pwc-archive/papers-with-abstracts huggingface dataset:
Category Trees
Authors: Kieran Greer
Date: 2018-11-06 00:00:00

This paper presents a batch classifier that has been improved from the earlier version and fixed a mistake in the earlier paper. Two important changes have been made. Each category is represented by a classifier, where each classifier classifies its own subset of data rows, using batch input values to represent the centroid. The first change is to use the category centroid as the desired category output. When the classifier represents more than one category, it creates a new layer and splits, to represent each category separately in the new layer. The second change therefore, is to allow the classifier to branch to new levels when there is a split in the data, or when some data rows are incorrectly classified. Each layer can therefore branch like a tree - not for distinguishing features, but for distinguishing categories. The paper then suggests further innovations, by adding fixed value ranges through bands, for each column or feature of the input dataset. When considering features, it is shown that some of the data can be classified directly through fixed value ranges, while the rest can be classified using the classifier technique. Tests show that the method can successfully classify a diverse set of benchmark datasets to better than the state-of-the-art. The paper also discusses a biological analogy with neurons and neuron links.
Multi-View Network Embedding Via Graph Factorization Clustering and Co-Regularized Multi-View Agreement
Authors: Yiwei Sun, Ngot Bui, Tsung-Yu Hsieh, Vasant Honavar
Date: 2018-11-06 00:00:00
Tasks: Clustering, Network Embedding

Real-world social networks and digital platforms are comprised of individuals
(nodes) that are linked to other individuals or entities through multiple types
of relationships (links). Sub-networks of such a network based on each type of
link correspond to distinct views of the underlying network. In real-world
applications, each node is typically linked to only a small subset of other
nodes. Hence, practical approaches to problems such as node labeling have to
cope with the resulting sparse networks. While low-dimensional network
embeddings offer a promising approach to this problem, most of the current
network embedding methods focus primarily on single view networks. We introduce
a novel multi-view network embedding (MVNE) algorithm for constructing
low-dimensional node embeddings from multi-view networks. MVNE adapts and
extends an approach to single view network embedding (SVNE) using graph
factorization clustering (GFC) to the multi-view setting using an objective
function that maximizes the agreement between views based on both the local and
global structure of the underlying multi-view graph. Our experiments with
several benchmark real-world single view networks show that GFC-based SVNE
yields network embeddings that are competitive with or superior to those
produced by the state-of-the-art single view network embedding methods when the
embeddings are used for labeling unlabeled nodes in the networks. Our
experiments with several multi-view networks show that MVNE substantially
outperforms the single view methods on integrated view and the state-of-the-art
multi-view methods. We further show that even when the goal is to predict
labels of nodes within a single target view, MVNE outperforms its single-view
counterpart suggesting that the MVNE is able to extract the information that is
useful for labeling nodes in the target view from the all of the views.
Evaluating the Ability of LSTMs to Learn Context-Free Grammars
Authors: Luzi Sennhauser, Robert C. Berwick
Date: 2018-11-06 00:00:00
Proceeding: WS 2018 11

While long short-term memory (LSTM) neural net architectures are designed to
capture sequence information, human language is generally composed of
hierarchical structures. This raises the question as to whether LSTMs can learn
hierarchical structures. We explore this question with a well-formed bracket
prediction task using two types of brackets modeled by an LSTM. Demonstrating
that such a system is learnable by an LSTM is the first step in demonstrating
that the entire class of CFLs is also learnable. We observe that the model
requires exponential memory in terms of the number of characters and embedded
depth, where a sub-linear memory should suffice. Still, the model does more
than memorize the training input. It learns how to distinguish between relevant
and irrelevant information. On the other hand, we also observe that the model
does not generalize well. We conclude that LSTMs do not learn the relevant
underlying context-free rules, suggesting the good overall performance is
attained rather by an efficient way of evaluating nuisance variables. LSTMs are
a way to quickly reach good results for many natural language tasks, but to
understand and generate natural language one has to investigate other concepts
that can make more direct use of natural language's structural nature.
Embedded polarizing filters to separate diffuse and specular reflection
Authors: Laurent Valentin Jospin, Gilles Baechler, Adam Scholefield
Date: 2018-11-06 00:00:00

Polarizing filters provide a powerful way to separate diffuse and specular
reflection; however, traditional methods rely on several captures and require
proper alignment of the filters. Recently, camera manufacturers have proposed
to embed polarizing micro-filters in front of the sensor, creating a mosaic of
pixels with different polarizations. In this paper, we investigate the
advantages of such camera designs. In particular, we consider different design
patterns for the filter arrays and propose an algorithm to demosaic an image
generated by such cameras. This essentially allows us to separate the diffuse
and specular components using a single image. The performance of our algorithm
is compared with a color-based method using synthetic and real data. Finally,
we demonstrate how we can recover the normals of a scene using the diffuse
images estimated by our method.
Fast Neural Chinese Word Segmentation for Long Sentences
Authors: Sufeng Duan, Jiangtong Li, Hai Zhao
Date: 2018-11-06 00:00:00
Tasks: Chinese Word Segmentation, Segmentation, TAG

