pwc-archive/papers-with-abstracts huggingface dataset:
Prediction performance after learning in Gaussian process regression
Authors: Johan Wågberg, Dave Zachariah, Thomas B. Schön, Petre Stoica
Date: 2016-06-13 00:00:00
Tasks: Gaussian Processes, Prediction, regression

This paper considers the quantification of the prediction performance in
Gaussian process regression. The standard approach is to base the prediction
error bars on the theoretical predictive variance, which is a lower bound on
the mean square-error (MSE). This approach, however, does not take into account
that the statistical model is learned from the data. We show that this omission
leads to a systematic underestimation of the prediction errors. Starting from a
generalization of the Cram\'er-Rao bound, we derive a more accurate MSE bound
which provides a measure of uncertainty for prediction of Gaussian processes.
The improved bound is easily computed and we illustrate it using synthetic and
real data examples. of uncertainty for prediction of Gaussian processes and
illustrate it using synthetic and real data examples.
Regularizing CNNs with Locally Constrained Decorrelations
Authors: Pau Rodríguez, Jordi Gonzàlez, Guillem Cucurull, Josep M. Gonfaus, Xavier Roca
Date: 2016-11-07 00:00:00

Regularization is key for deep learning since it allows training more complex
models while keeping lower levels of overfitting. However, the most prevalent
regularizations do not leverage all the capacity of the models since they rely
on reducing the effective number of parameters. Feature decorrelation is an
alternative for using the full capacity of the models but the overfitting
reduction margins are too narrow given the overhead it introduces. In this
paper, we show that regularizing negatively correlated features is an obstacle
for effective decorrelation and present OrthoReg, a novel regularization
technique that locally enforces feature orthogonality. As a result, imposing
locality constraints in feature decorrelation removes interferences between
negatively correlated feature weights, allowing the regularizer to reach higher
decorrelation bounds, and reducing the overfitting more effectively. In
particular, we show that the models regularized with OrthoReg have higher
accuracy bounds even when batch normalization and dropout are present.
Moreover, since our regularization is directly performed on the weights, it is
especially suitable for fully convolutional neural networks, where the weight
space is constant compared to the feature map space. As a result, we are able
to reduce the overfitting of state-of-the-art CNNs on CIFAR-10, CIFAR-100, and
SVHN.
Any-Angle Pathfinding for Multiple Agents Based on SIPP Algorithm
Authors: Konstantin Yakovlev, Anton Andreychuk
Date: 2017-03-12 00:00:00

The problem of finding conflict-free trajectories for multiple agents of
identical circular shape, operating in shared 2D workspace, is addressed in the
paper and decoupled, e.g., prioritized, approach is used to solve this problem.
Agents' workspace is tessellated into the square grid on which any-angle moves
are allowed, e.g. each agent can move into an arbitrary direction as long as
this move follows the straight line segment whose endpoints are tied to the
distinct grid elements. A novel any-angle planner based on Safe Interval Path
Planning (SIPP) algorithm is proposed to find trajectories for an agent moving
amidst dynamic obstacles (other agents) on a grid. This algorithm is then used
as part of a prioritized multi-agent planner AA-SIPP(m). On the theoretical,
side we show that AA-SIPP(m) is complete under well-defined conditions. On the
experimental side, in simulation tests with up to 200 agents involved, we show
that our planner finds much better solutions in terms of cost (up to 20%)
compared to the planners relying on cardinal moves only.
Neural Programming by Example
Authors: Chengxun Shu, Hongyu Zhang
Date: 2017-03-15 00:00:00

Programming by Example (PBE) targets at automatically inferring a computer
program for accomplishing a certain task from sample input and output. In this
paper, we propose a deep neural networks (DNN) based PBE model called Neural
Programming by Example (NPBE), which can learn from input-output strings and
induce programs that solve the string manipulation problems. Our NPBE model has
four neural network based components: a string encoder, an input-output
analyzer, a program generator, and a symbol selector. We demonstrate the
effectiveness of NPBE by training it end-to-end to solve some common string
manipulation problems in spreadsheet systems. The results show that our model
can induce string manipulation programs effectively. Our work is one step
towards teaching DNN to generate computer programs.
Label Stability in Multiple Instance Learning
Authors: Veronika Cheplygina, Lauge Sørensen, David M. J. Tax, Marleen de Bruijne, Marco Loog
Date: 2017-03-15 00:00:00
Tasks: Medical Image Analysis, Multiple Instance Learning

