pwc-archive/papers-with-abstracts huggingface dataset:
Multiobjective Optimization of Solar Powered Irrigation System with Fuzzy Type-2 Noise Modelling
Authors: T. Ganesan, P. Vasant, I. Elamvazuthi
Date: 2017-01-17 00:00:00
Tasks: Multiobjective Optimization

Optimization is becoming a crucial element in industrial applications
involving sustainable alternative energy systems. During the design of such
systems, the engineer/decision maker would often encounter noise factors (e.g.
solar insolation and ambient temperature fluctuations) when their system
interacts with the environment. In this chapter, the sizing and design
optimization of the solar powered irrigation system was considered. This
problem is multivariate, noisy, nonlinear and multiobjective. This design
problem was tackled by first using the Fuzzy Type II approach to model the
noise factors. Consequently, the Bacterial Foraging Algorithm (BFA) (in the
context of a weighted sum framework) was employed to solve this multiobjective
fuzzy design problem. This method was then used to construct the approximate
Pareto frontier as well as to identify the best solution option in a fuzzy
setting. Comprehensive analyses and discussions were performed on the generated
numerical results with respect to the implemented solution methods.
Image Generation and Editing with Variational Info Generative AdversarialNetworks
Authors: Mahesh Gorijala, Ambedkar Dukkipati
Date: 2017-01-17 00:00:00
Tasks: Image Generation

Recently there has been an enormous interest in generative models for images
in deep learning. In pursuit of this, Generative Adversarial Networks (GAN) and
Variational Auto-Encoder (VAE) have surfaced as two most prominent and popular
models. While VAEs tend to produce excellent reconstructions but blurry
samples, GANs generate sharp but slightly distorted images. In this paper we
propose a new model called Variational InfoGAN (ViGAN). Our aim is two fold:
(i) To generated new images conditioned on visual descriptions, and (ii) modify
the image, by fixing the latent representation of image and varying the visual
description. We evaluate our model on Labeled Faces in the Wild (LFW), celebA
and a modified version of MNIST datasets and demonstrate the ability of our
model to generate new images as well as to modify a given image by changing
attributes.
Improving Deep Neural Network with Multiple Parametric Exponential Linear Units
Authors: Yang Li, Chunxiao Fan, Yong Li, Qiong Wu, Yue Ming
Date: 2016-06-01 00:00:00

Activation function is crucial to the recent successes of deep neural
networks. In this paper, we first propose a new activation function, Multiple
Parametric Exponential Linear Units (MPELU), aiming to generalize and unify the
rectified and exponential linear units. As the generalized form, MPELU shares
the advantages of Parametric Rectified Linear Unit (PReLU) and Exponential
Linear Unit (ELU), leading to better classification performance and convergence
property. In addition, weight initialization is very important to train very
deep networks. The existing methods laid a solid foundation for networks using
rectified linear units but not for exponential linear units. This paper
complements the current theory and extends it to the wider range. Specifically,
we put forward a way of initialization, enabling training of very deep networks
using exponential linear units. Experiments demonstrate that the proposed
initialization not only helps the training process but leads to better
generalization performance. Finally, utilizing the proposed activation function
and initialization, we present a deep MPELU residual architecture that achieves
state-of-the-art performance on the CIFAR-10/100 datasets. The code is
available at https://github.com/Coldmooon/Code-for-MPELU.
Fusing Deep Learned and Hand-Crafted Features of Appearance, Shape, and Dynamics for Automatic Pain Estimation
Authors: Joy Egede, Michel Valstar, Brais Martinez
Date: 2017-01-17 00:00:00
Tasks: Deep Learning, Time Series, Time Series Analysis

Automatic continuous time, continuous value assessment of a patient's pain
from face video is highly sought after by the medical profession. Despite the
recent advances in deep learning that attain impressive results in many
domains, pain estimation risks not being able to benefit from this due to the
difficulty in obtaining data sets of considerable size. In this work we propose
a combination of hand-crafted and deep-learned features that makes the most of
deep learning techniques in small sample settings. Encoding shape, appearance,
and dynamics, our method significantly outperforms the current state of the
art, attaining a RMSE error of less than 1 point on a 16-level pain scale,
whilst simultaneously scoring a 67.3% Pearson correlation coefficient between
our predicted pain level time series and the ground truth.
Multi-view Regularized Gaussian Processes
Authors: Qiuyang Liu, Shiliang Sun
Date: 2017-01-17 00:00:00
Tasks: Gaussian Processes, MULTI-VIEW LEARNING

