pwc-archive/papers-with-abstracts huggingface dataset:
Classification of Things in DBpedia using Deep Neural Networks
Authors: Rahul Parundekar
Date: 2018-02-07 00:00:00
Tasks: Classification, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

The Semantic Web aims at representing knowledge about the real world at web
scale - things, their attributes and relationships among them can be
represented as nodes and edges in an inter-linked semantic graph. In the
presence of noisy data, as is typical of data on the Semantic Web, a software
Agent needs to be able to robustly infer one or more associated actionable
classes for the individuals in order to act automatically on it. We model this
problem as a multi-label classification task where we want to robustly identify
types of the individuals in a semantic graph such as DBpedia, which we use as
an exemplary dataset on the Semantic Web. Our approach first extracts multiple
features for the individuals using random walks and then performs multi-label
classification using fully-connected Neural Networks. Through systematic
exploration and experimentation, we identify the effect of hyper-parameters of
the feature extraction and the fully-connected Neural Network structure on the
classification performance. Our final results show that our method performs
better than state-of-the-art inferencing systems like SDtype and SLCN, from
which we can conclude that random-walk-based feature extraction of individuals
and their multi-label classification using Deep Neural Networks is a promising
alternative to these systems for type classification of individuals on the
Semantic Web. The main contribution of our work is to introduce a novel
approach that allows us to use Deep Neural Networks to identify types of
individuals in a noisy semantic graph by extracting features using random walks
Sketched Subspace Clustering
Authors: Panagiotis A. Traganitis, Georgios B. Giannakis
Date: 2017-07-22 00:00:00
Tasks: Clustering, Dimensionality Reduction

The immense amount of daily generated and communicated data presents unique
challenges in their processing. Clustering, the grouping of data without the
presence of ground-truth labels, is an important tool for drawing inferences
from data. Subspace clustering (SC) is a relatively recent method that is able
to successfully classify nonlinearly separable data in a multitude of settings.
In spite of their high clustering accuracy, SC methods incur prohibitively high
computational complexity when processing large volumes of high-dimensional
data. Inspired by random sketching approaches for dimensionality reduction, the
present paper introduces a randomized scheme for SC, termed Sketch-SC, tailored
for large volumes of high-dimensional data. Sketch-SC accelerates the
computationally heavy parts of state-of-the-art SC approaches by compressing
the data matrix across both dimensions using random projections, thus enabling
fast and accurate large-scale SC. Performance analysis as well as extensive
numerical tests on real data corroborate the potential of Sketch-SC and its
competitive performance relative to state-of-the-art scalable SC approaches.
Multi-Layer Competitive-Cooperative Framework for Performance Enhancement of Differential Evolution
Authors: Sheng Xin Zhang, Li Ming Zheng, Kit Sang Tang, Shao Yong Zheng, Wing Shing Chan
Date: 2018-01-31 00:00:00

Differential Evolution (DE) is recognized as one of the most powerful
optimizers in the evolutionary algorithm (EA) family. Many DE variants were
proposed in recent years, but significant differences in performances between
them are hardly observed. Therefore, this paper suggests a multi-layer
competitive-cooperative (MLCC) framework to facilitate the competition and
cooperation of multiple DEs, which in turns, achieve a significant performance
improvement. Unlike other multi-method strategies which adopt a
multi-population based structure, with individuals only evolving in their
corresponding subpopulations, MLCC implements a parallel structure with the
entire population simultaneously monitored by multiple DEs assigned to their
corresponding layers. An individual can store, utilize and update its evolution
information in different layers based on an individual preference based layer
selecting (IPLS) mechanism and a computational resource allocation bias (RAB)
mechanism. In IPLS, individuals connect to only one favorite layer. While in
RAB, high-quality solutions are evolved by considering all the layers. Thus DEs
associated in the layers work in a competitive and cooperative manner. The
proposed MLCC framework has been implemented on several highly competitive DEs.
Experimental studies show that the MLCC variants significantly outperform the
baseline DEs as well as several state-of-the-art and up-to-date DEs on CEC
benchmark functions.
DeepHeart: Semi-Supervised Sequence Learning for Cardiovascular Risk Prediction
Authors: Brandon Ballinger, Johnson Hsieh, Avesh Singh, Nimit Sohoni, Jack Wang, Geoffrey H. Tison, Gregory M. Marcus, Jose M. Sanchez, Carol Maguire, Jeffrey E. Olgin, Mark J. Pletcher
Date: 2018-02-07 00:00:00
Tasks: Prediction

