pwc-archive/papers-with-abstracts huggingface dataset:
Discrete Event, Continuous Time RNNs
Authors: Michael C. Mozer, Denis Kazakov, Robert V. Lindsey
Date: 2017-10-11 00:00:00
Tasks: Inductive Bias, Retrieval

We investigate recurrent neural network architectures for event-sequence
processing. Event sequences, characterized by discrete observations stamped
with continuous-valued times of occurrence, are challenging due to the
potentially wide dynamic range of relevant time scales as well as interactions
between time scales. We describe four forms of inductive bias that should
benefit architectures for event sequences: temporal locality, position and
scale homogeneity, and scale interdependence. We extend the popular gated
recurrent unit (GRU) architecture to incorporate these biases via intrinsic
temporal dynamics, obtaining a continuous-time GRU. The CT-GRU arises by
interpreting the gates of a GRU as selecting a time scale of memory, and the
CT-GRU generalizes the GRU by incorporating multiple time scales of memory and
performing context-dependent selection of time scales for information storage
and retrieval. Event time-stamps drive decay dynamics of the CT-GRU, whereas
they serve as generic additional inputs to the GRU. Despite the very different
manner in which the two models consider time, their performance on eleven data
sets we examined is essentially identical. Our surprising results point both to
the robustness of GRU and LSTM architectures for handling continuous time, and
to the potency of incorporating continuous dynamics into neural architectures.
Wembedder: Wikidata entity embedding web service
Authors: Finn Årup Nielsen
Date: 2017-10-11 00:00:00

I present a web service for querying an embedding of entities in the Wikidata
knowledge graph. The embedding is trained on the Wikidata dump using Gensim's
Word2Vec implementation and a simple graph walk. A REST API is implemented.
Together with the Wikidata API the web service exposes a multilingual resource
for over 600'000 Wikidata items and properties.
Lung Cancer Screening Using Adaptive Memory-Augmented Recurrent Networks
Authors: Aryan Mobiny, Supratik Moulik, Hien Van Nguyen
Date: 2017-10-11 00:00:00
Tasks: Clinical Knowledge, General Classification, Lung Nodule Classification

In this paper, we investigate the effectiveness of deep learning techniques
for lung nodule classification in computed tomography scans. Using less than
10,000 training examples, our deep networks perform two times better than a
standard radiology software. Visualization of the networks' neurons reveals
semantically meaningful features that are consistent with the clinical
knowledge and radiologists' perception. Our paper also proposes a novel
framework for rapidly adapting deep networks to the radiologists' feedback, or
change in the data due to the shift in sensor's resolution or patient
population. The classification accuracy of our approach remains above 80% while
popular deep networks' accuracy is around chance. Finally, we provide in-depth
analysis of our framework by asking a radiologist to examine important
networks' features and perform blind re-labeling of networks' mistakes.
Local Radon Descriptors for Image Search
Authors: Morteza Babaie, H. R. Tizhoosh, Amin Khatami, M. E. Shiri
Date: 2017-10-11 00:00:00
Tasks: Image Retrieval, Medical Image Retrieval, Retrieval

Radon transform and its inverse operation are important techniques in medical
imaging tasks. Recently, there has been renewed interest in Radon transform for
applications such as content-based medical image retrieval. However, all
studies so far have used Radon transform as a global or quasi-global image
descriptor by extracting projections of the whole image or large sub-images.
This paper attempts to show that the dense sampling to generate the histogram
of local Radon projections has a much higher discrimination capability than the
global one. In this paper, we introduce Local Radon Descriptor (LRD) and apply
it to the IRMA dataset, which contains 14,410 x-ray images as well as to the
INRIA Holidays dataset with 1,990 images. Our results show significant
improvement in retrieval performance by using LRD versus its global version. We
also demonstrate that LRD can deliver results comparable to well-established
descriptors like LBP and HOG.
Dimensionality Reduction Ensembles
Authors: Colleen M. Farrelly
Date: 2017-10-11 00:00:00
Tasks: Dimensionality Reduction, Distributed Computing, Ensemble Learning

