pwc-archive/papers-with-abstracts huggingface dataset:
Deep learning in business analytics and operations research: Models, applications and managerial implications
Authors: Mathias Kraus, Stefan Feuerriegel, Asil Oztekin
Date: 2018-06-28 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning

Business analytics refers to methods and practices that create value through data for individuals, firms, and organizations. This field is currently experiencing a radical shift due to the advent of deep learning: deep neural networks promise improvements in prediction performance as compared to models from traditional machine learning. However, our research into the existing body of literature reveals a scarcity of research works utilizing deep learning in our discipline. Accordingly, the objectives of this overview article are as follows: (1) we review research on deep learning for business analytics from an operational point of view. (2) We motivate why researchers and practitioners from business analytics should utilize deep neural networks and review potential use cases, necessary requirements, and benefits. (3) We investigate the added value to operations research in different case studies with real data from entrepreneurial undertakings. All such cases demonstrate improvements in operational performance over traditional machine learning and thus direct value gains. (4) We provide guidelines and implications for researchers, managers and practitioners in operations research who want to advance their capabilities for business analytics with regard to deep learning. (5) Our computational experiments find that default, out-of-the-box architectures are often suboptimal and thus highlight the value of customized architectures by proposing a novel deep-embedded network.
Efficient CNN Implementation for Eye-Gaze Estimation on Low-Power/Low-Quality Consumer Imaging Systems
Authors: Joseph Lemley, Anuradha Kar, Alexandru Drimbarean, Peter Corcoran
Date: 2018-06-28 00:00:00
Tasks: Gaze Estimation

Accurate and efficient eye gaze estimation is important for emerging consumer
electronic systems such as driver monitoring systems and novel user interfaces.
Such systems are required to operate reliably in difficult, unconstrained
environments with low power consumption and at minimal cost. In this paper a
new hardware friendly, convolutional neural network model with minimal
computational requirements is introduced and assessed for efficient
appearance-based gaze estimation. The model is tested and compared against
existing appearance based CNN approaches, achieving better eye gaze accuracy
with significantly fewer computational requirements. A brief updated literature
review is also provided.
Spatiotemporal Prediction of Ambulance Demand using Gaussian Process Regression
Authors: Seth Nabarro, Tristan Fletcher, John Shawe-Taylor
Date: 2018-06-28 00:00:00
Tasks: GPR, Prediction, regression

Accurately predicting when and where ambulance call-outs occur can reduce
response times and ensure the patient receives urgent care sooner. Here we
present a novel method for ambulance demand prediction using Gaussian process
regression (GPR) in time and geographic space. The method exhibits superior
accuracy to MEDIC, a method which has been used in industry. The use of GPR has
additional benefits such as the quantification of uncertainty with each
prediction, the choice of kernel functions to encode prior knowledge and the
ability to capture spatial correlation. Measures to increase the utility of GPR
in the current context, with large training sets and a Poisson-distributed
output, are outlined.
Exploring Architectures for CNN-Based Word Spotting
Authors: Eugen Rusakov, Sebastian Sudholt, Fabian Wolf, Gernot A. Fink
Date: 2018-06-28 00:00:00
Tasks: Attribute

The goal in word spotting is to retrieve parts of document images which are relevant with respect to a certain user-defined query. The recent past has seen attribute-based Convolutional Neural Networks take over this field of research. As is common for other fields of computer vision, the CNNs used for this task are already considerably deep. The question that arises, however, is: How complex does a CNN have to be for word spotting? Are increasingly deeper models giving increasingly better results or does performance behave asymptotically for these architectures? On the other hand, can similar results be obtained with a much smaller CNN? The goal of this paper is to give an answer to these questions. Therefore, the recently successful TPP-PHOCNet will be compared to a Residual Network, a Densely Connected Convolutional Network and a LeNet architecture empirically. As will be seen in the evaluation, a complex model can be beneficial for word spotting on harder tasks such as the IAM Offline Database but gives no advantage for easier benchmarks such as the George Washington Database.
Feature Selection for Unsupervised Domain Adaptation using Optimal Transport
Authors: Léo Gautheron, Ievgen Redko, Carole Lartizien
Date: 2018-06-28 00:00:00
Tasks: Domain Adaptation, feature selection, Unsupervised Domain Adaptation