We train and validate a semi-supervised, multi-task LSTM on 57,675
person-weeks of data from off-the-shelf wearable heart rate sensors, showing
high accuracy at detecting multiple medical conditions, including diabetes
(0.8451), high cholesterol (0.7441), high blood pressure (0.8086), and sleep
apnea (0.8298). We compare two semi-supervised train- ing methods,
semi-supervised sequence learning and heuristic pretraining, and show they
outperform hand-engineered biomarkers from the medical literature. We believe
our work suggests a new approach to patient risk stratification based on
cardiovascular risk scores derived from popular wearables such as Fitbit, Apple
Watch, or Android Wear.
Interactive Natural Language Acquisition in a Multi-modal Recurrent Neural Architecture
Authors: Stefan Heinrich, Stefan Wermter
Date: 2017-03-24 00:00:00
Tasks: Language Acquisition

For the complex human brain that enables us to communicate in natural
language, we gathered good understandings of principles underlying language
acquisition and processing, knowledge about socio-cultural conditions, and
insights about activity patterns in the brain. However, we were not yet able to
understand the behavioural and mechanistic characteristics for natural language
and how mechanisms in the brain allow to acquire and process language. In
bridging the insights from behavioural psychology and neuroscience, the goal of
this paper is to contribute a computational understanding of appropriate
characteristics that favour language acquisition. Accordingly, we provide
concepts and refinements in cognitive modelling regarding principles and
mechanisms in the brain and propose a neurocognitively plausible model for
embodied language acquisition from real world interaction of a humanoid robot
with its environment. In particular, the architecture consists of a continuous
time recurrent neural network, where parts have different leakage
characteristics and thus operate on multiple timescales for every modality and
the association of the higher level nodes of all modalities into cell
assemblies. The model is capable of learning language production grounded in
both, temporal dynamic somatosensation and vision, and features hierarchical
concept abstraction, concept decomposition, multi-modal integration, and
self-organisation of latent representations.
Dimension Reduction Using Active Manifolds
Authors: Robert A. Bridges, Chris Felder, Chelsey Hoff
Date: 2018-02-07 00:00:00
Tasks: Dimensionality Reduction

Scientists and engineers rely on accurate mathematical models to quantify the
objects of their studies, which are often high-dimensional. Unfortunately,
high-dimensional models are inherently difficult, i.e. when observations are
sparse or expensive to determine. One way to address this problem is to
approximate the original model with fewer input dimensions. Our project goal
was to recover a function f that takes n inputs and returns one output, where n
is potentially large. For any given n-tuple, we assume that we can observe a
sample of the gradient and output of the function but it is computationally
expensive to do so. This project was inspired by an approach known as Active
Subspaces, which works by linearly projecting to a linear subspace where the
function changes most on average. Our research gives mathematical developments
informing a novel algorithm for this problem. Our approach, Active Manifolds,
increases accuracy by seeking nonlinear analogues that approximate the
function. The benefits of our approach are eliminated unprincipled parameter,
choices, guaranteed accessible visualization, and improved estimation accuracy.
Cadre Modeling: Simultaneously Discovering Subpopulations and Predictive Models
Authors: Alexander New, Curt Breneman, Kristin P. Bennett
Date: 2018-02-07 00:00:00
Tasks: Clustering, feature selection, Prediction, regression

