pwc-archive/papers-with-abstracts huggingface dataset:
A New 3D Segmentation Methodology for Lumbar Vertebral Bodies for the Measurement of BMD and Geometry
Authors: Andre Mastmeyer, Klaus Engelke, Willi Kalender
Date: 2017-05-19 00:00:00
Tasks: Position, Segmentation

In this paper a new technique is presented that extracts the geometry of
lumbar vertebral bodies from spiral CT scans. Our new multi-step segmentation
approach yields highly accurate and precise measurement of the bone mineral
density (BMD) in different volumes of interest which are defined relative to a
local anatomical coordinate systems. The approach also enables the analysis of
the geometry of the relevant vertebrae. Intra- and inter operator precision for
segmentation, BMD measurement and position of the coordinate system are below
1.5% in patient data, accuracy errors are below 1.5% for BMD and below 4% for
volume in phantom data. The long-term goal of the approach is to improve
fracture prediction in osteoporosis.
Simultaneous Multiple Surface Segmentation Using Deep Learning
Authors: Abhay Shah, Michael Abramoff, Xiaodong Wu
Date: 2017-05-19 00:00:00
Tasks: Deep Learning, global-optimization, Segmentation

The task of automatically segmenting 3-D surfaces representing boundaries of
objects is important for quantitative analysis of volumetric images, and plays
a vital role in biomedical image analysis. Recently, graph-based methods with a
global optimization property have been developed and optimized for various
medical imaging applications. Despite their widespread use, these require human
experts to design transformations, image features, surface smoothness priors,
and re-design for a different tissue, organ or imaging modality. Here, we
propose a Deep Learning based approach for segmentation of the surfaces in
volumetric medical images, by learning the essential features and
transformations from training data, without any human expert intervention. We
employ a regional approach to learn the local surface profiles. The proposed
approach was evaluated on simultaneous intraretinal layer segmentation of
optical coherence tomography (OCT) images of normal retinas and retinas
affected by age related macular degeneration (AMD). The proposed approach was
validated on 40 retina OCT volumes including 20 normal and 20 AMD subjects. The
experiments showed statistically significant improvement in accuracy for our
approach compared to state-of-the-art graph based optimal surface segmentation
with convex priors (G-OSC). A single Convolution Neural Network (CNN) was used
to learn the surfaces for both normal and diseased images. The mean unsigned
surface positioning errors obtained by G-OSC method 2.31 voxels (95% CI
2.02-2.60 voxels) was improved to $1.27$ voxels (95% CI 1.14-1.40 voxels) using
our new approach. On average, our approach takes 94.34 s, requiring 95.35 MB
memory, which is much faster than the 2837.46 s and 6.87 GB memory required by
the G-OSC method on the same computer system.
Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity
Authors: Amit Daniely, Roy Frostig, Yoram Singer
Date: 2016-02-18 00:00:00
Proceeding: NeurIPS 2016 12

We develop a general duality between neural networks and compositional
kernels, striving towards a better understanding of deep learning. We show that
initial representations generated by common random initializations are
sufficiently rich to express all functions in the dual kernel space. Hence,
though the training objective is hard to optimize in the worst case, the
initial weights form a good starting point for optimization. Our dual view also
reveals a pragmatic and aesthetic perspective of neural networks and
underscores their expressive power.
SGD Learns the Conjugate Kernel Class of the Network
Authors: Amit Daniely
Date: 2017-02-27 00:00:00
Proceeding: NeurIPS 2017 12

We show that the standard stochastic gradient decent (SGD) algorithm is
guaranteed to learn, in polynomial time, a function that is competitive with
the best function in the conjugate kernel space of the network, as defined in
Daniely, Frostig and Singer. The result holds for log-depth networks from a
rich family of architectures. To the best of our knowledge, it is the first
polynomial-time guarantee for the standard neural network learning algorithm
for networks of depth more that two.
  As corollaries, it follows that for neural networks of any depth between $2$
and $\log(n)$, SGD is guaranteed to learn, in polynomial time, constant degree
polynomials with polynomially bounded coefficients. Likewise, it follows that
SGD on large enough networks can learn any continuous function (not in
polynomial time), complementing classical expressivity results.
Deep De-Aliasing for Fast Compressive Sensing MRI
Authors: Simiao Yu, Hao Dong, Guang Yang, Greg Slabaugh, Pier Luigi Dragotti, Xujiong Ye, Fangde Liu, Simon Arridge, Jennifer Keegan, David Firmin, Yike Guo
Date: 2017-05-19 00:00:00
Tasks: Compressive Sensing, De-aliasing, Image Reconstruction