Gaussian processes (GPs) have been proven to be powerful tools in various
areas of machine learning. However, there are very few applications of GPs in
the scenario of multi-view learning. In this paper, we present a new GP model
for multi-view learning. Unlike existing methods, it combines multiple views by
regularizing marginal likelihood with the consistency among the posterior
distributions of latent functions from different views. Moreover, we give a
general point selection scheme for multi-view learning and improve the proposed
model by this criterion. Experimental results on multiple real world data sets
have verified the effectiveness of the proposed model and witnessed the
performance improvement through employing this novel point selection scheme.
From Community Detection to Community Profiling
Authors: Hongyun Cai, Vincent W. Zheng, Fanwei Zhu, Kevin Chen-Chuan Chang, Zi Huang
Date: 2017-01-17 00:00:00
Tasks: Community Detection

Most existing community-related studies focus on detection, which aim to find
the community membership for each user from user friendship links. However,
membership alone, without a complete profile of what a community is and how it
interacts with other communities, has limited applications. This motivates us
to consider systematically profiling the communities and thereby developing
useful community-level applications. In this paper, we for the first time
formalize the concept of community profiling. With rich user information on the
network, such as user published content and user diffusion links, we
characterize a community in terms of both its internal content profile and
external diffusion profile. The difficulty of community profiling is often
underestimated. We novelly identify three unique challenges and propose a joint
Community Profiling and Detection (CPD) model to address them accordingly. We
also contribute a scalable inference algorithm, which scales linearly with the
data size and it is easily parallelizable. We evaluate CPD on large-scale
real-world data sets, and show that it is significantly better than the
state-of-the-art baselines in various tasks.
Systematic study of color spaces and components for the segmentation of sky/cloud images
Authors: Soumyabrata Dev, Yee Hui Lee, Stefan Winkler
Date: 2017-01-17 00:00:00
Tasks: Clustering, Segmentation

Sky/cloud imaging using ground-based Whole Sky Imagers (WSI) is a
cost-effective means to understanding cloud cover and weather patterns. The
accurate segmentation of clouds in these images is a challenging task, as
clouds do not possess any clear structure. Several algorithms using different
color models have been proposed in the literature. This paper presents a
systematic approach for the selection of color spaces and components for
optimal segmentation of sky/cloud images. Using mainly principal component
analysis (PCA) and fuzzy clustering for evaluation, we identify the most
suitable color components for this task.
Towards prediction of rapid intensification in tropical cyclones with recurrent neural networks
Authors: Rohitash Chandra
Date: 2017-01-17 00:00:00
Tasks: Time Series, Time Series Analysis, Weather Forecasting

The problem where a tropical cyclone intensifies dramatically within a short
period of time is known as rapid intensification. This has been one of the
major challenges for tropical weather forecasting. Recurrent neural networks
have been promising for time series problems which makes them appropriate for
rapid intensification. In this paper, recurrent neural networks are used to
predict rapid intensification cases of tropical cyclones from the South Pacific
and South Indian Ocean regions. A class imbalanced problem is encountered which
makes it very challenging to achieve promising performance. A simple strategy
was proposed to include more positive cases for detection where the false
positive rate was slightly improved. The limitations of building an efficient
system remains due to the challenges of addressing the class imbalance problem
encountered for rapid intensification prediction. This motivates further
research in using innovative machine learning methods.
On The Construction of Extreme Learning Machine for Online and Offline One-Class Classification - An Expanded Toolbox
Authors: Chandan Gautam, Aruna Tiwari, Qian Leng
Date: 2017-01-17 00:00:00
Tasks: General Classification, One-Class Classification