In this paper, we propose a new feature selection method for unsupervised
domain adaptation based on the emerging optimal transportation theory. We build
upon a recent theoretical analysis of optimal transport in domain adaptation
and show that it can directly suggest a feature selection procedure leveraging
the shift between the domains. Based on this, we propose a novel algorithm that
aims to sort features by their similarity across the source and target domains,
where the order is obtained by analyzing the coupling matrix representing the
solution of the proposed optimal transportation problem. We evaluate our method
on a well-known benchmark data set and illustrate its capability of selecting
correlated features leading to better classification performances. Furthermore,
we show that the proposed algorithm can be used as a pre-processing step for
existing domain adaptation techniques ensuring an important speed-up in terms
of the computational time while maintaining comparable results. Finally, we
validate our algorithm on clinical imaging databases for computer-aided
diagnosis task with promising results.
DeepSDCS: Dissecting cancer proliferation heterogeneity in Ki67 digital whole slide images
Authors: Priya Lakshmi Narayanan, Shan E Ahmed Raza, Andrew Dodson, Barry Gusterson, Mitchell Dowsett, Yinyin Yuan
Date: 2018-06-28 00:00:00
Tasks: Cancer Classification, Cell Segmentation, General Classification, Segmentation, whole slide images

Ki67 is an important biomarker for breast cancer. Classification of positive
and negative Ki67 cells in histology slides is a common approach to determine
cancer proliferation status. However, there is a lack of generalizable and
accurate methods to automate Ki67 scoring in large-scale patient cohorts. In
this work, we have employed a novel deep learning technique based on
hypercolumn descriptors for cell classification in Ki67 images. Specifically,
we developed the Simultaneous Detection and Cell Segmentation (DeepSDCS)
network to perform cell segmentation and detection. VGG16 network was used for
the training and fine tuning to training data. We extracted the hypercolumn
descriptors of each cell to form the vector of activation from specific layers
to capture features at different granularity. Features from these layers that
correspond to the same pixel were propagated using a stochastic gradient
descent optimizer to yield the detection of the nuclei and the final cell
segmentations. Subsequently, seeds generated from cell segmentation were
propagated to a spatially constrained convolutional neural network for the
classification of the cells into stromal, lymphocyte, Ki67-positive cancer
cell, and Ki67-negative cancer cell. We validated its accuracy in the context
of a large-scale clinical trial of oestrogen-receptor-positive breast cancer.
We achieved 99.06% and 89.59% accuracy on two separate test sets of Ki67
stained breast cancer dataset comprising biopsy and whole-slide images.
Training Discriminative Models to Evaluate Generative Ones
Authors: Timothée Lesort, Andrei Stoain, Jean-François Goudou, David Filliat
Date: 2018-06-28 00:00:00
Tasks: Generative Adversarial Network

Generative models are known to be difficult to assess. Recent works, especially on generative adversarial networks (GANs), produce good visual samples of varied categories of images. However, the validation of their quality is still difficult to define and there is no existing agreement on the best evaluation process. This paper aims at making a step toward an objective evaluation process for generative models. It presents a new method to assess a trained generative model by evaluating the test accuracy of a classifier trained with generated data. The test set is composed of real images. Therefore, The classifier accuracy is used as a proxy to evaluate if the generative model fit the true data distribution. By comparing results with different generated datasets we are able to classify and compare generative models. The motivation of this approach is also to evaluate if generative models can help discriminative neural networks to learn, i.e., measure if training on generated data is able to make a model successful at testing on real settings. Our experiments compare different generators from the Variational Auto-Encoders (VAE) and Generative Adversarial Network (GAN) frameworks on MNIST and fashion MNIST datasets. Our results show that none of the generative models is able to replace completely true data to train a discriminative model. But they also show that the initial GAN and WGAN are the best choices to generate on MNIST database (Modified National Institute of Standards and Technology database) and fashion MNIST database.
CT Image Registration in Acute Stroke Monitoring
Authors: Lucio Amelio, Alessia Amelio
Date: 2018-06-28 00:00:00
Tasks: Image Registration

