:00
Tasks: Change Detection, Scene Change Detection

A critical challenge problem of scene change detection is that noisy changes
generated by varying illumination, shadows and camera viewpoint make variances
of a scene difficult to define and measure since the noisy changes and semantic
ones are entangled. Following the intuitive idea of detecting changes by
directly comparing dissimilarities between a pair of features, we propose a
novel fully Convolutional siamese metric Network(CosimNet) to measure changes
by customizing implicit metrics. To learn more discriminative metrics, we
utilize contrastive loss to reduce the distance between the unchanged feature
pairs and to enlarge the distance between the changed feature pairs.
Specifically, to address the issue of large viewpoint differences, we propose
Thresholded Contrastive Loss (TCL) with a more tolerant strategy to punish
noisy changes. We demonstrate the effectiveness of the proposed approach with
experiments on three challenging datasets: CDnet, PCD2015, and VL-CMU-CD. Our
approach is robust to lots of challenging conditions, such as illumination
changes, large viewpoint difference caused by camera motion and zooming. In
addition, we incorporate the distance metric into the segmentation framework
and validate the effectiveness through visualization of change maps and feature
distribution. The source code is available at
https://github.com/gmayday1997/ChangeDet.