Fast Magnetic Resonance Imaging (MRI) is highly in demand for many clinical
applications in order to reduce the scanning cost and improve the patient
experience. This can also potentially increase the image quality by reducing
the motion artefacts and contrast washout. However, once an image field of view
and the desired resolution are chosen, the minimum scanning time is normally
determined by the requirement of acquiring sufficient raw data to meet the
Nyquist-Shannon sampling criteria. Compressive Sensing (CS) theory has been
perfectly matched to the MRI scanning sequence design with much less required
raw data for the image reconstruction. Inspired by recent advances in deep
learning for solving various inverse problems, we propose a conditional
Generative Adversarial Networks-based deep learning framework for de-aliasing
and reconstructing MRI images from highly undersampled data with great promise
to accelerate the data acquisition process. By coupling an innovative content
loss with the adversarial loss our de-aliasing results are more realistic.
Furthermore, we propose a refinement learning procedure for training the
generator network, which can stabilise the training with fast convergence and
less parameter tuning. We demonstrate that the proposed framework outperforms
state-of-the-art CS-MRI methods, in terms of reconstruction error and
perceptual image quality. In addition, our method can reconstruct each image in
0.22ms--0.37ms, which is promising for real-time applications.
A Comparison of Reinforcement Learning Techniques for Fuzzy Cloud Auto-Scaling
Authors: Hamid Arabnejad, Claus Pahl, Pooyan Jamshidi, Giovani Estrada
Date: 2017-05-19 00:00:00
Tasks: Management, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

A goal of cloud service management is to design self-adaptable auto-scaler to
react to workload fluctuations and changing the resources assigned. The key
problem is how and when to add/remove resources in order to meet agreed
service-level agreements. Reducing application cost and guaranteeing
service-level agreements (SLAs) are two critical factors of dynamic controller
design. In this paper, we compare two dynamic learning strategies based on a
fuzzy logic system, which learns and modifies fuzzy scaling rules at runtime. A
self-adaptive fuzzy logic controller is combined with two reinforcement
learning (RL) approaches: (i) Fuzzy SARSA learning (FSL) and (ii) Fuzzy
Q-learning (FQL). As an off-policy approach, Q-learning learns independent of
the policy currently followed, whereas SARSA as an on-policy always
incorporates the actual agent's behavior and leads to faster learning. Both
approaches are implemented and compared in their advantages and disadvantages,
here in the OpenStack cloud platform. We demonstrate that both auto-scaling
approaches can handle various load traffic situations, sudden and periodic, and
delivering resources on demand while reducing operating costs and preventing
SLA violations. The experimental results demonstrate that FSL and FQL have
acceptable performance in terms of adjusted number of virtual machine targeted
to optimize SLA compliance and response time.
Fast Singular Value Shrinkage with Chebyshev Polynomial Approximation Based on Signal Sparsity
Authors: Masaki Onuki, Shunsuke Ono, Keiichiro Shirai, Yuichi Tanaka
Date: 2017-05-19 00:00:00

We propose an approximation method for thresholding of singular values using
Chebyshev polynomial approximation (CPA). Many signal processing problems
require iterative application of singular value decomposition (SVD) for
minimizing the rank of a given data matrix with other cost functions and/or
constraints, which is called matrix rank minimization. In matrix rank
minimization, singular values of a matrix are shrunk by hard-thresholding,
soft-thresholding, or weighted soft-thresholding. However, the computational
cost of SVD is generally too expensive to handle high dimensional signals such
as images; hence, in this case, matrix rank minimization requires enormous
computation time. In this paper, we leverage CPA to (approximately) manipulate
singular values without computing singular values and vectors. The thresholding
of singular values is expressed by a multiplication of certain matrices, which
is derived from a characteristic of CPA. The multiplication is also efficiently
computed using the sparsity of signals. As a result, the computational cost is
significantly reduced. Experimental results suggest the effectiveness of our
method through several image processing applications based on matrix rank
minimization with nuclear norm relaxation in terms of computation time and
approximation precision.
The Kernel Mixture Network: A Nonparametric Method for Conditional Density Estimation of Continuous Random Variables
Authors: Luca Ambrogioni, Umut Güçlü, Marcel A. J. van Gerven, Eric Maris
Date: 2017-05-19 00:00:00
Tasks: Density Estimation