We consider the problem in regression analysis of identifying subpopulations
that exhibit different patterns of response, where each subpopulation requires
a different underlying model. Unlike statistical cohorts, these subpopulations
are not known a priori; thus, we refer to them as cadres. When the cadres and
their associated models are interpretable, modeling leads to insights about the
subpopulations and their associations with the regression target. We introduce
a discriminative model that simultaneously learns cadre assignment and
target-prediction rules. Sparsity-inducing priors are placed on the model
parameters, under which independent feature selection is performed for both the
cadre assignment and target-prediction processes. We learn models using
adaptive step size stochastic gradient descent, and we assess cadre quality
with bootstrapped sample analysis. We present simulated results showing that,
when the true clustering rule does not depend on the entire set of features,
our method significantly outperforms methods that learn subpopulation-discovery
and target-prediction rules separately. In a materials-by-design case study,
our model provides state-of-the-art prediction of polymer glass transition
temperature. Importantly, the method identifies cadres of polymers that respond
differently to structural perturbations, thus providing design insight for
targeting or avoiding specific transition temperature ranges. It identifies
chemically meaningful cadres, each with interpretable models. Further
experimental results show that cadre methods have generalization that is
competitive with linear and nonlinear regression models and can identify robust
subpopulations.
Spectral Learning of Binomial HMMs for DNA Methylation Data
Authors: Chicheng Zhang, Eran A. Mukamel, Kamalika Chaudhuri
Date: 2018-02-07 00:00:00
Tasks: Computational Efficiency, Tensor Decomposition

We consider learning parameters of Binomial Hidden Markov Models, which may
be used to model DNA methylation data. The standard algorithm for the problem
is EM, which is computationally expensive for sequences of the scale of the
mammalian genome. Recently developed spectral algorithms can learn parameters
of latent variable models via tensor decomposition, and are highly efficient
for large data. However, these methods have only been applied to categorial
HMMs, and the main challenge is how to extend them to Binomial HMMs while still
retaining computational efficiency. We address this challenge by introducing a
new feature-map based approach that exploits specific properties of Binomial
HMMs. We provide theoretical performance guarantees for our algorithm and
evaluate it on real DNA methylation data.
Understanding Anatomy Classification Through Attentive Response Maps
Authors: Devinder Kumar, Vlado Menkovski, Graham W. Taylor, Alexander Wong
Date: 2016-11-19 00:00:00
Tasks: Anatomy, Classification, General Classification

One of the main challenges for broad adoption of deep learning based models
such as convolutional neural networks (CNN), is the lack of understanding of
their decisions. In many applications, a simpler, less capable model that can
be easily understood is favorable to a black-box model that has superior
performance. In this paper, we present an approach for designing CNNs based on
visualization of the internal activations of the model. We visualize the
model's response through attentive response maps obtained using a fractional
stride convolution technique and compare the results with known imaging
landmarks from the medical literature. We show that sufficiently deep and
capable models can be successfully trained to use the same medical landmarks a
human expert would use. Our approach allows for communicating the model
decision process well, but also offers insight towards detecting biases.
Deep Reinforcement Learning for Image Hashing
Authors: Yuxin Peng, Jian Zhang, Zhaoda Ye
Date: 2018-02-07 00:00:00
Tasks: Deep Hashing, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Retrieval

Deep hashing methods have received much attention recently, which achieve
promising results by taking advantage of the strong representation power of
deep networks. However, most existing deep hashing methods learn a whole set of
hashing functions independently, while ignore the correlations between
different hashing functions that can promote the retrieval accuracy greatly.
Inspired by the sequential decision ability of deep reinforcement learning, we
propose a new Deep Reinforcement Learning approach for Image Hashing (DRLIH).
Our proposed DRLIH approach models the hashing learning problem as a sequential
decision process, which learns each hashing function by correcting the errors
imposed by previous ones and promotes retrieval accuracy. To the best of our
knowledge, this is the first work to address hashing problem from deep
reinforcement learning perspective. The main contributions of our proposed
DRLIH approach can be summarized as follows: (1) We propose a deep
reinforcement learning hashing network. In the proposed network, we utilize
recurrent neural network (RNN) as agents to model the hashing functions, which
take actions of projecting images into binary codes sequentially, so that the
current hashing function learning can take previous hashing functions' error
into account. (2) We propose a sequential learning strategy based on proposed
DRLIH. We define the state as a tuple of internal features of RNN's hidden
layers and image features, which can reflect history decisions made by the
agents. We also propose an action group method to enhance the correlation of
hash functions in the same group. Experiments on three widely-used datasets
demonstrate the effectiveness of our proposed DRLIH approach.
SCH-GAN: Semi-supervised Cross-modal Hashing by Generative Adversarial Network
Authors: Jian Zhang, Yuxin Peng, Mingkuan Yuan
Date: 2018-02-07 00:00:00
Tasks: Generative Adversarial Network, Reinforcement Learning, Retrieval

