pwc-archive/papers-with-abstracts huggingface dataset:
Distributed Kernel K-Means for Large Scale Clustering
Authors: Marco Jacopo Ferrarotti, Sergio Decherchi, Walter Rocchia
Date: 2017-10-09 00:00:00
Tasks: Clustering, Computational chemistry, CPU, GPU

Clustering samples according to an effective metric and/or vector space
representation is a challenging unsupervised learning task with a wide spectrum
of applications. Among several clustering algorithms, k-means and its
kernelized version have still a wide audience because of their conceptual
simplicity and efficacy. However, the systematic application of the kernelized
version of k-means is hampered by its inherent square scaling in memory with
the number of samples. In this contribution, we devise an approximate strategy
to minimize the kernel k-means cost function in which the trade-off between
accuracy and velocity is automatically ruled by the available system memory.
Moreover, we define an ad-hoc parallelization scheme well suited for hybrid
cpu-gpu state-of-the-art parallel architectures. We proved the effectiveness
both of the approximation scheme and of the parallelization method on standard
UCI datasets and on molecular dynamics (MD) data in the realm of computational
chemistry. In this applicative domain, clustering can play a key role for both
quantitively estimating kinetics rates via Markov State Models or to give
qualitatively a human compatible summarization of the underlying chemical
phenomenon under study. For these reasons, we selected it as a valuable
real-world application scenario.
CTD: Fast, Accurate, and Interpretable Method for Static and Dynamic Tensor Decompositions
Authors: Jungwoo Lee, Dongjin Choi, Lee Sael
Date: 2017-10-09 00:00:00
Tasks: Tensor Decomposition

How can we find patterns and anomalies in a tensor, or multi-dimensional
array, in an efficient and directly interpretable way? How can we do this in an
online environment, where a new tensor arrives each time step? Finding patterns
and anomalies in a tensor is a crucial problem with many applications,
including building safety monitoring, patient health monitoring, cyber
security, terrorist detection, and fake user detection in social networks.
Standard PARAFAC and Tucker decomposition results are not directly
interpretable. Although a few sampling-based methods have previously been
proposed towards better interpretability, they need to be made faster, more
memory efficient, and more accurate.
  In this paper, we propose CTD, a fast, accurate, and directly interpretable
tensor decomposition method based on sampling. CTD-S, the static version of
CTD, provably guarantees a high accuracy that is 17 ~ 83x more accurate than
that of the state-of-the-art method. Also, CTD-S is made 5 ~ 86x faster, and 7
~ 12x more memory-efficient than the state-of-the-art method by removing
redundancy. CTD-D, the dynamic version of CTD, is the first interpretable
dynamic tensor decomposition method ever proposed. Also, it is made 2 ~ 3x
faster than already fast CTD-S by exploiting factors at previous time step and
by reordering operations. With CTD, we demonstrate how the results can be
effectively interpreted in the online distributed denial of service (DDoS)
attack detection.
A Tutorial on Hawkes Processes for Events in Social Media
Authors: Marian-Andrei Rizoiu, Young Lee, Swapnil Mishra, Lexing Xie
Date: 2017-08-21 00:00:00
Tasks: parameter estimation, Point Processes

This chapter provides an accessible introduction for point processes, and
especially Hawkes processes, for modeling discrete, inter-dependent events over
continuous time. We start by reviewing the definitions and the key concepts in
point processes. We then introduce the Hawkes process, its event intensity
function, as well as schemes for event simulation and parameter estimation. We
also describe a practical example drawn from social media data - we show how to
model retweet cascades using a Hawkes self-exciting process. We presents a
design of the memory kernel, and results on estimating parameters and
predicting popularity. The code and sample event data are available as an
online appendix
On denoising autoencoders trained to minimise binary cross-entropy
Authors: Antonia Creswell, Kai Arulkumaran, Anil A. Bharath
Date: 2017-08-28 00:00:00
Tasks: Decoder, Denoising