This paper introduces the kernel mixture network, a new method for
nonparametric estimation of conditional probability densities using neural
networks. We model arbitrarily complex conditional densities as linear
combinations of a family of kernel functions centered at a subset of training
points. The weights are determined by the outer layer of a deep neural network,
trained by minimizing the negative log likelihood. This generalizes the popular
quantized softmax approach, which can be seen as a kernel mixture network with
square and non-overlapping kernels. We test the performance of our method on
two important applications, namely Bayesian filtering and generative modeling.
In the Bayesian filtering example, we show that the method can be used to
filter complex nonlinear and non-Gaussian signals defined on manifolds. The
resulting kernel mixture network filter outperforms both the quantized softmax
filter and the extended Kalman filter in terms of model likelihood. Finally,
our experiments on generative models show that, given the same architecture,
the kernel mixture network leads to higher test set likelihood, less
overfitting and more diversified and realistic generated samples than the
quantized softmax approach.
Snapshot Difference Imaging using Time-of-Flight Sensors
Authors: Clara Callenberg, Felix Heide, Gordon Wetzstein, Matthias Hullin
Date: 2017-05-19 00:00:00
Tasks: Diversity

Computational photography encompasses a diversity of imaging techniques, but
one of the core operations performed by many of them is to compute image
differences. An intuitive approach to computing such differences is to capture
several images sequentially and then process them jointly. Usually, this
approach leads to artifacts when recording dynamic scenes. In this paper, we
introduce a snapshot difference imaging approach that is directly implemented
in the sensor hardware of emerging time-of-flight cameras. With a variety of
examples, we demonstrate that the proposed snapshot difference imaging
technique is useful for direct-global illumination separation, for direct
imaging of spatial and temporal image gradients, for direct depth edge imaging,
and more.
The Bag Semantics of Ontology-Based Data Access
Authors: Charalampos Nikolaou, Egor V. Kostylev, George Konstantinidis, Mark Kaminski, Bernardo Cuenca Grau, Ian Horrocks
Date: 2017-05-19 00:00:00

Ontology-based data access (OBDA) is a popular approach for integrating and
querying multiple data sources by means of a shared ontology. The ontology is
linked to the sources using mappings, which assign views over the data to
ontology predicates. Motivated by the need for OBDA systems supporting
database-style aggregate queries, we propose a bag semantics for OBDA, where
duplicate tuples in the views defined by the mappings are retained, as is the
case in standard databases. We show that bag semantics makes conjunctive query
answering in OBDA coNP-hard in data complexity. To regain tractability, we
consider a rather general class of queries and show its rewritability to a
generalisation of the relational calculus to bags.
Efficient Learning of Harmonic Priors for Pitch Detection in Polyphonic Music
Authors: Pablo A. Alvarado, Dan Stowell
Date: 2017-05-19 00:00:00
Tasks: Music Transcription

Automatic music transcription (AMT) aims to infer a latent symbolic
representation of a piece of music (piano-roll), given a corresponding observed
audio recording. Transcribing polyphonic music (when multiple notes are played
simultaneously) is a challenging problem, due to highly structured overlapping
between harmonics. We study whether the introduction of physically inspired
Gaussian process (GP) priors into audio content analysis models improves the
extraction of patterns required for AMT. Audio signals are described as a
linear combination of sources. Each source is decomposed into the product of an
amplitude-envelope, and a quasi-periodic component process. We introduce the
Mat\'ern spectral mixture (MSM) kernel for describing frequency content of
singles notes. We consider two different regression approaches. In the sigmoid
model every pitch-activation is independently non-linear transformed. In the
softmax model several activation GPs are jointly non-linearly transformed. This
introduce cross-correlation between activations. We use variational Bayes for
approximate inference. We empirically evaluate how these models work in
practice transcribing polyphonic music. We demonstrate that rather than
encourage dependency between activations, what is relevant for improving pitch
detection is to learnt priors that fit the frequency content of the sound
events to detect.
Training object class detectors with click supervision
Authors: Dim P. Papadopoulos, Jasper R. R. Uijlings, Frank Keller, Vittorio Ferrari
Date: 2017-04-20 00:00:00
Proceeding: CVPR 2017 7
Tasks: Multiple Instance Learning, Object, Object Localization, Weakly-Supervised Object Localization

