pwc-archive/papers-with-abstracts huggingface dataset:
Construction of neural networks for realization of localized deep learning
Authors: Charles K. Chui, Shao-Bo Lin, Ding-Xuan Zhou
Date: 2018-03-09 00:00:00
Tasks: Deep Learning, Dimensionality Reduction, Handwriting Recognition, Medical Diagnosis, Time Series, Time Series Analysis, Time Series Forecasting

The subject of deep learning has recently attracted users of machine learning
from various disciplines, including: medical diagnosis and bioinformatics,
financial market analysis and online advertisement, speech and handwriting
recognition, computer vision and natural language processing, time series
forecasting, and search engines. However, theoretical development of deep
learning is still at its infancy. The objective of this paper is to introduce a
deep neural network (also called deep-net) approach to localized manifold
learning, with each hidden layer endowed with a specific learning task. For the
purpose of illustrations, we only focus on deep-nets with three hidden layers,
with the first layer for dimensionality reduction, the second layer for bias
reduction, and the third layer for variance reduction. A feedback component
also designed to eliminate outliers. The main theoretical result in this paper
is the order $\mathcal O\left(m^{-2s/(2s+d)}\right)$ of approximation of the
regression function with regularity $s$, in terms of the number $m$ of sample
points, where the (unknown) manifold dimension $d$ replaces the dimension $D$
of the sampling (Euclidean) space for shallow nets.
Review on Computer Vision Techniques in Emergency Situation
Authors: Laura Lopez-Fuentes, Joost Van de Weijer, Manuel Gonzalez-Hidalgo, Harald Skinnemoen, Andrew D. Bagdanov
Date: 2017-08-24 00:00:00

In emergency situations, actions that save lives and limit the impact of
hazards are crucial. In order to act, situational awareness is needed to decide
what to do. Geolocalized photos and video of the situations as they evolve can
be crucial in better understanding them and making decisions faster. Cameras
are almost everywhere these days, either in terms of smartphones, installed
CCTV cameras, UAVs or others. However, this poses challenges in big data and
information overflow. Moreover, most of the time there are no disasters at any
given location, so humans aiming to detect sudden situations may not be as
alert as needed at any point in time. Consequently, computer vision tools can
be an excellent decision support. The number of emergencies where computer
vision tools has been considered or used is very wide, and there is a great
overlap across related emergency research. Researchers tend to focus on
state-of-the-art systems that cover the same emergency as they are studying,
obviating important research in other fields. In order to unveil this overlap,
the survey is divided along four main axes: the types of emergencies that have
been studied in computer vision, the objective that the algorithms can address,
the type of hardware needed and the algorithms used. Therefore, this review
provides a broad overview of the progress of computer vision covering all sorts
of emergencies.
Valuing knowledge, information and agency in Multi-agent Reinforcement Learning: a case study in smart buildings
Authors: Hussain Kazmi, Johan Suykens, Johan Driesen
Date: 2018-03-09 00:00:00
Tasks: Multi-agent Reinforcement Learning, Reinforcement Learning

Increasing energy efficiency in buildings can reduce costs and emissions
substantially. Historically, this has been treated as a local, or single-agent,
optimization problem. However, many buildings utilize the same types of thermal
equipment e.g. electric heaters and hot water vessels. During operation,
occupants in these buildings interact with the equipment differently thereby
driving them to diverse regions in the state-space. Reinforcement learning
agents can learn from these interactions, recorded as sensor data, to optimize
the overall energy efficiency. However, if these agents operate individually at
a household level, they can not exploit the replicated structure in the
problem. In this paper, we demonstrate that this problem can indeed benefit
from multi-agent collaboration by making use of targeted exploration of the
state-space allowing for better generalization. We also investigate trade-offs
between integrating human knowledge and additional sensors. Results show that
savings of over 40% are possible with collaborative multi-agent systems making
use of either expert knowledge or additional sensors with no loss of occupant
comfort. We find that such multi-agent systems comfortably outperform
comparable single agent systems.
Cooperative Starting Movement Detection of Cyclists Using Convolutional Neural Networks and a Boosted Stacking Ensemble
Authors: Maarten Bieshaar, Stefan Zernetsch, Andreas Hubert, Bernhard Sick, Konrad Doll
Date: 2018-03-09 00:00:00