Cross-modal hashing aims to map heterogeneous multimedia data into a common
Hamming space, which can realize fast and flexible retrieval across different
modalities. Supervised cross-modal hashing methods have achieved considerable
progress by incorporating semantic side information. However, they mainly have
two limitations: (1) Heavily rely on large-scale labeled cross-modal training
data which are labor intensive and hard to obtain. (2) Ignore the rich
information contained in the large amount of unlabeled data across different
modalities, especially the margin examples that are easily to be incorrectly
retrieved, which can help to model the correlations. To address these problems,
in this paper we propose a novel Semi-supervised Cross-Modal Hashing approach
by Generative Adversarial Network (SCH-GAN). We aim to take advantage of GAN's
ability for modeling data distributions to promote cross-modal hashing learning
in an adversarial way. The main contributions can be summarized as follows: (1)
We propose a novel generative adversarial network for cross-modal hashing. In
our proposed SCH-GAN, the generative model tries to select margin examples of
one modality from unlabeled data when giving a query of another modality. While
the discriminative model tries to distinguish the selected examples and true
positive examples of the query. These two models play a minimax game so that
the generative model can promote the hashing performance of discriminative
model. (2) We propose a reinforcement learning based algorithm to drive the
training of proposed SCH-GAN. The generative model takes the correlation score
predicted by discriminative model as a reward, and tries to select the examples
close to the margin to promote discriminative model by maximizing the margin
between positive and negative data. Experiments on 3 widely-used datasets
verify the effectiveness of our proposed approach.
Efficient Learning of Bounded-Treewidth Bayesian Networks from Complete and Incomplete Data Sets
Authors: Mauro Scanagatta, Giorgio Corani, Marco Zaffalon, Jaemin Yoo, U Kang
Date: 2018-02-07 00:00:00
Tasks: Imputation

Learning a Bayesian networks with bounded treewidth is important for reducing
the complexity of the inferences. We present a novel anytime algorithm (k-MAX)
method for this task, which scales up to thousands of variables. Through
extensive experiments we show that it consistently yields higher-scoring
structures than its competitors on complete data sets. We then consider the
problem of structure learning from incomplete data sets. This can be addressed
by structural EM, which however is computationally very demanding. We thus
adopt the novel k-MAX algorithm in the maximization step of structural EM,
obtaining an efficient computation of the expected sufficient statistics. We
test the resulting structural EM method on the task of imputing missing data,
comparing it against the state-of-the-art approach based on random forests. Our
approach achieves the same imputation accuracy of the competitors, but in about
one tenth of the time. Furthermore we show that it has worst-case complexity
linear in the input size, and that it is easily parallelizable.
GP-SUM. Gaussian Processes Filtering of non-Gaussian Beliefs
Authors: Maria Bauza, Alberto Rodriguez
Date: 2017-09-23 00:00:00
Tasks: Attribute, Gaussian Processes