Rapidly developed neural models have achieved competitive performance in
Chinese word segmentation (CWS) as their traditional counterparts. However,
most of methods encounter the computational inefficiency especially for long
sentences because of the increasing model complexity and slower decoders. This
paper presents a simple neural segmenter which directly labels the gap
existence between adjacent characters to alleviate the existing drawback. Our
segmenter is fully end-to-end and capable of performing segmentation very fast.
We also show a performance difference with different tag sets. The experiments
show that our segmenter can provide comparable performance with
state-of-the-art.
Training Generative Adversarial Networks with Weights
Authors: Yannis Pantazis, Dipjyoti Paul, Michail Fasoulakis, Yannis Stylianou
Date: 2018-11-06 00:00:00

The impressive success of Generative Adversarial Networks (GANs) is often
overshadowed by the difficulties in their training. Despite the continuous
efforts and improvements, there are still open issues regarding their
convergence properties. In this paper, we propose a simple training variation
where suitable weights are defined and assist the training of the Generator. We
provide theoretical arguments why the proposed algorithm is better than the
baseline training in the sense of speeding up the training process and of
creating a stronger Generator. Performance results showed that the new
algorithm is more accurate in both synthetic and image datasets resulting in
improvements ranging between 5% and 50%.
Online Off-policy Prediction
Authors: Sina Ghiassian, Andrew Patterson, Martha White, Richard S. Sutton, Adam White
Date: 2018-11-06 00:00:00
Tasks: Prediction

This paper investigates the problem of online prediction learning, where
learning proceeds continuously as the agent interacts with an environment. The
predictions made by the agent are contingent on a particular way of behaving,
represented as a value function. However, the behavior used to select actions
and generate the behavior data might be different from the one used to define
the predictions, and thus the samples are generated off-policy. The ability to
learn behavior-contingent predictions online and off-policy has long been
advocated as a key capability of predictive-knowledge learning systems but
remained an open algorithmic challenge for decades. The issue lies with the
temporal difference (TD) learning update at the heart of most prediction
algorithms: combining bootstrapping, off-policy sampling and function
approximation may cause the value estimate to diverge. A breakthrough came with
the development of a new objective function that admitted stochastic gradient
descent variants of TD. Since then, many sound online off-policy prediction
algorithms have been developed, but there has been limited empirical work
investigating the relative merits of all the variants. This paper aims to fill
these empirical gaps and provide clarity on the key ideas behind each method.
We summarize the large body of literature on off-policy learning, focusing on
1- methods that use computation linear in the number of features and are
convergent under off-policy sampling, and 2- other methods which have proven
useful with non-fixed, nonlinear function approximation. We provide an
empirical study of off-policy prediction methods in two challenging
microworlds. We report each method's parameter sensitivity, empirical
convergence rate, and final performance, providing new insights that should
enable practitioners to successfully extend these new methods to large-scale
applications.[Abridged abstract]
Deep Weighted Averaging Classifiers
Authors: Dallas Card, Michael Zhang, Noah A. Smith
Date: 2018-11-06 00:00:00
Tasks: General Classification

Recent advances in deep learning have achieved impressive gains in
classification accuracy on a variety of types of data, including images and
text. Despite these gains, however, concerns have been raised about the
calibration, robustness, and interpretability of these models. In this paper we
propose a simple way to modify any conventional deep architecture to
automatically provide more transparent explanations for classification
decisions, as well as an intuitive notion of the credibility of each
prediction. Specifically, we draw on ideas from nonparametric kernel
regression, and propose to predict labels based on a weighted sum of training
instances, where the weights are determined by distance in a learned
instance-embedding space. Working within the framework of conformal methods, we
propose a new measure of nonconformity suggested by our model, and
experimentally validate the accompanying theoretical expectations,
demonstrating improved transparency, controlled error rates, and robustness to
out-of-domain data, without compromising on accuracy or calibration.
Point2Sequence: Learning the Shape Representation of 3D Point Clouds with an Attention-based Sequence to Sequence Network
Authors: Xinhai Liu, Zhizhong Han, Yu-Shen Liu, Matthias Zwicker
Date: 2018-11-06 00:00:00
Tasks: 3D Part Segmentation, 3D Point Cloud Classification, Decoder, Shape Representation Of 3D Point Clouds