Ensemble learning has had many successes in supervised learning, but it has
been rare in unsupervised learning and dimensionality reduction. This study
explores dimensionality reduction ensembles, using principal component analysis
and manifold learning techniques to capture linear, nonlinear, local, and
global features in the original dataset. Dimensionality reduction ensembles are
tested first on simulation data and then on two real medical datasets using
random forest classifiers; results suggest the efficacy of this approach, with
accuracies approaching that of the full dataset. Limitations include
computational cost of some algorithms with strong performance, which may be
ameliorated through distributed computing and the development of more efficient
versions of these algorithms.
Stream Graphs and Link Streams for the Modeling of Interactions over Time
Authors: Matthieu Latapy, Tiphaine Viard, Clémence Magnien
Date: 2017-10-11 00:00:00
Tasks: Clustering

Graph theory provides a language for studying the structure of relations, and
it is often used to study interactions over time too. However, it poorly
captures the both temporal and structural nature of interactions, that calls
for a dedicated formalism. In this paper, we generalize graph concepts in order
to cope with both aspects in a consistent way. We start with elementary
concepts like density, clusters, or paths, and derive from them more advanced
concepts like cliques, degrees, clustering coefficients, or connected
components. We obtain a language to directly deal with interactions over time,
similar to the language provided by graphs to deal with relations. This
formalism is self-consistent: usual relations between different concepts are
preserved. It is also consistent with graph theory: graph concepts are special
cases of the ones we introduce. This makes it easy to generalize higher-level
objects such as quotient graphs, line graphs, k-cores, and centralities. This
paper also considers discrete versus continuous time assumptions, instantaneous
links, and extensions to more complex cases.
Decentralized Online Learning with Kernels
Authors: Alec Koppel, Santiago Paternain, Cedric Richard, Alejandro Ribeiro
Date: 2017-10-11 00:00:00
Tasks: General Classification, Multi-class Classification, regression, Stochastic Optimization

We consider multi-agent stochastic optimization problems over reproducing
kernel Hilbert spaces (RKHS). In this setting, a network of interconnected
agents aims to learn decision functions, i.e., nonlinear statistical models,
that are optimal in terms of a global convex functional that aggregates data
across the network, with only access to locally and sequentially observed
samples. We propose solving this problem by allowing each agent to learn a
local regression function while enforcing consensus constraints. We use a
penalized variant of functional stochastic gradient descent operating
simultaneously with low-dimensional subspace projections. These subspaces are
constructed greedily by applying orthogonal matching pursuit to the sequence of
kernel dictionaries and weights. By tuning the projection-induced bias, we
propose an algorithm that allows for each individual agent to learn, based upon
its locally observed data stream and message passing with its neighbors only, a
regression function that is close to the globally optimal regression function.
That is, we establish that with constant step-size selections agents' functions
converge to a neighborhood of the globally optimal one while satisfying the
consensus constraints as the penalty parameter is increased. Moreover, the
complexity of the learned regression functions is guaranteed to remain finite.
On both multi-class kernel logistic regression and multi-class kernel support
vector classification with data generated from class-dependent Gaussian mixture
models, we observe stable function estimation and state of the art performance
for distributed online multi-class classification. Experiments on the Brodatz
textures further substantiate the empirical validity of this approach.
Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning
Authors: Guotai Wang, Wenqi Li, Maria A. Zuluaga, Rosalind Pratt, Premal A. Patel, Michael Aertsen, Tom Doel, Anna L. David, Jan Deprest, Sebastien Ourselin, Tom Vercauteren
Date: 2017-10-11 00:00:00
Tasks: Image Segmentation, Interactive Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

Convolutional neural networks (CNNs) have achieved state-of-the-art
performance for automatic medical image segmentation. However, they have not
demonstrated sufficiently accurate and robust results for clinical use. In
addition, they are limited by the lack of image-specific adaptation and the
lack of generalizability to previously unseen object classes. To address these
problems, we propose a novel deep learning-based framework for interactive
segmentation by incorporating CNNs into a bounding box and scribble-based
segmentation pipeline. We propose image-specific fine-tuning to make a CNN
model adaptive to a specific test image, which can be either unsupervised
(without additional user interactions) or supervised (with additional
scribbles). We also propose a weighted loss function considering network and
interaction-based uncertainty for the fine-tuning. We applied this framework to
two applications: 2D segmentation of multiple organs from fetal MR slices,
where only two types of these organs were annotated for training; and 3D
segmentation of brain tumor core (excluding edema) and whole brain tumor
(including edema) from different MR sequences, where only tumor cores in one MR
sequence were annotated for training. Experimental results show that 1) our
model is more robust to segment previously unseen objects than state-of-the-art
CNNs; 2) image-specific fine-tuning with the proposed weighted loss function
significantly improves segmentation accuracy; and 3) our method leads to
accurate results with fewer user interactions and less user time than
traditional interactive segmentation methods.
Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Image
Authors: Denis Tome, Chris Russell, Lourdes Agapito
Date: 2017-01-01 00:00:00
Proceeding: CVPR 2017 7
Tasks: 3D Human Pose Estimation, 3D Pose Estimation, Monocular 3D Human Pose Estimation, Pose Estimation, Weakly-supervised 3D Human Pose Estimation