In future, vehicles and other traffic participants will be interconnected and
equipped with various types of sensors, allowing for cooperation on different
levels, such as situation prediction or intention detection. In this article we
present a cooperative approach for starting movement detection of cyclists
using a boosted stacking ensemble approach realizing feature- and decision
level cooperation. We introduce a novel method based on a 3D Convolutional
Neural Network (CNN) to detect starting motions on image sequences by learning
spatio-temporal features. The CNN is complemented by a smart device based
starting movement detection originating from smart devices carried by the
cyclist. Both model outputs are combined in a stacking ensemble approach using
an extreme gradient boosting classifier resulting in a fast and yet robust
cooperative starting movement detector. We evaluate our cooperative approach on
real-world data originating from experiments with 49 test subjects consisting
of 84 starting motions.
Online Spatial Concept and Lexical Acquisition with Simultaneous Localization and Mapping
Authors: Akira Taniguchi, Yoshinobu Hagiwara, Tadahiro Taniguchi, Tetsunari Inamura
Date: 2017-04-15 00:00:00
Tasks: Language Modeling, Language Modelling, Simultaneous Localization and Mapping

In this paper, we propose an online learning algorithm based on a
Rao-Blackwellized particle filter for spatial concept acquisition and mapping.
We have proposed a nonparametric Bayesian spatial concept acquisition model
(SpCoA). We propose a novel method (SpCoSLAM) integrating SpCoA and FastSLAM in
the theoretical framework of the Bayesian generative model. The proposed method
can simultaneously learn place categories and lexicons while incrementally
generating an environmental map. Furthermore, the proposed method has scene
image features and a language model added to SpCoA. In the experiments, we
tested online learning of spatial concepts and environmental maps in a novel
environment of which the robot did not have a map. Then, we evaluated the
results of online learning of spatial concepts and lexical acquisition. The
experimental results demonstrated that the robot was able to more accurately
learn the relationships between words and the place in the environmental map
incrementally by using the proposed method.
Improved and Scalable Online Learning of Spatial Concepts and Language Models with Mapping
Authors: Akira Taniguchi, Yoshinobu Hagiwara, Tadahiro Taniguchi, Tetsunari Inamura
Date: 2018-03-09 00:00:00

We propose a novel online learning algorithm, called SpCoSLAM 2.0, for spatial concepts and lexical acquisition with high accuracy and scalability. Previously, we proposed SpCoSLAM as an online learning algorithm based on unsupervised Bayesian probabilistic model that integrates multimodal place categorization, lexical acquisition, and SLAM. However, our original algorithm had limited estimation accuracy owing to the influence of the early stages of learning, and increased computational complexity with added training data. Therefore, we introduce techniques such as fixed-lag rejuvenation to reduce the calculation time while maintaining an accuracy higher than that of the original algorithm. The results show that, in terms of estimation accuracy, the proposed algorithm exceeds the original algorithm and is comparable to batch learning. In addition, the calculation time of the proposed algorithm does not depend on the amount of training data and becomes constant for each step of the scalable algorithm. Our approach will contribute to the realization of long-term spatial language interactions between humans and robots.
BoxCars: Improving Fine-Grained Recognition of Vehicles using 3-D Bounding Boxes in Traffic Surveillance
Authors: Jakub Sochor, Jakub Špaňhel, Adam Herout
Date: 2017-03-02 00:00:00
Tasks: 3D Object Detection, Vehicle Pose Estimation