We address the problem of \emph{instance label stability} in multiple
instance learning (MIL) classifiers. These classifiers are trained only on
globally annotated images (bags), but often can provide fine-grained
annotations for image pixels or patches (instances). This is interesting for
computer aided diagnosis (CAD) and other medical image analysis tasks for which
only a coarse labeling is provided. Unfortunately, the instance labels may be
unstable. This means that a slight change in training data could potentially
lead to abnormalities being detected in different parts of the image, which is
undesirable from a CAD point of view. Despite MIL gaining popularity in the CAD
literature, this issue has not yet been addressed. We investigate the stability
of instance labels provided by several MIL classifiers on 5 different datasets,
of which 3 are medical image datasets (breast histopathology, diabetic
retinopathy and computed tomography lung images). We propose an unsupervised
measure to evaluate instance stability, and demonstrate that a
performance-stability trade-off can be made when comparing MIL classifiers.
Transfer Learning by Asymmetric Image Weighting for Segmentation across Scanners
Authors: Veronika Cheplygina, Annegreet van Opbroek, M. Arfan Ikram, Meike W. Vernooij, Marleen de Bruijne
Date: 2017-03-15 00:00:00
Tasks: Clustering, Lesion Segmentation, Segmentation, Transfer Learning

Supervised learning has been very successful for automatic segmentation of
images from a single scanner. However, several papers report deteriorated
performances when using classifiers trained on images from one scanner to
segment images from other scanners. We propose a transfer learning classifier
that adapts to differences between training and test images. This method uses a
weighted ensemble of classifiers trained on individual images. The weight of
each classifier is determined by the similarity between its training image and
the test image.
  We examine three unsupervised similarity measures, which can be used in
scenarios where no labeled data from a newly introduced scanner or scanning
protocol is available. The measures are based on a divergence, a bag distance,
and on estimating the labels with a clustering procedure. These measures are
asymmetric. We study whether the asymmetry can improve classification. Out of
the three similarity measures, the bag similarity measure is the most robust
across different studies and achieves excellent results on four brain tissue
segmentation datasets and three white matter lesion segmentation datasets,
acquired at different centers and with different scanners and scanning
protocols. We show that the asymmetry can indeed be informative, and that
computing the similarity from the test image to the training images is more
appropriate than the opposite direction.
Classification of COPD with Multiple Instance Learning
Authors: Veronika Cheplygina, Lauge Sørensen, David M. J. Tax, Jesper Holst Pedersen, Marco Loog, Marleen de Bruijne
Date: 2017-03-15 00:00:00
Tasks: Classification, General Classification, Multiple Instance Learning

Chronic obstructive pulmonary disease (COPD) is a lung disease where early
detection benefits the survival rate. COPD can be quantified by classifying
patches of computed tomography images, and combining patch labels into an
overall diagnosis for the image. As labeled patches are often not available,
image labels are propagated to the patches, incorrectly labeling healthy
patches in COPD patients as being affected by the disease. We approach
quantification of COPD from lung images as a multiple instance learning (MIL)
problem, which is more suitable for such weakly labeled data. We investigate
various MIL assumptions in the context of COPD and show that although a concept
region with COPD-related disease patterns is present, considering the whole
distribution of lung tissue patches improves the performance. The best method
is based on averaging instances and obtains an AUC of 0.742, which is higher
than the previously reported best of 0.713 on the same dataset. Using the full
training set further increases performance to 0.776, which is significantly
higher (DeLong test) than previous results.
Viraliency: Pooling Local Virality
Authors: Xavier Alameda-Pineda, Andrea Pilzer, Dan Xu, Nicu Sebe, Elisa Ricci
Date: 2017-03-11 00:00:00
Proceeding: CVPR 2017 7