Exploring contextual information in the local region is important for shape
understanding and analysis. Existing studies often employ hand-crafted or
explicit ways to encode contextual information of local regions. However, it is
hard to capture fine-grained contextual information in hand-crafted or explicit
manners, such as the correlation between different areas in a local region,
which limits the discriminative ability of learned features. To resolve this
issue, we propose a novel deep learning model for 3D point clouds, named
Point2Sequence, to learn 3D shape features by capturing fine-grained contextual
information in a novel implicit way. Point2Sequence employs a novel sequence
learning model for point clouds to capture the correlations by aggregating
multi-scale areas of each local region with attention. Specifically,
Point2Sequence first learns the feature of each area scale in a local region.
Then, it captures the correlation between area scales in the process of
aggregating all area scales using a recurrent neural network (RNN) based
encoder-decoder structure, where an attention mechanism is proposed to
highlight the importance of different area scales. Experimental results show
that Point2Sequence achieves state-of-the-art performance in shape
classification and segmentation tasks.
An amplitudes-perturbation data augmentation method in convolutional neural networks for EEG decoding
Authors: Xian-Rui Zhang, Meng-Ying Lei, Yang Li
Date: 2018-11-06 00:00:00
Tasks: Brain Computer Interface, Data Augmentation, Deep Learning, EEG, Eeg Decoding, Electroencephalogram (EEG)

Brain-Computer Interface (BCI) system provides a pathway between humans and
the outside world by analyzing brain signals which contain potential neural
information. Electroencephalography (EEG) is one of most commonly used brain
signals and EEG recognition is an important part of BCI system. Recently,
convolutional neural networks (ConvNet) in deep learning are becoming the new
cutting edge tools to tackle the problem of EEG recognition. However, training
an effective deep learning model requires a big number of data, which limits
the application of EEG datasets with a small number of samples. In order to
solve the issue of data insufficiency in deep learning for EEG decoding, we
propose a novel data augmentation method that add perturbations to amplitudes
of EEG signals after transform them to frequency domain. In experiments, we
explore the performance of signal recognition with the state-of-the-art models
before and after data augmentation on BCI Competition IV dataset 2a and our
local dataset. The results show that our data augmentation technique can
improve the accuracy of EEG recognition effectively.
An exploration of algorithmic discrimination in data and classification
Authors: Jixue Liu, Jiuyong Li, Feiyue Ye, Lin Liu, Thuc Duy Le, Ping Xiong
Date: 2018-11-06 00:00:00
Tasks: Classification, General Classification

Algorithmic discrimination is an important aspect when data is used for
predictive purposes. This paper analyzes the relationships between
discrimination and classification, data set partitioning, and decision models,
as well as correlation. The paper uses real world data sets to demonstrate the
existence of discrimination and the independence between the discrimination of
data sets and the discrimination of classification models.
A Parallel MOEA with Criterion-based Selection Applied to the Knapsack Problem
Authors: Kantour Nedjmeddine, Bouroubi Sadek, Chaabane Djamel
Date: 2018-11-06 00:00:00
Tasks: Evolutionary Algorithms

In this paper, we propose a parallel multiobjective evolutionary algorithm
called Parallel Criterion-based Partitioning MOEA (PCPMOEA), with an
application to the Mutliobjective Knapsack Problem (MOKP). The suggested search
strategy is based on a periodic partitioning of potentially efficient
solutions, which are distributed to multiple multiobjective evolutionary
algorithms (MOEAs). Each MOEA is dedicated to a sole objective, in which it
combines both criterion-based and dominance-based approaches. The suggested
algorithm addresses two main sub-objectives: minimizing the distance between
the current non-dominated solutions and the ideal point, and ensuring the
spread of the potentially efficient solutions. Experimental results are
included, where we assess the performance of the suggested algorithm against
the above mentioned sub-objectives, compared with state-of-the-art results
using well-known multi-objective metaheuristics.
Un-normalized hypergraph p-Laplacian based semi-supervised learning methods
Authors: Loc Hoang Tran, Linh Hoang Tran
Date: 2018-11-06 00:00:00

Most network-based machine learning methods assume that the labels of two
adjacent samples in the network are likely to be the same. However, assuming
the pairwise relationship between samples is not complete. The information a
group of samples that shows very similar pattern and tends to have similar
labels is missed. The natural way overcoming the information loss of the above
assumption is to represent the feature dataset of samples as the hypergraph.
Thus, in this paper, we will present the un-normalized hypergraph p-Laplacian
semi-supervised learning methods. These methods will be applied to the zoo
dataset and the tiny version of 20 newsgroups dataset. Experiment results show
that the accuracy performance measures of these un-normalized hypergraph
p-Laplacian based semi-supervised learning methods are significantly greater
than the accuracy performance measure of the un-normalized hypergraph Laplacian
based semi-supervised learning method (the current state of the art method
hypergraph Laplacian based semi-supervised learning method for classification
problem with p=2).
On exponential convergence of SGD in non-convex over-parametrized learning
Authors: Raef Bassily, Mikhail Belkin, Siyuan Ma
Date: 2018-11-06 00:00:00
Tasks: BIG-bench Machine Learning