We present a new system based on tracking the temporal evolution of stroke
lesions using an image registration technique on CT exams of the patient's
brain. The system is able to compare past CT exams with the most recent one
related to stroke event in order to evaluate past lesions which are not related
to stroke. Then, it can compare recent CT exams related to the current stroke
for assessing the evolution of the lesion over time. A new similarity measure
is also introduced for the comparison of the source and target images during
image registration. It will result in a cheaper, faster and more accessible
evaluation of the acute phase of the stroke overcoming the current limitations
of the proposed systems in the state-of-the-art.
Grassmannian Discriminant Maps (GDM) for Manifold Dimensionality Reduction with Application to Image Set Classification
Authors: Rui Wang, Xiao-Jun Wu, Kai-Xuan Chen, Josef Kittler
Date: 2018-06-28 00:00:00
Tasks: Dimensionality Reduction, Face Recognition, General Classification, Gesture Recognition, Hand Gesture Recognition, Hand-Gesture Recognition, Metric Learning, Object Categorization

In image set classification, a considerable progress has been made by representing original image sets on Grassmann manifolds. In order to extend the advantages of the Euclidean based dimensionality reduction methods to the Grassmann Manifold, several methods have been suggested recently which jointly perform dimensionality reduction and metric learning on Grassmann manifold to improve performance. Nevertheless, when applied to complex datasets, the learned features do not exhibit enough discriminatory power. To overcome this problem, we propose a new method named Grassmannian Discriminant Maps (GDM) for manifold dimensionality reduction problems. The core of the method is a new discriminant function for metric learning and dimensionality reduction. For comparison and better understanding, we also study a simple variations to GDM. The key difference between them is the discriminant function. We experiment on data sets corresponding to three tasks: face recognition, object categorization, and hand gesture recognition to evaluate the proposed method and its simple extensions. Compared with the state of the art, the results achieved show the effectiveness of the proposed algorithm.
Deep Learning-Aided Projected Gradient Detector for Massive Overloaded MIMO Channels
Authors: Satoshi Takabe, Masayuki Imanishi, Tadashi Wadayama, Kazunori Hayashi
Date: 2018-06-28 00:00:00
Tasks: Deep Learning

The paper presents a deep learning-aided iterative detection algorithm for
massive overloaded MIMO systems. Since the proposed algorithm is based on the
projected gradient descent method with trainable parameters, it is named as
trainable projected descent-detector (TPG-detector). The trainable internal
parameters can be optimized with standard deep learning techniques such as back
propagation and stochastic gradient descent algorithms. This approach referred
to as data-driven tuning brings notable advantages of the proposed scheme such
as fast convergence. The numerical experiments show that TPG-detector achieves
comparable detection performance to those of the known algorithms for massive
overloaded MIMO channels with lower computation cost.
Automatic Rank Selection for High-Speed Convolutional Neural Network
Authors: Hyeji Kim, Chong-Min Kyung
Date: 2018-06-28 00:00:00
Tasks: Combinatorial Optimization, Vocal Bursts Intensity Prediction