One-Class Classification (OCC) has been prime concern for researchers and
effectively employed in various disciplines. But, traditional methods based
one-class classifiers are very time consuming due to its iterative process and
various parameters tuning. In this paper, we present six OCC methods based on
extreme learning machine (ELM) and Online Sequential ELM (OSELM). Our proposed
classifiers mainly lie in two categories: reconstruction based and boundary
based, which supports both types of learning viz., online and offline learning.
Out of various proposed methods, four are offline and remaining two are online
methods. Out of four offline methods, two methods perform random feature
mapping and two methods perform kernel feature mapping. Kernel feature mapping
based approaches have been tested with RBF kernel and online version of
one-class classifiers are tested with both types of nodes viz., additive and
RBF. It is well known fact that threshold decision is a crucial factor in case
of OCC, so, three different threshold deciding criteria have been employed so
far and analyses the effectiveness of one threshold deciding criteria over
another. Further, these methods are tested on two artificial datasets to check
there boundary construction capability and on eight benchmark datasets from
different discipline to evaluate the performance of the classifiers. Our
proposed classifiers exhibit better performance compared to ten traditional
one-class classifiers and ELM based two one-class classifiers. Through proposed
one-class classifiers, we intend to expand the functionality of the most used
toolbox for OCC i.e. DD toolbox. All of our methods are totally compatible with
all the present features of the toolbox.
Deep Learning for Computational Chemistry
Authors: Garrett B. Goh, Nathan O. Hodas, Abhinav Vishnu
Date: 2017-01-17 00:00:00
Tasks: BIG-bench Machine Learning, Computational chemistry, Deep Learning, GPU, Property Prediction, Protein Structure Prediction, speech-recognition, Speech Recognition

The rise and fall of artificial neural networks is well documented in the
scientific literature of both computer science and computational chemistry. Yet
almost two decades later, we are now seeing a resurgence of interest in deep
learning, a machine learning algorithm based on multilayer neural networks.
Within the last few years, we have seen the transformative impact of deep
learning in many domains, particularly in speech recognition and computer
vision, to the extent that the majority of expert practitioners in those field
are now regularly eschewing prior established models in favor of deep learning
models. In this review, we provide an introductory overview into the theory of
deep neural networks and their unique properties that distinguish them from
traditional machine learning algorithms used in cheminformatics. By providing
an overview of the variety of emerging applications of deep neural networks, we
highlight its ubiquity and broad applicability to a wide range of challenges in
the field, including QSAR, virtual screening, protein structure prediction,
quantum chemistry, materials design and property prediction. In reviewing the
performance of deep neural networks, we observed a consistent outperformance
against non-neural networks state-of-the-art models across disparate research
topics, and deep neural network based models often exceeded the "glass ceiling"
expectations of their respective tasks. Coupled with the maturity of
GPU-accelerated computing for training deep neural networks and the exponential
growth of chemical data on which to train these networks on, we anticipate that
deep learning algorithms will be a valuable tool for computational chemistry.
Towards a New Interpretation of Separable Convolutions
Authors: Tapabrata Ghosh
Date: 2017-01-16 00:00:00

In recent times, the use of separable convolutions in deep convolutional
neural network architectures has been explored. Several researchers, most
notably (Chollet, 2016) and (Ghosh, 2017) have used separable convolutions in
their deep architectures and have demonstrated state of the art or close to
state of the art performance. However, the underlying mechanism of action of
separable convolutions are still not fully understood. Although their
mathematical definition is well understood as a depthwise convolution followed
by a pointwise convolution, deeper interpretations such as the extreme
Inception hypothesis (Chollet, 2016) have failed to provide a thorough
explanation of their efficacy. In this paper, we propose a hybrid
interpretation that we believe is a better model for explaining the efficacy of
separable convolutions.
Multi-level Representations for Fine-Grained Typing of Knowledge Base Entities
Authors: Yadollah Yaghoobzadeh, Hinrich Schütze
Date: 2017-01-08 00:00:00
Proceeding: EACL 2017 4
Tasks: Entity Embeddings, Entity Typing, Word Embeddings