Large over-parametrized models learned via stochastic gradient descent (SGD)
methods have become a key element in modern machine learning. Although SGD
methods are very effective in practice, most theoretical analyses of SGD
suggest slower convergence than what is empirically observed. In our recent
work [8] we analyzed how interpolation, common in modern over-parametrized
learning, results in exponential convergence of SGD with constant step size for
convex loss functions. In this note, we extend those results to a much broader
non-convex function class satisfying the Polyak-Lojasiewicz (PL) condition. A
number of important non-convex problems in machine learning, including some
classes of neural networks, have been recently shown to satisfy the PL
condition. We argue that the PL condition provides a relevant and attractive
setting for many machine learning problems, particularly in the
over-parametrized regime.
Towards a Near Universal Time Series Data Mining Tool: Introducing the Matrix Profile
Authors: Chin-Chia Michael Yeh
Date: 2018-11-05 00:00:00
Tasks: Clustering, Representation Learning, Semantic Segmentation, Time Series, Time Series Analysis, Time Series Classification

The last decade has seen a flurry of research on all-pairs-similarity-search (or, self-join) for text, DNA, and a handful of other datatypes, and these systems have been applied to many diverse data mining problems. Surprisingly, however, little progress has been made on addressing this problem for time series subsequences. In this thesis, we have introduced a near universal time series data mining tool called matrix profile which solves the all-pairs-similarity-search problem and caches the output in an easy-to-access fashion. The proposed algorithm is not only parameter-free, exact and scalable, but also applicable for both single and multidimensional time series. By building time series data mining methods on top of matrix profile, many time series data mining tasks (e.g., motif discovery, discord discovery, shapelet discovery, semantic segmentation, and clustering) can be efficiently solved. Because the same matrix profile can be shared by a diverse set of time series data mining methods, matrix profile is versatile and computed-once-use-many-times data structure. We demonstrate the utility of matrix profile for many time series data mining problems, including motif discovery, discord discovery, weakly labeled time series classification, and representation learning on domains as diverse as seismology, entomology, music processing, bioinformatics, human activity monitoring, electrical power-demand monitoring, and medicine. We hope the matrix profile is not the end but the beginning of many more time series data mining projects.
Toward Efficient Breast Cancer Diagnosis and Survival Prediction Using L-Perceptron
Authors: Hadi Mansourifar, Weidong Shi
Date: 2018-11-05 00:00:00
Tasks: All, feature selection, Prognosis, Survival Prediction

Breast cancer is the most frequently reported cancer type among the women
around the globe and beyond that it has the second highest female fatality rate
among all cancer types. Despite all the progresses made in prevention and early
intervention, early prognosis and survival prediction rates are still
unsatisfactory. In this paper, we propose a novel type of perceptron called
L-Perceptron which outperforms all the previous supervised learning methods by
reaching 97.42 \% and 98.73 \% in terms of accuracy and sensitivity,
respectively in Wisconsin Breast Cancer dataset. Experimental results on
Haberman's Breast Cancer Survival dataset, show the superiority of proposed
method by reaching 75.18 \% and 83.86 \% in terms of accuracy and F1 score,
respectively. The results are the best reported ones obtained in 10-fold cross
validation in absence of any preprocessing or feature selection.
Learning Bone Suppression from Dual Energy Chest X-rays using Adversarial Networks
Authors: Dong Yul Oh, Il Dong Yun
Date: 2018-11-05 00:00:00
Tasks: Bone Suppression From Dual Energy Chest X-Rays, Diagnostic, Generative Adversarial Network

Suppressing bones on chest X-rays such as ribs and clavicle is often expected
to improve pathologies classification. These bones can interfere with a broad
range of diagnostic tasks on pulmonary disease except for musculoskeletal
system. Current conventional method for acquisition of bone suppressed X-rays
is dual energy imaging, which captures two radiographs at a very short interval
with different energy levels; however, the patient is exposed to radiation
twice and the artifacts arise due to heartbeats between two shots. In this
paper, we introduce a deep generative model trained to predict bone suppressed
images on single energy chest X-rays, analyzing a finite set of previously
acquired dual energy chest X-rays. Since the relatively small amount of data is
available, such approach relies on the methodology maximizing the data
utilization. Here we integrate the following two approaches. First, we use a
conditional generative adversarial network that complements the traditional
regression method minimizing the pairwise image difference. Second, we use Haar
2D wavelet decomposition to offer a perceptual guideline in frequency details
to allow the model to converge quickly and efficiently. As a result, we achieve
state-of-the-art performance on bone suppression as compared to the existing
approaches with dual energy chest X-rays.