In this paper, we focus on fine-grained recognition of vehicles mainly in
traffic surveillance applications. We propose an approach that is orthogonal to
recent advancements in fine-grained recognition (automatic part discovery and
bilinear pooling). In addition, in contrast to other methods focused on
fine-grained recognition of vehicles, we do not limit ourselves to a
frontal/rear viewpoint, but allow the vehicles to be seen from any viewpoint.
Our approach is based on 3-D bounding boxes built around the vehicles. The
bounding box can be automatically constructed from traffic surveillance data.
For scenarios where it is not possible to use precise construction, we propose
a method for an estimation of the 3-D bounding box. The 3-D bounding box is
used to normalize the image viewpoint by "unpacking" the image into a plane. We
also propose to randomly alter the color of the image and add a rectangle with
random noise to a random position in the image during the training of
convolutional neural networks (CNNs). We have collected a large fine-grained
vehicle data set BoxCars116k, with 116k images of vehicles from various
viewpoints taken by numerous surveillance cameras. We performed a number of
experiments, which show that our proposed method significantly improves CNN
classification accuracy (the accuracy is increased by up to 12% points and the
error is reduced by up to 50% compared with CNNs without the proposed
modifications). We also show that our method outperforms the state-of-the-art
methods for fine-grained recognition.
Highly Automated Learning for Improved Active Safety of Vulnerable Road Users
Authors: Maarten Bieshaar, Günther Reitberger, Viktor Kreß, Stefan Zernetsch, Konrad Doll, Erich Fuchs, Bernhard Sick
Date: 2018-03-09 00:00:00
Tasks: Active Learning, BIG-bench Machine Learning, Novelty Detection

Highly automated driving requires precise models of traffic participants.
Many state of the art models are currently based on machine learning
techniques. Among others, the required amount of labeled data is one major
challenge. An autonomous learning process addressing this problem is proposed.
The initial models are iteratively refined in three steps: (1) detection and
context identification, (2) novelty detection and active learning and (3)
online model adaption.
Deep Auxiliary Learning for Visual Localization and Odometry
Authors: Abhinav Valada, Noha Radwan, Wolfram Burgard
Date: 2018-03-09 00:00:00
Tasks: Auxiliary Learning, Visual Localization, Visual Odometry

Localization is an indispensable component of a robot's autonomy stack that
enables it to determine where it is in the environment, essentially making it a
precursor for any action execution or planning. Although convolutional neural
networks have shown promising results for visual localization, they are still
grossly outperformed by state-of-the-art local feature-based techniques. In
this work, we propose VLocNet, a new convolutional neural network architecture
for 6-DoF global pose regression and odometry estimation from consecutive
monocular images. Our multitask model incorporates hard parameter sharing, thus
being compact and enabling real-time inference, in addition to being end-to-end
trainable. We propose a novel loss function that utilizes auxiliary learning to
leverage relative pose information during training, thereby constraining the
search space to obtain consistent pose estimates. We evaluate our proposed
VLocNet on indoor as well as outdoor datasets and show that even our single
task model exceeds the performance of state-of-the-art deep architectures for
global localization, while achieving competitive performance for visual
odometry estimation. Furthermore, we present extensive experimental evaluations
utilizing our proposed Geometric Consistency Loss that show the effectiveness
of multitask learning and demonstrate that our model is the first deep learning
technique to be on par with, and in some cases outperforms state-of-the-art
SIFT-based approaches.
An Unsupervised Model with Attention Autoencoders for Question Retrieval
Authors: Minghua Zhang, Yunfang Wu
Date: 2018-03-09 00:00:00
Tasks: Community Question Answering, Feature Engineering, Question Answering, Retrieval

Question retrieval is a crucial subtask for community question answering.
Previous research focus on supervised models which depend heavily on training
data and manual feature engineering. In this paper, we propose a novel
unsupervised framework, namely reduced attentive matching network (RAMN), to
compute semantic matching between two questions. Our RAMN integrates together
the deep semantic representations, the shallow lexical mismatching information
and the initial rank produced by an external search engine. For the first time,
we propose attention autoencoders to generate semantic representations of
questions. In addition, we employ lexical mismatching to capture surface
matching between two questions, which is derived from the importance of each
word in a question. We conduct experiments on the open CQA datasets of
SemEval-2016 and SemEval-2017. The experimental results show that our
unsupervised model obtains comparable performance with the state-of-the-art
supervised methods in SemEval-2016 Task 3, and outperforms the best system in
SemEval-2017 Task 3 by a wide margin.
A Stochastic Semismooth Newton Method for Nonsmooth Nonconvex Optimization
Authors: Andre Milzarek, Xiantao Xiao, Shicong Cen, Zaiwen Wen, Michael Ulbrich
Date: 2018-03-09 00:00:00
Tasks: Binary Classification, Stochastic Optimization