Entities are essential elements of natural language. In this paper, we
present methods for learning multi-level representations of entities on three
complementary levels: character (character patterns in entity names extracted,
e.g., by neural networks), word (embeddings of words in entity names) and
entity (entity embeddings). We investigate state-of-the-art learning methods on
each level and find large differences, e.g., for deep learning models,
traditional ngram features and the subword model of fasttext (Bojanowski et
al., 2016) on the character level; for word2vec (Mikolov et al., 2013) on the
word level; and for the order-aware model wang2vec (Ling et al., 2015a) on the
entity level. We confirm experimentally that each level of representation
contributes complementary information and a joint representation of all three
levels improves the existing embedding based baseline for fine-grained entity
typing by a large margin. Additionally, we show that adding information from
entity descriptions further improves multi-level representations of entities.
Improving Gravitational Search Algorithm Performance with Artificial Bee Colony Algorithm for Constrained Numerical Optimization
Authors: Hasan Ali Akyürek, Ömer Kaan Baykan, Barış Koçer
Date: 2017-01-16 00:00:00

In this paper, we propose an improved gravitational search algorithm named
GSABC. The algorithm improves gravitational search algorithm (GSA) results
improved by using artificial bee colony algorithm (ABC) to solve constrained
numerical optimization problems. In GSA, solutions are attracted towards each
other by applying gravitational forces, which depending on the masses assigned
to the solutions, to each other. The heaviest mass will move slower than other
masses and gravitate others. Due to nature of gravitation, GSA may pass global
minimum if some solutions stuck to local minimum. ABC updates the positions of
the best solutions that has obtained from GSA, preventing the GSA from sticking
to the local minimum by its strong searching ability. The proposed algorithm
improves the performance of GSA. The proposed method tested on 23 well-known
unimodal, multimodal and fixed-point multimodal benchmark test functions.
Experimental results show that GSABC outperforms or performs similarly to five
state-of-the-art optimization approaches.
Automatic Knot Adjustment Using Dolphin Echolocation Algorithm for B-Spline Curve Approximation
Authors: Hasan Ali Akyürek, Erkan Ülker, Barış Koçer
Date: 2017-01-16 00:00:00

In this paper, a new approach to solve the cubic B-spline curve fitting
problem is presented based on a meta-heuristic algorithm called " dolphin
echolocation ". The method minimizes the proximity error value of the selected
nodes that measured using the least squares method and the Euclidean distance
method of the new curve generated by the reverse engineering. The results of
the proposed method are compared with the genetic algorithm. As a result, this
new method seems to be successful.
Stochastic Variance-reduced Gradient Descent for Low-rank Matrix Recovery from Linear Measurements
Authors: Xiao Zhang, Lingxiao Wang, Quanquan Gu
Date: 2017-01-02 00:00:00

We study the problem of estimating low-rank matrices from linear measurements
(a.k.a., matrix sensing) through nonconvex optimization. We propose an
efficient stochastic variance reduced gradient descent algorithm to solve a
nonconvex optimization problem of matrix sensing. Our algorithm is applicable
to both noisy and noiseless settings. In the case with noisy observations, we
prove that our algorithm converges to the unknown low-rank matrix at a linear
rate up to the minimax optimal statistical error. And in the noiseless setting,
our algorithm is guaranteed to linearly converge to the unknown low-rank matrix
and achieves exact recovery with optimal sample complexity. Most notably, the
overall computational complexity of our proposed algorithm, which is defined as
the iteration complexity times per iteration time complexity, is lower than the
state-of-the-art algorithms based on gradient descent. Experiments on synthetic
data corroborate the superiority of the proposed algorithm over the
state-of-the-art algorithms.
Classification of MRI data using Deep Learning and Gaussian Process-based Model Selection
Authors: Hadrien Bertrand, Matthieu Perrot, Roberto Ardon, Isabelle Bloch
Date: 2017-01-16 00:00:00
Tasks: Gaussian Processes, General Classification, Model Selection

The classification of MRI images according to the anatomical field of view is
a necessary task to solve when faced with the increasing quantity of medical
images. In parallel, advances in deep learning makes it a suitable tool for
computer vision problems. Using a common architecture (such as AlexNet)
provides quite good results, but not sufficient for clinical use. Improving the
model is not an easy task, due to the large number of hyper-parameters
governing both the architecture and the training of the network, and to the
limited understanding of their relevance. Since an exhaustive search is not
tractable, we propose to optimize the network first by random search, and then
by an adaptive search based on Gaussian Processes and Probability of
Improvement. Applying this method on a large and varied MRI dataset, we show a
substantial improvement between the baseline network and the final one (up to
20\% for the most difficult classes).
Datenqualität in Regressionsproblemen
Authors: Wolfgang Doneit, Ralf Mikut, Markus Reischl
Date: 2017-01-16 00:00:00
Tasks: regression