In our overly-connected world, the automatic recognition of virality - the
quality of an image or video to be rapidly and widely spread in social networks
- is of crucial importance, and has recently awaken the interest of the
computer vision community. Concurrently, recent progress in deep learning
architectures showed that global pooling strategies allow the extraction of
activation maps, which highlight the parts of the image most likely to contain
instances of a certain class. We extend this concept by introducing a pooling
layer that learns the size of the support area to be averaged: the learned
top-N average (LENA) pooling. We hypothesize that the latent concepts (feature
maps) describing virality may require such a rich pooling strategy. We assess
the effectiveness of the LENA layer by appending it on top of a convolutional
siamese architecture and evaluate its performance on the task of predicting and
localizing virality. We report experiments on two publicly available datasets
annotated for virality and show that our method outperforms state-of-the-art
approaches.
WebCaricature: a benchmark for caricature recognition
Authors: Jing Huo, Wenbin Li, Yinghuan Shi, Yang Gao, Hujun Yin
Date: 2017-03-09 00:00:00
Tasks: Caricature, Face Recognition

Studying caricature recognition is fundamentally important to understanding
of face perception. However, little research has been conducted in the computer
vision community, largely due to the shortage of suitable datasets. In this
paper, a new caricature dataset is built, with the objective to facilitate
research in caricature recognition. All the caricatures and face images were
collected from the Web. Compared with two existing datasets, this dataset is
much more challenging, with a much greater number of available images, artistic
styles and larger intra-personal variations. Evaluation protocols are also
offered together with their baseline performances on the dataset to allow fair
comparisons. Besides, a framework for caricature face recognition is presented
to make a thorough analyze of the challenges of caricature recognition. By
analyzing the challenges, the goal is to show problems that worth to be further
investigated. Additionally, based on the evaluation protocols and the
framework, baseline performances of various state-of-the-art algorithms are
provided. A conclusion is that there is still a large space for performance
improvement and the analyzed problems still need further investigation.
Comparison of the Deep-Learning-Based Automated Segmentation Methods for the Head Sectioned Images of the Virtual Korean Human Project
Authors: Mohammad Eshghi, Holger R. Roth, Masahiro Oda, Min Suk Chung, Kensaku MORI
Date: 2017-03-15 00:00:00
Tasks: Segmentation, Semantic Segmentation

This paper presents an end-to-end pixelwise fully automated segmentation of
the head sectioned images of the Visible Korean Human (VKH) project based on
Deep Convolutional Neural Networks (DCNNs). By converting classification
networks into Fully Convolutional Networks (FCNs), a coarse prediction map,
with smaller size than the original input image, can be created for
segmentation purposes. To refine this map and to obtain a dense pixel-wise
output, standard FCNs use deconvolution layers to upsample the coarse map.
However, upsampling based on deconvolution increases the number of network
parameters and causes loss of detail because of interpolation. On the other
hand, dilated convolution is a new technique introduced recently that attempts
to capture multi-scale contextual information without increasing the network
parameters while keeping the resolution of the prediction maps high. We used
both a standard FCN and a dilated convolution based FCN for semantic
segmentation of the head sectioned images of the VKH dataset. Quantitative
results showed approximately 20% improvement in the segmentation accuracy when
using FCNs with dilated convolutions.
Application of Fuzzy Logic in Design of Smart Washing Machine
Authors: Rao Farhat Masood
Date: 2017-01-04 00:00:00
Tasks: Management