In this work, we present a globalized stochastic semismooth Newton method for
solving stochastic optimization problems involving smooth nonconvex and
nonsmooth convex terms in the objective function. We assume that only noisy
gradient and Hessian information of the smooth part of the objective function
is available via calling stochastic first and second order oracles. The
proposed method can be seen as a hybrid approach combining stochastic
semismooth Newton steps and stochastic proximal gradient steps. Two inexact
growth conditions are incorporated to monitor the convergence and the
acceptance of the semismooth Newton steps and it is shown that the algorithm
converges globally to stationary points in expectation. Moreover, under
standard assumptions and utilizing random matrix concentration inequalities, we
prove that the proposed approach locally turns into a pure stochastic
semismooth Newton method and converges r-superlinearly with high probability.
We present numerical results and comparisons on $\ell_1$-regularized logistic
regression and nonconvex binary classification that demonstrate the efficiency
of our algorithm.
AEkNN: An AutoEncoder kNN-based classifier with built-in dimensionality reduction
Authors: Francisco J. Pulgar, Francisco Charte, Antonio J. Rivera, María J. del Jesus
Date: 2018-02-23 00:00:00
Tasks: Dimensionality Reduction, General Classification

High dimensionality, i.e. data having a large number of variables, tends to
be a challenge for most machine learning tasks, including classification. A
classifier usually builds a model representing how a set of inputs explain the
outputs. The larger is the set of inputs and/or outputs, the more complex would
be that model. There is a family of classification algorithms, known as lazy
learning methods, which does not build a model. One of the best known members
of this family is the kNN algorithm. Its strategy relies on searching a set of
nearest neighbors, using the input variables as position vectors and computing
distances among them. These distances loss significance in high-dimensional
spaces. Therefore kNN, as many other classifiers, tends to worse its
performance as the number of input variables grows.
  In this work AEkNN, a new kNN-based algorithm with built-in dimensionality
reduction, is presented. Aiming to obtain a new representation of the data,
having a lower dimensionality but with more informational features, AEkNN
internally uses autoencoders. From this new feature vectors the computed
distances should be more significant, thus providing a way to choose better
neighbors. A experimental evaluation of the new proposal is conducted,
analyzing several configurations and comparing them against the classical kNN
algorithm. The obtained conclusions demonstrate that AEkNN offers better
results in predictive and runtime performance.
Solving Fourier ptychographic imaging problems via neural network modeling and TensorFlow
Authors: Shaowei Jiang, Kaikai Guo, Jun Liao, Guoan Zheng
Date: 2018-03-09 00:00:00
Tasks: Retrieval

Fourier ptychography is a recently developed imaging approach for large
field-of-view and high-resolution microscopy. Here we model the Fourier
ptychographic forward imaging process using a convolution neural network (CNN)
and recover the complex object information in the network training process. In
this approach, the input of the network is the point spread function in the
spatial domain or the coherent transfer function in the Fourier domain. The
object is treated as 2D learnable weights of a convolution or a multiplication
layer. The output of the network is modeled as the loss function we aim to
minimize. The batch size of the network corresponds to the number of captured
low-resolution images in one forward / backward pass. We use a popular
open-source machine learning library, TensorFlow, for setting up the network
and conducting the optimization process. We analyze the performance of
different learning rates, different solvers, and different batch sizes. It is
shown that a large batch size with the Adam optimizer achieves the best
performance in general. To accelerate the phase retrieval process, we also
discuss a strategy to implement Fourier-magnitude projection using a
multiplication neural network model. Since convolution and multiplication are
the two most-common operations in imaging modeling, the reported approach may
provide a new perspective to examine many coherent and incoherent systems. As a
demonstration, we discuss the extensions of the reported networks for modeling
single-pixel imaging and structured illumination microscopy (SIM). 4-frame
resolution doubling is demonstrated using a neural network for SIM. We have
made our implementation code open-source for the broad research community.
Bayesian Optimization for Dynamic Problems
Authors: Favour M. Nyikosa, Michael A. Osborne, Stephen J. Roberts
Date: 2018-03-09 00:00:00
Tasks: Bayesian Optimization