Training object class detectors typically requires a large set of images with
objects annotated by bounding boxes. However, manually drawing bounding boxes
is very time consuming. In this paper we greatly reduce annotation time by
proposing center-click annotations: we ask annotators to click on the center of
an imaginary bounding box which tightly encloses the object instance. We then
incorporate these clicks into existing Multiple Instance Learning techniques
for weakly supervised object localization, to jointly localize object bounding
boxes over all training images. Extensive experiments on PASCAL VOC 2007 and MS
COCO show that: (1) our scheme delivers high-quality detectors, performing
substantially better than those produced by weakly supervised techniques, with
a modest extra annotation effort; (2) these detectors in fact perform in a
range close to those trained from manually drawn bounding boxes; (3) as the
center-click task is very fast, our scheme reduces total annotation time by 9x
to 18x.
Induction of Interpretable Possibilistic Logic Theories from Relational Data
Authors: Ondrej Kuzelka, Jesse Davis, Steven Schockaert
Date: 2017-05-19 00:00:00
Tasks: Relational Reasoning

The field of Statistical Relational Learning (SRL) is concerned with learning
probabilistic models from relational data. Learned SRL models are typically
represented using some kind of weighted logical formulas, which make them
considerably more interpretable than those obtained by e.g. neural networks. In
practice, however, these models are often still difficult to interpret
correctly, as they can contain many formulas that interact in non-trivial ways
and weights do not always have an intuitive meaning. To address this, we
propose a new SRL method which uses possibilistic logic to encode relational
models. Learned models are then essentially stratified classical theories,
which explicitly encode what can be derived with a given level of certainty.
Compared to Markov Logic Networks (MLNs), our method is faster and produces
considerably more interpretable models.
Estimating Accuracy from Unlabeled Data: A Probabilistic Logic Approach
Authors: Emmanouil A. Platanios, Hoifung Poon, Tom M. Mitchell, Eric Horvitz
Date: 2017-05-19 00:00:00
Proceeding: NeurIPS 2017 12

We propose an efficient method to estimate the accuracy of classifiers using
only unlabeled data. We consider a setting with multiple classification
problems where the target classes may be tied together through logical
constraints. For example, a set of classes may be mutually exclusive, meaning
that a data instance can belong to at most one of them. The proposed method is
based on the intuition that: (i) when classifiers agree, they are more likely
to be correct, and (ii) when the classifiers make a prediction that violates
the constraints, at least one classifier must be making an error. Experiments
on four real-world data sets produce accuracy estimates within a few percent of
the true accuracy, using solely unlabeled data. Our models also outperform
existing state-of-the-art solutions in both estimating accuracies, and
combining multiple classifier outputs. The results emphasize the utility of
logical constraints in estimating accuracy, thus validating our intuition.
A Neural Representation of Sketch Drawings
Authors: David Ha, Douglas Eck
Date: 2017-04-11 00:00:00
Proceeding: ICLR 2018 1

We present sketch-rnn, a recurrent neural network (RNN) able to construct
stroke-based drawings of common objects. The model is trained on thousands of
crude human-drawn images representing hundreds of classes. We outline a
framework for conditional and unconditional sketch generation, and describe new
robust training methods for generating coherent sketch drawings in a vector
format.
Bitwise Operations of Cellular Automaton on Gray-scale Images
Authors: Karttikeya Mangalam, K. S. Venkatesh
Date: 2017-05-19 00:00:00
Tasks: Denoising, Image Denoising