Denoising autoencoders (DAEs) are powerful deep learning models used for
feature extraction, data generation and network pre-training. DAEs consist of
an encoder and decoder which may be trained simultaneously to minimise a loss
(function) between an input and the reconstruction of a corrupted version of
the input. There are two common loss functions used for training autoencoders,
these include the mean-squared error (MSE) and the binary cross-entropy (BCE).
When training autoencoders on image data a natural choice of loss function is
BCE, since pixel values may be normalised to take values in [0,1] and the
decoder model may be designed to generate samples that take values in (0,1). We
show theoretically that DAEs trained to minimise BCE may be used to take
gradient steps in the data space towards regions of high probability under the
data-generating distribution. Previously this had only been shown for DAEs
trained using MSE. As a consequence of the theory, iterative application of a
trained DAE moves a data sample from regions of low probability to regions of
higher probability under the data-generating distribution. Firstly, we validate
the theory by showing that novel data samples, consistent with the training
data, may be synthesised when the initial data samples are random noise.
Secondly, we motivate the theory by showing that initial data samples
synthesised via other methods may be improved via iterative application of a
trained DAE to those initial samples.
Neural Semantic Parsing by Character-based Translation: Experiments with Abstract Meaning Representations
Authors: Rik van Noord, Johan Bos
Date: 2017-05-28 00:00:00
Tasks: AMR Parsing, Semantic Parsing, Sentence, Translation

We evaluate the character-level translation method for neural semantic
parsing on a large corpus of sentences annotated with Abstract Meaning
Representations (AMRs). Using a sequence-to-sequence model, and some trivial
preprocessing and postprocessing of AMRs, we obtain a baseline accuracy of 53.1
(F-score on AMR-triples). We examine five different approaches to improve this
baseline result: (i) reordering AMR branches to match the word order of the
input sentence increases performance to 58.3; (ii) adding part-of-speech tags
(automatically produced) to the input shows improvement as well (57.2); (iii)
So does the introduction of super characters (conflating frequent sequences of
characters to a single character), reaching 57.4; (iv) optimizing the training
process by using pre-training and averaging a set of models increases
performance to 58.7; (v) adding silver-standard training data obtained by an
off-the-shelf parser yields the biggest improvement, resulting in an F-score of
64.0. Combining all five techniques leads to an F-score of 71.0 on holdout
data, which is state-of-the-art in AMR parsing. This is remarkable because of
the relative simplicity of the approach.
Age Group and Gender Estimation in the Wild with Deep RoR Architecture
Authors: Ke Zhang, Ce Gao, Liru Guo, Miao Sun, Xingfang Yuan, Tony X. Han, Zhenbing Zhao, Baogang Li
Date: 2017-10-09 00:00:00
Tasks: Age And Gender Classification, Age and Gender Estimation, Gender Classification

Automatically predicting age group and gender from face images acquired in
unconstrained conditions is an important and challenging task in many
real-world applications. Nevertheless, the conventional methods with
manually-designed features on in-the-wild benchmarks are unsatisfactory because
of incompetency to tackle large variations in unconstrained images. This
difficulty is alleviated to some degree through Convolutional Neural Networks
(CNN) for its powerful feature representation. In this paper, we propose a new
CNN based method for age group and gender estimation leveraging Residual
Networks of Residual Networks (RoR), which exhibits better optimization ability
for age group and gender classification than other CNN architectures.Moreover,
two modest mechanisms based on observation of the characteristics of age group
are presented to further improve the performance of age estimation.In order to
further improve the performance and alleviate over-fitting problem, RoR model
is pre-trained on ImageNet firstly, and then it is fune-tuned on the
IMDB-WIKI-101 data set for further learning the features of face images,
finally, it is used to fine-tune on Adience data set. Our experiments
illustrate the effectiveness of RoR method for age and gender estimation in the
wild, where it achieves better performance than other CNN methods. Finally, the
RoR-152+IMDB-WIKI-101 with two mechanisms achieves new state-of-the-art results
on Adience benchmark.
Algorithm guided outlining of 105 pancreatic cancer liver metastases in Ultrasound
Authors: Alexander Hann, Lucas Bettac, Mark M. Haenle, Tilmann Graeter, Andreas W. Berger, Jens Dreyhaupt, Dieter Schmalstieg, Wolfram G. Zoller, Jan Egger
Date: 2017-10-09 00:00:00
Tasks: Segmentation