Low-rank decomposition plays a central role in accelerating convolutional
neural network (CNN), and the rank of decomposed kernel-tensor is a key
parameter that determines the complexity and accuracy of a neural network. In
this paper, we define rank selection as a combinatorial optimization problem
and propose a methodology to minimize network complexity while maintaining the
desired accuracy. Combinatorial optimization is not feasible due to search
space limitations. To restrict the search space and obtain the optimal rank, we
define the space constraint parameters with a boundary condition. We also
propose a linearly-approximated accuracy function to predict the fine-tuned
accuracy of the optimized CNN model during the cost reduction. Experimental
results on AlexNet and VGG-16 show that the proposed rank selection algorithm
satisfies the accuracy constraint. Our method combined with truncated-SVD
outperforms state-of-the-art methods in terms of inference and training time at
almost the same accuracy.
Beyond One-hot Encoding: lower dimensional target embedding
Authors: Pau Rodríguez, Miguel A. Bautista, Jordi Gonzàlez, Sergio Escalera
Date: 2018-06-28 00:00:00
Tasks: valid

Target encoding plays a central role when learning Convolutional Neural
Networks. In this realm, One-hot encoding is the most prevalent strategy due to
its simplicity. However, this so widespread encoding schema assumes a flat
label space, thus ignoring rich relationships existing among labels that can be
exploited during training. In large-scale datasets, data does not span the full
label space, but instead lies in a low-dimensional output manifold. Following
this observation, we embed the targets into a low-dimensional space,
drastically improving convergence speed while preserving accuracy. Our
contribution is two fold: (i) We show that random projections of the label
space are a valid tool to find such lower dimensional embeddings, boosting
dramatically convergence rates at zero computational cost; and (ii) we propose
a normalized eigenrepresentation of the class manifold that encodes the targets
with minimal information loss, improving the accuracy of random projections
encoding while enjoying the same convergence rates. Experiments on CIFAR-100,
CUB200-2011, Imagenet, and MIT Places demonstrate that the proposed approach
drastically improves convergence speed while reaching very competitive accuracy
rates.
Hierarchical Reinforcement Learning with Abductive Planning
Authors: Kazeto Yamamoto, Takashi Onishi, Yoshimasa Tsuruoka
Date: 2018-06-28 00:00:00
Tasks: Hierarchical Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

One of the key challenges in applying reinforcement learning to real-life
problems is that the amount of train-and-error required to learn a good policy
increases drastically as the task becomes complex. One potential solution to
this problem is to combine reinforcement learning with automated symbol
planning and utilize prior knowledge on the domain. However, existing methods
have limitations in their applicability and expressiveness. In this paper we
propose a hierarchical reinforcement learning method based on abductive
symbolic planning. The planner can deal with user-defined evaluation functions
and is not based on the Herbrand theorem. Therefore it can utilize prior
knowledge of the rewards and can work in a domain where the state space is
unknown. We demonstrate empirically that our architecture significantly
improves learning efficiency with respect to the amount of training examples on
the evaluation domain, in which the state space is unknown and there exist
multiple goals.
How To Extract Fashion Trends From Social Media? A Robust Object Detector With Support For Unsupervised Learning
Authors: Vijay Gabale, Anand Prabhu Subramanian
Date: 2018-06-28 00:00:00
Tasks: Object, object-detection, Object Detection

With the proliferation of social media, fashion inspired from celebrities,
reputed designers as well as fashion influencers has shortened the cycle of
fashion design and manufacturing. However, with the explosion of fashion
related content and large number of user generated fashion photos, it is an
arduous task for fashion designers to wade through social media photos and
create a digest of trending fashion. This necessitates deep parsing of fashion
photos on social media to localize and classify multiple fashion items from a
given fashion photo. While object detection competitions such as MSCOCO have
thousands of samples for each of the object categories, it is quite difficult
to get large labeled datasets for fast fashion items. Moreover,
state-of-the-art object detectors do not have any functionality to ingest large
amount of unlabeled data available on social media in order to fine tune object
detectors with labeled datasets. In this work, we show application of a generic
object detector, that can be pretrained in an unsupervised manner, on 24
categories from recently released Open Images V4 dataset. We first train the
base architecture of the object detector using unsupervisd learning on 60K
unlabeled photos from 24 categories gathered from social media, and then
subsequently fine tune it on 8.2K labeled photos from Open Images V4 dataset.
On 300 X 300 image inputs, we achieve 72.7% mAP on a test dataset of 2.4K
photos while performing 11% to 17% better as compared to the state-of-the-art
object detectors. We show that this improvement is due to our choice of
architecture that lets us do unsupervised learning and that performs
significantly better in identifying small objects.
Accurate and efficient video de-fencing using convolutional neural networks and temporal information
Authors: Chen Du, Byeongkeun Kang, Zheng Xu, Ji Dai, Truong Nguyen
Date: 2018-06-28 00:00:00
Tasks: object-detection, Object Detection, Optical Flow Estimation, Segmentation