We propose a unified formulation for the problem of 3D human pose estimation
from a single raw RGB image that reasons jointly about 2D joint estimation and
3D pose reconstruction to improve both tasks. We take an integrated approach
that fuses probabilistic knowledge of 3D human pose with a multi-stage CNN
architecture and uses the knowledge of plausible 3D landmark locations to
refine the search for better 2D locations. The entire process is trained
end-to-end, is extremely efficient and obtains state- of-the-art results on
Human3.6M outperforming previous approaches both on 2D and 3D errors.
Porcellio scaber algorithm (PSA) for solving constrained optimization problems
Authors: Yinyan Zhang, Shuai Li, Hongliang Guo
Date: 2017-10-11 00:00:00

In this paper, we extend a bio-inspired algorithm called the porcellio scaber
algorithm (PSA) to solve constrained optimization problems, including a
constrained mixed discrete-continuous nonlinear optimization problem. Our
extensive experiment results based on benchmark optimization problems show that
the PSA has a better performance than many existing methods or algorithms. The
results indicate that the PSA is a promising algorithm for constrained
optimization.
Image retargeting via Beltrami representation
Authors: Chun Pong Lau, Chun Pang Yung, Lok Ming Lui
Date: 2017-10-11 00:00:00
Tasks: Image Retargeting

Image retargeting aims to resize an image to one with a prescribed aspect
ratio. Simple scaling inevitably introduces unnatural geometric distortions on
the important content of the image. In this paper, we propose a simple and yet
effective method to resize an image, which preserves the geometry of the
important content, using the Beltrami representation. Our algorithm allows
users to interactively label content regions as well as line structures. Image
resizing can then be achieved by warping the image by an orientation-preserving
bijective warping map with controlled distortion. The warping map is
represented by its Beltrami representation, which captures the local geometric
distortion of the map. By carefully prescribing the values of the Beltrami
representation, images with different complexity can be effectively resized.
Our method does not require solving any optimization problems and tuning
parameters throughout the process. This results in a simple and efficient
algorithm to solve the image retargeting problem. Extensive experiments have
been carried out, which demonstrate the efficacy of our proposed method.
An introduction to Topological Data Analysis: fundamental and practical aspects for data scientists
Authors: Frédéric Chazal, Bertrand Michel
Date: 2017-10-11 00:00:00
Tasks: Topological Data Analysis

Topological Data Analysis is a recent and fast growing field providing a set of new topological and geometric tools to infer relevant features for possibly complex data. This paper is a brief introduction, through a few selected topics, to basic fundamental and practical aspects of \tda\ for non experts.
A Review of Convolutional Neural Networks for Inverse Problems in Imaging
Authors: Michael T. McCann, Kyong Hwan Jin, Michael Unser
Date: 2017-10-11 00:00:00
Tasks: compressed sensing, Denoising, Image Reconstruction, Super-Resolution

In this survey paper, we review recent uses of convolution neural networks
(CNNs) to solve inverse problems in imaging. It has recently become feasible to
train deep CNNs on large databases of images, and they have shown outstanding
performance on object classification and segmentation tasks. Motivated by these
successes, researchers have begun to apply CNNs to the resolution of inverse
problems such as denoising, deconvolution, super-resolution, and medical image
reconstruction, and they have started to report improvements over
state-of-the-art methods, including sparsity-based techniques such as
compressed sensing. Here, we review the recent experimental work in these
areas, with a focus on the critical design decisions: Where does the training
data come from? What is the architecture of the CNN? and How is the learning
problem formulated and solved? We also bring together a few key theoretical
papers that offer perspective on why CNNs are appropriate for inverse problems
and point to some next steps in the field.
A Dynamic Edge Exchangeable Model for Sparse Temporal Networks
Authors: Yin Cheng Ng, Ricardo Silva
Date: 2017-10-11 00:00:00
Tasks: Link Prediction, Variational Inference