Manual segmentation of hepatic metastases in ultrasound images acquired from
patients suffering from pancreatic cancer is common practice. Semiautomatic
measurements promising assistance in this process are often assessed using a
small number of lesions performed by examiners who already know the algorithm.
In this work, we present the application of an algorithm for the segmentation
of liver metastases due to pancreatic cancer using a set of 105 different
images of metastases. The algorithm and the two examiners had never assessed
the images before. The examiners first performed a manual segmentation and,
after five weeks, a semiautomatic segmentation using the algorithm. They were
satisfied in up to 90% of the cases with the semiautomatic segmentation
results. Using the algorithm was significantly faster and resulted in a median
Dice similarity score of over 80%. Estimation of the inter-operator variability
by using the intra class correlation coefficient was good with 0.8. In
conclusion, the algorithm facilitates fast and accurate segmentation of liver
metastases, comparable to the current gold standard of manual segmentation.
Sentiment analysis based on rhetorical structure theory: Learning deep neural networks from discourse trees
Authors: Mathias Kraus, Stefan Feuerriegel
Date: 2017-04-18 00:00:00
Tasks: Data Augmentation, Marketing, Sentiment Analysis

Prominent applications of sentiment analysis are countless, covering areas
such as marketing, customer service and communication. The conventional
bag-of-words approach for measuring sentiment merely counts term frequencies;
however, it neglects the position of the terms within the discourse. As a
remedy, we develop a discourse-aware method that builds upon the discourse
structure of documents. For this purpose, we utilize rhetorical structure
theory to label (sub-)clauses according to their hierarchical relationships and
then assign polarity scores to individual leaves. To learn from the resulting
rhetorical structure, we propose a tensor-based, tree-structured deep neural
network (named Discourse-LSTM) in order to process the complete discourse tree.
The underlying tensors infer the salient passages of narrative materials. In
addition, we suggest two algorithms for data augmentation (node reordering and
artificial leaf insertion) that increase our training set and reduce
overfitting. Our benchmarks demonstrate the superior performance of our
approach. Moreover, our tensor structure reveals the salient text passages and
thereby provides explanatory insights.
Neuro Fuzzy Modelling for Prediction of Consumer Price Index
Authors: Godwin Ambukege, Godfrey Justo, Joseph Mushi
Date: 2017-10-09 00:00:00
Tasks: Prediction, Time Series, Time Series Analysis

Economic indicators such as Consumer Price Index (CPI) have frequently used
in predicting future economic wealth for financial policy makers of respective
country. Most central banks, on guidelines of research studies, have recently
adopted an inflation targeting monetary policy regime, which accounts for high
requirement for effective prediction model of consumer price index. However,
prediction accuracy by numerous studies is still low, which raises a need for
improvement. This manuscript presents findings of study that use neuro fuzzy
technique to design a machine-learning model that train and test data to
predict a univariate time series CPI. The study establishes a matrix of monthly
CPI data from secondary data source of Tanzania National Bureau of Statistics
from January 2000 to December 2015 as case study and thereafter conducted
simulation experiments on MATLAB whereby ninety five percent (95%) of data used
to train the model and five percent (5%) for testing. Furthermore, the study
use root mean square error (RMSE) and mean absolute percentage error (MAPE) as
error metrics for model evaluation. The results show that the neuro fuzzy model
have an architecture of 5:74:1 with Gaussian membership functions (2, 2, 2, 2,
2), provides RMSE of 0.44886 and MAPE 0.23384, which is far better compared to
existing research studies.
LD-SDS: Towards an Expressive Spoken Dialogue System based on Linked-Data
Authors: Alexandros Papangelis, Panagiotis Papadakos, Margarita Kotti, Yannis Stylianou, Yannis Tzitzikas, Dimitris Plexousakis
Date: 2017-10-09 00:00:00
Tasks: Conversational Search, Information Retrieval, Retrieval, slot-filling, Slot Filling, Spoken Dialogue Systems