We propose practical extensions to Bayesian optimization for solving dynamic
problems. We model dynamic objective functions using spatiotemporal Gaussian
process priors which capture all the instances of the functions over time. Our
extensions to Bayesian optimization use the information learnt from this model
to guide the tracking of a temporally evolving minimum. By exploiting temporal
correlations, the proposed method also determines when to make evaluations, how
fast to make those evaluations, and it induces an appropriate budget of steps
based on the available information. Lastly, we evaluate our technique on
synthetic and real-world problems.
MonoCap: Monocular Human Motion Capture using a CNN Coupled with a Geometric Prior
Authors: Xiaowei Zhou, Menglong Zhu, Georgios Pavlakos, Spyridon Leonardos, Kostantinos G. Derpanis, Kostas Daniilidis
Date: 2017-01-09 00:00:00
Tasks: 3D geometry

Recovering 3D full-body human pose is a challenging problem with many
applications. It has been successfully addressed by motion capture systems with
body worn markers and multiple cameras. In this paper, we address the more
challenging case of not only using a single camera but also not leveraging
markers: going directly from 2D appearance to 3D geometry. Deep learning
approaches have shown remarkable abilities to discriminatively learn 2D
appearance features. The missing piece is how to integrate 2D, 3D and temporal
information to recover 3D geometry and account for the uncertainties arising
from the discriminative model. We introduce a novel approach that treats 2D
joint locations as latent variables whose uncertainty distributions are given
by a deep fully convolutional neural network. The unknown 3D poses are modeled
by a sparse representation and the 3D parameter estimates are realized via an
Expectation-Maximization algorithm, where it is shown that the 2D joint
location uncertainties can be conveniently marginalized out during inference.
Extensive evaluation on benchmark datasets shows that the proposed approach
achieves greater accuracy over state-of-the-art baselines. Notably, the
proposed approach does not require synchronized 2D-3D data for training and is
applicable to "in-the-wild" images, which is demonstrated with the MPII
dataset.
Robust Landmark Detection for Alignment of Mouse Brain Section Images
Authors: Yuncong Chen, David Kleinfeld, Martyn Goulding, Yoav Freund
Date: 2018-03-09 00:00:00

Brightfield and fluorescent imaging of whole brain sections are funda- mental
tools of research in mouse brain study. As sectioning and imaging become more
efficient, there is an increasing need to automate the post-processing of sec-
tions for alignment and three dimensional visualization. There is a further
need to facilitate the development of a digital atlas, i.e. a brain-wide map
annotated with cell type and tract tracing data, which would allow the
automatic registra- tion of images stacks to a common coordinate system.
Currently, registration of slices requires manual identification of landmarks.
In this work we describe the first steps in developing a semi-automated system
to construct a histology at- las of mouse brainstem that combines atlas-guided
annotation, landmark-based registration and atlas generation in an iterative
framework. We describe an unsu- pervised approach for identifying and matching
region and boundary landmarks, based on modelling texture. Experiments show
that the detected landmarks corre- spond well with brain structures, and
matching is robust under distortion. These results will serve as the basis for
registration and atlas building.
Institutional Metaphors for Designing Large-Scale Distributed AI versus AI Techniques for Running Institutions
Authors: Alexander Boer, Giovanni Sileno
Date: 2018-03-09 00:00:00

Artificial Intelligence (AI) started out with an ambition to reproduce the human mind, but, as the sheer scale of that ambition became manifest, it quickly retreated into either studying specialized intelligent behaviours, or proposing over-arching architectural concepts for interfacing specialized intelligent behaviour components, conceived of as agents in a kind of organization. This agent-based modeling paradigm, in turn, proves to have interesting applications in understanding, simulating, and predicting the behaviour of social and legal structures on an aggregate level. For these reasons, this chapter examines a number of relevant cross-cutting concerns, conceptualizations, modeling problems and design challenges in large-scale distributed Artificial Intelligence, as well as in institutional systems, and identifies potential grounds for novel advances.