This work studies the problem of stochastic dynamic filtering and state
propagation with complex beliefs. The main contribution is GP-SUM, a filtering
algorithm tailored to dynamic systems and observation models expressed as
Gaussian Processes (GP), and to states represented as a weighted sum of
Gaussians. The key attribute of GP-SUM is that it does not rely on
linearizations of the dynamic or observation models, or on unimodal Gaussian
approximations of the belief, hence enables tracking complex state
distributions. The algorithm can be seen as a combination of a sampling-based
filter with a probabilistic Bayes filter. On the one hand, GP-SUM operates by
sampling the state distribution and propagating each sample through the dynamic
system and observation models. On the other hand, it achieves effective
sampling and accurate probabilistic propagation by relying on the GP form of
the system, and the sum-of-Gaussian form of the belief. We show that GP-SUM
outperforms several GP-Bayes and Particle Filters on a standard benchmark. We
also demonstrate its use in a pushing task, predicting with experimental
accuracy the naturally occurring non-Gaussian distributions.
Pixel-Level Alignment of Facial Images for High Accuracy Recognition Using Ensemble of Patches
Authors: Hoda Mohammadzade, Amirhossein Sayyafan, Benyamin Ghojogh
Date: 2018-02-07 00:00:00
Tasks: Face Alignment, Face Recognition, Face Reconstruction, Face to Face Translation

The variation of pose, illumination and expression makes face recognition
still a challenging problem. As a pre-processing in holistic approaches, faces
are usually aligned by eyes. The proposed method tries to perform a pixel
alignment rather than eye-alignment by mapping the geometry of faces to a
reference face while keeping their own textures. The proposed geometry
alignment not only creates a meaningful correspondence among every pixel of all
faces, but also removes expression and pose variations effectively. The
geometry alignment is performed pixel-wise, i.e., every pixel of the face is
corresponded to a pixel of the reference face. In the proposed method, the
information of intensity and geometry of faces are separated properly, trained
by separate classifiers, and finally fused together to recognize human faces.
Experimental results show a great improvement using the proposed method in
comparison to eye-aligned recognition. For instance, at the false acceptance
rate of 0.001, the recognition rates are respectively improved by 24% and 33%
in Yale and AT&T datasets. In LFW dataset, which is a challenging big dataset,
improvement is 20% at FAR of 0.1.
Stochastic Deconvolutional Neural Network Ensemble Training on Generative Pseudo-Adversarial Networks
Authors: Alexey Chaplygin, Joshua Chacksfield
Date: 2018-02-07 00:00:00

The training of Generative Adversarial Networks is a difficult task mainly
due to the nature of the networks. One such issue is when the generator and
discriminator start oscillating, rather than converging to a fixed point.
Another case can be when one agent becomes more adept than the other which
results in the decrease of the other agent's ability to learn, reducing the
learning capacity of the system as a whole. Additionally, there exists the
problem of Mode Collapse which involves the generators output collapsing to a
single sample or a small set of similar samples. To train GANs a careful
selection of the architecture that is used along with a variety of other
methods to improve training. Even when applying these methods there is low
stability of training in relation to the parameters that are chosen. Stochastic
ensembling is suggested as a method for improving the stability while training
GANs.
Evolutionary Computation plus Dynamic Programming for the Bi-Objective Travelling Thief Problem
Authors: Junhua Wu, Sergey Polyakovskiy, Markus Wagner, Frank Neumann
Date: 2018-02-07 00:00:00

This research proposes a novel indicator-based hybrid evolutionary approach
that combines approximate and exact algorithms. We apply it to a new
bi-criteria formulation of the travelling thief problem, which is known to the
Evolutionary Computation community as a benchmark multi-component optimisation
problem that interconnects two classical NP-hard problems: the travelling
salesman problem and the 0-1 knapsack problem. Our approach employs the exact
dynamic programming algorithm for the underlying Packing-While-Travelling (PWT)
problem as a subroutine within a bi-objective evolutionary algorithm. This
design takes advantage of the data extracted from Pareto fronts generated by
the dynamic program to achieve better solutions. Furthermore, we develop a
number of novel indicators and selection mechanisms to strengthen synergy of
the two algorithmic components of our approach. The results of computational
experiments show that the approach is capable to outperform the
state-of-the-art results for the single-objective case of the problem.