In this work we discuss the related challenges and describe an approach
towards the fusion of state-of-the-art technologies from the Spoken Dialogue
Systems (SDS) and the Semantic Web and Information Retrieval domains. We
envision a dialogue system named LD-SDS that will support advanced, expressive,
and engaging user requests, over multiple, complex, rich, and open-domain data
sources that will leverage the wealth of the available Linked Data.
Specifically, we focus on: a) improving the identification, disambiguation and
linking of entities occurring in data sources and user input; b) offering
advanced query services for exploiting the semantics of the data, with
reasoning and exploratory capabilities; and c) expanding the typical
information seeking dialogue model (slot filling) to better reflect real-world
conversational search scenarios.
Conic Scan-and-Cover algorithms for nonparametric topic modeling
Authors: Mikhail Yurochkin, Aritra Guha, XuanLong Nguyen
Date: 2017-10-09 00:00:00
Proceeding: NeurIPS 2017 12

We propose new algorithms for topic modeling when the number of topics is
unknown. Our approach relies on an analysis of the concentration of mass and
angular geometry of the topic simplex, a convex polytope constructed by taking
the convex hull of vertices representing the latent topics. Our algorithms are
shown in practice to have accuracy comparable to a Gibbs sampler in terms of
topic estimation, which requires the number of topics be given. Moreover, they
are one of the fastest among several state of the art parametric techniques.
Statistical consistency of our estimator is established under some conditions.
Maximum Regularized Likelihood Estimators: A General Prediction Theory and Applications
Authors: Rui Zhuang, Johannes Lederer
Date: 2017-10-09 00:00:00
Tasks: Prediction, regression

Maximum regularized likelihood estimators (MRLEs) are arguably the most
established class of estimators in high-dimensional statistics. In this paper,
we derive guarantees for MRLEs in Kullback-Leibler divergence, a general
measure of prediction accuracy. We assume only that the densities have a convex
parametrization and that the regularization is definite and positive
homogenous. The results thus apply to a very large variety of models and
estimators, such as tensor regression and graphical models with convex and
non-convex regularized methods. A main conclusion is that MRLEs are broadly
consistent in prediction - regardless of whether restricted eigenvalues or
similar conditions hold.
Does Normalization Methods Play a Role for Hyperspectral Image Classification?
Authors: Faxian Cao, Zhijing Yang, Jinchang Ren, Mengying Jiang, Wing-Kuen Ling
Date: 2017-10-09 00:00:00
Tasks: Classification, General Classification, Hyperspectral Image Classification, image-classification, Image Classification

For Hyperspectral image (HSI) datasets, each class have their salient feature
and classifiers classify HSI datasets according to the class's saliency
features, however, there will be different salient features when use different
normalization method. In this letter, we report the effect on classifiers by
different normalization methods and recommend the best normalization methods
for classifier after analyzing the impact of different normalization methods on
classifiers. Pavia University datasets, Indian Pines datasets and Kennedy Space
Center datasets will apply to several typical classifiers in order to evaluate
and analysis the impact of different normalization methods on typical
classifiers.
Visual Servoing of Unmanned Surface Vehicle from Small Tethered Unmanned Aerial Vehicle
Authors: Haresh Karnan, Aritra Biswas, Pranav Vaidik Dhulipala, Jan Dufek, Robin Murphy
Date: 2017-10-09 00:00:00
Tasks: Visual Localization

This paper presents an algorithm and the implementation of a motor schema to
aid the visual localization subsystem of the ongoing EMILY project at Texas A
and M University. The EMILY project aims to team an Unmanned Surface Vehicle
(USV) with an Unmanned Aerial Vehicle (UAV) to augment the search and rescue of
marine casualties during an emergency response phase. The USV is designed to
serve as a flotation device once it reaches the victims. A live video feed from
the UAV is provided to the casuality responders giving them a visual estimate
of the USVs orientation and position to help with its navigation. One of the
challenges involved with casualty response using a USV UAV team is to
simultaneously control the USV and track it. In this paper, we present an
implemented solution to automate the UAV camera movements to keep the USV in
view at all times. The motor schema proposed, uses the USVs coordinates from
the visual localization subsystem to control the UAVs camera movements and
track the USV with minimal camera movements such that the USV is always in the
cameras field of view.
Forecasting Across Time Series Databases using Recurrent Neural Networks on Groups of Similar Series: A Clustering Approach
Authors: Kasun Bandara, Christoph Bergmeir, Slawek Smyl
Date: 2017-10-09 00:00:00
Tasks: Benchmarking, Clustering, Time Series, Time Series Analysis, Time Series Clustering, Time Series Forecasting, Univariate Time Series Forecasting