Regression models are increasingly built using datasets which do not follow a
design of experiment. Instead, the data is e.g. gathered by an automated
monitoring of a technical system. As a consequence, already the input data
represents phenomena of the system and violates statistical assumptions of
distributions. The input data can show correlations, clusters or other
patterns. Further, the distribution of input data influences the reliability of
regression models. We propose criteria to quantify typical phenomena of input
data for regression and show their suitability with simulated benchmark
datasets.
  -----
  Regressionen werden zunehmend auf Datens\"atzen angewendet, deren
Eingangsvektoren nicht durch eine statistische Versuchsplanung festgelegt
wurden. Stattdessen werden die Daten beispielsweise durch die passive
Beobachtung technischer Systeme gesammelt. Damit bilden bereits die
Eingangsdaten Ph\"anomene des Systems ab und widersprechen statistischen
Verteilungsannahmen. Die Verteilung der Eingangsdaten hat Einfluss auf die
Zuverl\"assigkeit eines Regressionsmodells. Wir stellen deshalb
Bewertungskriterien f\"ur einige typische Ph\"anomene in Eingangsdaten von
Regressionen vor und zeigen ihre Funktionalit\"at anhand simulierter
Benchmarkdatens\"atze.
Interactive Elicitation of Knowledge on Feature Relevance Improves Predictions in Small Data Sets
Authors: Luana Micallef, Iiris Sundin, Pekka Marttinen, Muhammad Ammad-Ud-Din, Tomi Peltola, Marta Soare, Giulio Jacucci, Samuel Kaski
Date: 2016-12-07 00:00:00
Tasks: BIG-bench Machine Learning, Prediction, Small Data Image Classification

Providing accurate predictions is challenging for machine learning algorithms
when the number of features is larger than the number of samples in the data.
Prior knowledge can improve machine learning models by indicating relevant
variables and parameter values. Yet, this prior knowledge is often tacit and
only available from domain experts. We present a novel approach that uses
interactive visualization to elicit the tacit prior knowledge and uses it to
improve the accuracy of prediction models. The main component of our approach
is a user model that models the domain expert's knowledge of the relevance of
different features for a prediction task. In particular, based on the expert's
earlier input, the user model guides the selection of the features on which to
elicit user's knowledge next. The results of a controlled user study show that
the user model significantly improves prior knowledge elicitation and
prediction accuracy, when predicting the relative citation counts of scientific
documents in a specific domain.
Semantic classifier approach to document classification
Authors: Piotr Borkowski, Krzysztof Ciesielski, Mieczysław A. Kłopotek
Date: 2017-01-16 00:00:00
Tasks: Classification, Document Classification, General Classification, text-classification, Text Classification

In this paper we propose a new document classification method, bridging
discrepancies (so-called semantic gap) between the training set and the
application sets of textual data. We demonstrate its superiority over classical
text classification approaches, including traditional classifier ensembles. The
method consists in combining a document categorization technique with a single
classifier or a classifier ensemble (SEMCOM algorithm - Committee with Semantic
Categorizer).
Machine Translation Approaches and Survey for Indian Languages
Authors: Nadeem Jadoon Khan, Waqas Anwar, Nadir Durrani
Date: 2017-01-16 00:00:00
Tasks: Machine Translation, Survey, Translation

In this study, we present an analysis regarding the performance of the
state-of-art Phrase-based Statistical Machine Translation (SMT) on multiple
Indian languages. We report baseline systems on several language pairs. The
motivation of this study is to promote the development of SMT and linguistic
resources for these language pairs, as the current state-of-the-art is quite
bleak due to sparse data resources. The success of an SMT system is contingent
on the availability of a large parallel corpus. Such data is necessary to
reliably estimate translation probabilities. We report the performance of
baseline systems translating from Indian languages (Bengali, Guajarati, Hindi,
Malayalam, Punjabi, Tamil, Telugu and Urdu) into English with average 10%
accurate results for all the language pairs.