Washing machine is of great domestic necessity as it frees us from the burden
of washing our clothes and saves ample of our time. This paper will cover the
aspect of designing and developing of Fuzzy Logic based, Smart Washing Machine.
The regular washing machine (timer based) makes use of multi-turned timer based
start-stop mechanism which is mechanical as is prone to breakage. In addition
to its starting and stopping issues, the mechanical timers are not efficient
with respect of maintenance and electricity usage. Recent developments have
shown that merger of digital electronics in optimal functionality of this
machine is possible and nowadays in practice. A number of international
renowned companies have developed the machine with the introduction of smart
artificial intelligence. Such a machine makes use of sensors and smartly
calculates the amount of run-time (washing time) for the main machine motor.
Realtime calculations and processes are also catered in optimizing the run-time
of the machine. The obvious result is smart time management, better economy of
electricity and efficiency of work. This paper deals with the indigenization of
FLC (Fuzzy Logic Controller) based Washing Machine, which is capable of
automating the inputs and getting the desired output (wash-time).
Matched bipartite block model with covariates
Authors: Zahra S. Razaee, Arash A. Amini, Jingyi Jessica Li
Date: 2017-03-15 00:00:00
Tasks: Clustering, Community Detection, model, Variational Inference

Community detection or clustering is a fundamental task in the analysis of
network data. Many real networks have a bipartite structure which makes
community detection challenging. In this paper, we consider a model which
allows for matched communities in the bipartite setting, in addition to node
covariates with information about the matching. We derive a simple fast
algorithm for fitting the model based on variational inference ideas and show
its effectiveness on both simulated and real data. A variation of the model to
allow for degree-correction is also considered, in addition to a novel approach
to fitting such degree-corrected models.
SyntaxNet Models for the CoNLL 2017 Shared Task
Authors: Chris Alberti, Daniel Andor, Ivan Bogatyy, Michael Collins, Dan Gillick, Lingpeng Kong, Terry Koo, Ji Ma, Mark Omernick, Slav Petrov, Chayut Thanapirom, Zora Tung, David Weiss
Date: 2017-03-15 00:00:00
Tasks: Dependency Parsing

We describe a baseline dependency parsing system for the CoNLL2017 Shared
Task. This system, which we call "ParseySaurus," uses the DRAGNN framework
[Kong et al, 2017] to combine transition-based recurrent parsing and tagging
with character-based word representations. On the v1.3 Universal Dependencies
Treebanks, the new system outpeforms the publicly available, state-of-the-art
"Parsey's Cousins" models by 3.47% absolute Labeled Accuracy Score (LAS) across
52 treebanks.
Emergence of Grounded Compositional Language in Multi-Agent Populations
Authors: Igor Mordatch, Pieter Abbeel
Date: 2017-03-15 00:00:00
Tasks: Machine Translation, Question Answering, Sentiment Analysis, Translation

By capturing statistical patterns in large corpora, machine learning has
enabled significant advances in natural language processing, including in
machine translation, question answering, and sentiment analysis. However, for
agents to intelligently interact with humans, simply capturing the statistical
patterns is insufficient. In this paper we investigate if, and how, grounded
compositional language can emerge as a means to achieve goals in multi-agent
populations. Towards this end, we propose a multi-agent learning environment
and learning methods that bring about emergence of a basic compositional
language. This language is represented as streams of abstract discrete symbols
uttered by agents over time, but nonetheless has a coherent structure that
possesses a defined vocabulary and syntax. We also observe emergence of
non-verbal communication such as pointing and guiding when language
communication is unavailable.
Risk-Sensitive Learning and Pricing for Demand Response
Authors: Kia Khezeli, Eilyan Bitar
Date: 2016-11-21 00:00:00

We consider the setting in which an electric power utility seeks to curtail
its peak electricity demand by offering a fixed group of customers a uniform
price for reductions in consumption relative to their predetermined baselines.
The underlying demand curve, which describes the aggregate reduction in
consumption in response to the offered price, is assumed to be affine and
subject to unobservable random shocks. Assuming that both the parameters of the
demand curve and the distribution of the random shocks are initially unknown to
the utility, we investigate the extent to which the utility might dynamically
adjust its offered prices to maximize its cumulative risk-sensitive payoff over
a finite number of $T$ days. In order to do so effectively, the utility must
design its pricing policy to balance the tradeoff between the need to learn the
unknown demand model (exploration) and maximize its payoff (exploitation) over
time. In this paper, we propose such a pricing policy, which is shown to
exhibit an expected payoff loss over $T$ days that is at most
$O(\sqrt{T}\log(T))$, relative to an oracle pricing policy that knows the
underlying demand model. Moreover, the proposed pricing policy is shown to
yield a sequence of prices that converge to the oracle optimal prices in the
mean square sense.
Cloud Radiative Effect Study Using Sky Camera
Authors: Soumyabrata Dev, Shilpa Manandhar, Feng Yuan, Yee Hui Lee, Stefan Winkler
Date: 2017-03-15 00:00:00