With the advent of Big Data, nowadays in many applications databases
containing large quantities of similar time series are available. Forecasting
time series in these domains with traditional univariate forecasting procedures
leaves great potentials for producing accurate forecasts untapped. Recurrent
neural networks (RNNs), and in particular Long Short-Term Memory (LSTM)
networks, have proven recently that they are able to outperform
state-of-the-art univariate time series forecasting methods in this context
when trained across all available time series. However, if the time series
database is heterogeneous, accuracy may degenerate, so that on the way towards
fully automatic forecasting methods in this space, a notion of similarity
between the time series needs to be built into the methods. To this end, we
present a prediction model that can be used with different types of RNN models
on subgroups of similar time series, which are identified by time series
clustering techniques. We assess our proposed methodology using LSTM networks,
a widely popular RNN variant. Our method achieves competitive results on
benchmarking datasets under competition evaluation procedures. In particular,
in terms of mean sMAPE accuracy, it consistently outperforms the baseline LSTM
model and outperforms all other methods on the CIF2016 forecasting competition
dataset.
Natural Language Inference from Multiple Premises
Authors: Alice Lai, Yonatan Bisk, Julia Hockenmaier
Date: 2017-10-09 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Natural Language Inference

We define a novel textual entailment task that requires inference over
multiple premise sentences. We present a new dataset for this task that
minimizes trivial lexical inferences, emphasizes knowledge of everyday events,
and presents a more challenging setting for textual entailment. We evaluate
several strong neural baselines and analyze how the multiple premise task
differs from standard textual entailment.
Enhancing Interpretability of Black-box Soft-margin SVM by Integrating Data-based Priors
Authors: Shaohan Chen, Chuanhou Gao, Ping Zhang
Date: 2017-10-09 00:00:00

The lack of interpretability often makes black-box models difficult to be
applied to many practical domains. For this reason, the current work, from the
black-box model input port, proposes to incorporate data-based prior
information into the black-box soft-margin SVM model to enhance its
interpretability. The concept and incorporation mechanism of data-based prior
information are successively developed, based on which the interpretable or
partly interpretable SVM optimization model is designed and then solved through
handily rewriting the optimization problem as a nonlinear quadratic programming
problem. An algorithm for mining data-based linear prior information from data
set is also proposed, which generates a linear expression with respect to two
appropriate inputs identified from all inputs of system. At last, the proposed
interpretability enhancement strategy is applied to eight benchmark examples
for effectiveness exhibition.
Face Sketch Matching via Coupled Deep Transform Learning
Authors: Shruti Nagpal, Maneet Singh, Richa Singh, Mayank Vatsa, Afzel Noore, Angshul Majumdar
Date: 2017-10-09 00:00:00
Proceeding: ICCV 2017 10
Tasks: Face Recognition

Face sketch to digital image matching is an important challenge of face
recognition that involves matching across different domains. Current research
efforts have primarily focused on extracting domain invariant representations
or learning a mapping from one domain to the other. In this research, we
propose a novel transform learning based approach termed as DeepTransformer,
which learns a transformation and mapping function between the features of two
domains. The proposed formulation is independent of the input information and
can be applied with any existing learned or hand-crafted feature. Since the
mapping function is directional in nature, we propose two variants of
DeepTransformer: (i) semi-coupled and (ii) symmetrically-coupled deep transform
learning. This research also uses a novel IIIT-D Composite Sketch with Age
(CSA) variations database which contains sketch images of 150 subjects along
with age-separated digital photos. The performance of the proposed models is
evaluated on a novel application of sketch-to-sketch matching, along with
sketch-to-digital photo matching. Experimental results demonstrate the
robustness of the proposed models in comparison to existing state-of-the-art
sketch matching algorithms and a commercial face recognition system.