Cellular Automata (CA) theory is a discrete model that represents the state
of each of its cells from a finite set of possible values which evolve in time
according to a pre-defined set of transition rules. CA have been applied to a
number of image processing tasks such as Convex Hull Detection, Image Denoising
etc. but mostly under the limitation of restricting the input to binary images.
In general, a gray-scale image may be converted to a number of different binary
images which are finally recombined after CA operations on each of them
individually. We have developed a multinomial regression based weighed
summation method to recombine binary images for better performance of CA based
Image Processing algorithms. The recombination algorithm is tested for the
specific case of denoising Salt and Pepper Noise to test against standard
benchmark algorithms such as the Median Filter for various images and noise
levels. The results indicate several interesting invariances in the application
of the CA, such as the particular noise realization and the choice of
sub-sampling of pixels to determine recombination weights. Additionally, it
appears that simpler algorithms for weight optimization which seek local minima
work as effectively as those that seek global minima such as Simulated
Annealing.
What do We Learn by Semantic Scene Understanding for Remote Sensing imagery in CNN framework?
Authors: Haifeng Li, Jian Peng, Chao Tao, Jie Chen, Min Deng
Date: 2017-05-19 00:00:00
Tasks: Object Recognition, Scene Recognition, Scene Understanding

Recently, deep convolutional neural network (DCNN) achieved increasingly
remarkable success and rapidly developed in the field of natural image
recognition. Compared with the natural image, the scale of remote sensing image
is larger and the scene and the object it represents are more macroscopic. This
study inquires whether remote sensing scene and natural scene recognitions
differ and raises the following questions: What are the key factors in remote
sensing scene recognition? Is the DCNN recognition mechanism centered on object
recognition still applicable to the scenarios of remote sensing scene
understanding? We performed several experiments to explore the influence of the
DCNN structure and the scale of remote sensing scene understanding from the
perspective of scene complexity. Our experiment shows that understanding a
complex scene depends on an in-depth network and multiple-scale perception.
Using a visualization method, we qualitatively and quantitatively analyze the
recognition mechanism in a complex remote sensing scene and demonstrate the
importance of multi-objective joint semantic support.
EE-Grad: Exploration and Exploitation for Cost-Efficient Mini-Batch SGD
Authors: Mehmet A. Donmez, Maxim Raginsky, Andrew C. Singer
Date: 2017-05-19 00:00:00

We present a generic framework for trading off fidelity and cost in computing
stochastic gradients when the costs of acquiring stochastic gradients of
different quality are not known a priori. We consider a mini-batch oracle that
distributes a limited query budget over a number of stochastic gradients and
aggregates them to estimate the true gradient. Since the optimal mini-batch
size depends on the unknown cost-fidelity function, we propose an algorithm,
{\it EE-Grad}, that sequentially explores the performance of mini-batch oracles
and exploits the accumulated knowledge to estimate the one achieving the best
performance in terms of cost-efficiency. We provide performance guarantees for
EE-Grad with respect to the optimal mini-batch oracle, and illustrate these
results in the case of strongly convex objectives. We also provide a simple
numerical example that corroborates our theoretical findings.
CardiacNET: Segmentation of Left Atrium and Proximal Pulmonary Veins from MRI Using Multi-View CNN
Authors: Aliasghar Mortazi, Rashed Karim, Kawal Rhode, Jeremy Burt, Ulas Bagci
Date: 2017-05-17 00:00:00
Tasks: Cardiac Segmentation, CPU, GPU, Image Segmentation, Management, Segmentation, Semantic Segmentation, Specificity

Anatomical and biophysical modeling of left atrium (LA) and proximal
pulmonary veins (PPVs) is important for clinical management of several cardiac
diseases. Magnetic resonance imaging (MRI) allows qualitative assessment of LA
and PPVs through visualization. However, there is a strong need for an advanced
image segmentation method to be applied to cardiac MRI for quantitative
analysis of LA and PPVs. In this study, we address this unmet clinical need by
exploring a new deep learning-based segmentation strategy for quantification of
LA and PPVs with high accuracy and heightened efficiency. Our approach is based
on a multi-view convolutional neural network (CNN) with an adaptive fusion
strategy and a new loss function that allows fast and more accurate convergence
of the backpropagation based optimization. After training our network from
scratch by using more than 60K 2D MRI images (slices), we have evaluated our
segmentation strategy to the STACOM 2013 cardiac segmentation challenge
benchmark. Qualitative and quantitative evaluations, obtained from the
segmentation challenge, indicate that the proposed method achieved the
state-of-the-art sensitivity (90%), specificity (99%), precision (94%), and
efficiency levels (10 seconds in GPU, and 7.5 minutes in CPU).