The analysis of clouds in the earth's atmosphere is important for a variety
of applications, viz. weather reporting, climate forecasting, and solar energy
generation. In this paper, we focus our attention on the impact of cloud on the
total solar irradiance reaching the earth's surface. We use weather station to
record the total solar irradiance. Moreover, we employ collocated ground-based
sky camera to automatically compute the instantaneous cloud coverage. We
analyze the relationship between measured solar irradiance and computed cloud
coverage value, and conclude that higher cloud coverage greatly impacts the
total solar irradiance. Such studies will immensely help in solar energy
generation and forecasting.
Sparse Named Entity Classification using Factorization Machines
Authors: Ai Hirata, Mamoru Komachi
Date: 2017-03-15 00:00:00
Tasks: Classification, General Classification

Named entity classification is the task of classifying text-based elements
into various categories, including places, names, dates, times, and monetary
values. A bottleneck in named entity classification, however, is the data
problem of sparseness, because new named entities continually emerge, making it
rather difficult to maintain a dictionary for named entity classification.
Thus, in this paper, we address the problem of named entity classification
using matrix factorization to overcome the problem of feature sparsity.
Experimental results show that our proposed model, with fewer features and a
smaller size, achieves competitive accuracy to state-of-the-art models.
Real-time 3D Human Tracking for Mobile Robots with Multisensors
Authors: Mengmeng Wang, Daobilige Su, Lei Shi, Yong liu, Jaime Valls Miro
Date: 2017-03-15 00:00:00
Tasks: Sensor Fusion, Visual Tracking

Acquiring the accurate 3-D position of a target person around a robot
provides fundamental and valuable information that is applicable to a wide
range of robotic tasks, including home service, navigation and entertainment.
This paper presents a real-time robotic 3-D human tracking system which
combines a monocular camera with an ultrasonic sensor by the extended Kalman
filter (EKF). The proposed system consists of three sub-modules: monocular
camera sensor tracking model, ultrasonic sensor tracking model and multi-sensor
fusion. An improved visual tracking algorithm is presented to provide partial
location estimation (2-D). The algorithm is designed to overcome severe
occlusions, scale variation, target missing and achieve robust re-detection.
The scale accuracy is further enhanced by the estimated 3-D information. An
ultrasonic sensor array is employed to provide the range information from the
target person to the robot and Gaussian Process Regression is used for partial
location estimation (2-D). EKF is adopted to sequentially process multiple,
heterogeneous measurements arriving in an asynchronous order from the vision
sensor and the ultrasonic sensor separately. In the experiments, the proposed
tracking system is tested in both simulation platform and actual mobile robot
for various indoor and outdoor scenes. The experimental results show the
superior performance of the 3-D tracking system in terms of both the accuracy
and robustness.
LesionSeg: Semantic segmentation of skin lesions using Deep Convolutional Neural Network
Authors: Dhanesh Ramachandram, Terrance DeVries
Date: 2017-03-09 00:00:00
Tasks: Lesion Segmentation, Segmentation, Semantic Segmentation, Skin Lesion Segmentation, Super-Resolution

We present a method for skin lesion segmentation for the ISIC 2017 Skin
Lesion Segmentation Challenge. Our approach is based on a Fully Convolutional
Network architecture which is trained end to end, from scratch, on a limited
dataset. Our semantic segmentation architecture utilizes several recent
innovations in particularly in the combined use of (i) use of atrous
convolutions to increase the effective field of view of the network's receptive
field without increasing the number of parameters, (ii) the use of
network-in-network $1\times1$ convolution layers to add capacity to the network
and (iii) state-of-art super-resolution upsampling of predictions using
subpixel CNN layers. We reported a mean IOU score of 0.642 on the validation
set provided by the organisers.