De-fencing is to eliminate the captured fence on an image or a video,
providing a clear view of the scene. It has been applied for many purposes
including assisting photographers and improving the performance of computer
vision algorithms such as object detection and recognition. However, the
state-of-the-art de-fencing methods have limited performance caused by the
difficulty of fence segmentation and also suffer from the motion of the camera
or objects. To overcome these problems, we propose a novel method consisting of
segmentation using convolutional neural networks and a fast/robust recovery
algorithm. The segmentation algorithm using convolutional neural network
achieves significant improvement in the accuracy of fence segmentation. The
recovery algorithm using optical flow produces plausible de-fenced images and
videos. The proposed method is experimented on both our diverse and complex
dataset and publicly available datasets. The experimental results demonstrate
that the proposed method achieves the state-of-the-art performance for both
segmentation and content recovery.
Differentiable Learning-to-Normalize via Switchable Normalization
Authors: Ping Luo, Jiamin Ren, Zhanglin Peng, Ruimao Zhang, Jingyu Li
Date: 2018-06-28 00:00:00
Proceeding: ICLR 2019 5
Tasks: GPU

We address a learning-to-normalize problem by proposing Switchable
Normalization (SN), which learns to select different normalizers for different
normalization layers of a deep neural network. SN employs three distinct scopes
to compute statistics (means and variances) including a channel, a layer, and a
minibatch. SN switches between them by learning their importance weights in an
end-to-end manner. It has several good properties. First, it adapts to various
network architectures and tasks (see Fig.1). Second, it is robust to a wide
range of batch sizes, maintaining high performance even when small minibatch is
presented (e.g. 2 images/GPU). Third, SN does not have sensitive
hyper-parameter, unlike group normalization that searches the number of groups
as a hyper-parameter. Without bells and whistles, SN outperforms its
counterparts on various challenging benchmarks, such as ImageNet, COCO,
CityScapes, ADE20K, and Kinetics. Analyses of SN are also presented. We hope SN
will help ease the usage and understand the normalization techniques in deep
learning. The code of SN has been made available in
https://github.com/switchablenorms/.
Successive Convex Approximation Algorithms for Sparse Signal Estimation with Nonconvex Regularizations
Authors: Yang Yang, Marius Pesavento, Symeon Chatzinotas, Björn Ottersten
Date: 2018-06-28 00:00:00
Tasks: Anomaly Detection, Clustering

In this paper, we propose a successive convex approximation framework for
sparse optimization where the nonsmooth regularization function in the
objective function is nonconvex and it can be written as the difference of two
convex functions. The proposed framework is based on a nontrivial combination
of the majorization-minimization framework and the successive convex
approximation framework proposed in literature for a convex regularization
function. The proposed framework has several attractive features, namely, i)
flexibility, as different choices of the approximate function lead to different
type of algorithms; ii) fast convergence, as the problem structure can be
better exploited by a proper choice of the approximate function and the
stepsize is calculated by the line search; iii) low complexity, as the
approximate function is convex and the line search scheme is carried out over a
differentiable function; iv) guaranteed convergence to a stationary point. We
demonstrate these features by two example applications in subspace learning,
namely, the network anomaly detection problem and the sparse subspace
clustering problem. Customizing the proposed framework by adopting the
best-response type approximation, we obtain soft-thresholding with exact line
search algorithms for which all elements of the unknown parameter are updated
in parallel according to closed-form expressions. The attractive features of
the proposed algorithms are illustrated numerically.