We propose a dynamic edge exchangeable network model that can capture sparse
connections observed in real temporal networks, in contrast to existing models
which are dense. The model achieved superior link prediction accuracy on
multiple data sets when compared to a dynamic variant of the blockmodel, and is
able to extract interpretable time-varying community structures from the data.
In addition to sparsity, the model accounts for the effect of social influence
on vertices' future behaviours. Compared to the dynamic blockmodels, our model
has a smaller latent space. The compact latent space requires a smaller number
of parameters to be estimated in variational inference and results in a
computationally friendly inference algorithm.
A batching and scheduling optimisation for a cutting work-center: Acta-Mobilier case study
Authors: Emmanuel Zimmermann, Hind Haouzi, Philippe Thomas, André Thomas, Melanie Noyel
Date: 2017-10-11 00:00:00
Tasks: Management, Scheduling

The purpose of this study is to investigate an approach to group lots in
batches and to schedule these batches on Acta-Mobilier cutting work-center
while taking into account numerous constraints and objectives. The specific
batching method was proposed to handle the Acta-Mobilier problem and a
mathematical formalisation and genetic algorithm were proposed to deal with the
scheduling problem. The proposed algorithm has been embedded in software to
optimise production costs and emphasis the visual management on the production
line. The application is currently being used in Acta-Mobilier plant and shows
significant results
Exploring Cross-Domain Data Dependencies for Smart Homes to Improve Energy Efficiency
Authors: Shamaila Iram, Terrence Fernando, May Bassanino
Date: 2017-10-11 00:00:00

Over the past decade, the idea of smart homes has been conceived as a
potential solution to counter energy crises or to at least mitigate its
intensive destructive consequences in the residential building sector.
Adaptive multi-penalty regularization based on a generalized Lasso path
Authors: Markus Grasmair, Timo Klock, Valeriya Naumova
Date: 2017-10-11 00:00:00
Tasks: compressed sensing, Model Selection

For many algorithms, parameter tuning remains a challenging and critical
task, which becomes tedious and infeasible in a multi-parameter setting.
Multi-penalty regularization, successfully used for solving undetermined sparse
regression of problems of unmixing type where signal and noise are additively
mixed, is one of such examples. In this paper, we propose a novel algorithmic
framework for an adaptive parameter choice in multi-penalty regularization with
a focus on the correct support recovery. Building upon the theory of
regularization paths and algorithms for single-penalty functionals, we extend
these ideas to a multi-penalty framework by providing an efficient procedure
for the construction of regions containing structurally similar solutions,
i.e., solutions with the same sparsity and sign pattern, over the whole range
of parameters. Combining this with a model selection criterion, we can choose
regularization parameters in a data-adaptive manner. Another advantage of our
algorithm is that it provides an overview on the solution stability over the
whole range of parameters. This can be further exploited to obtain additional
insights into the problem of interest. We provide a numerical analysis of our
method and compare it to the state-of-the-art single-penalty algorithms for
compressed sensing problems in order to demonstrate the robustness and power of
the proposed algorithm.
Algebraic Image Processing
Authors: Enrico Celeghini
Date: 2017-10-11 00:00:00

We propose an approach to image processing related to algebraic operators
acting in the space of images. In view of the interest in the applications in
optics and computer science, mathematical aspects of the paper have been
simplified as much as possible. Underlying theory, related to rigged Hilbert
spaces and Lie algebras, is discussed elsewhere
Deep learning in remote sensing: a review
Authors: Xiao Xiang Zhu, Devis Tuia, Lichao Mou, Gui-Song Xia, Liangpei Zhang, Feng Xu, Friedrich Fraundorfer
Date: 2017-10-11 00:00:00
Tasks: Deep Learning

Standing at the paradigm shift towards data-intensive science, machine
learning techniques are becoming increasingly important. In particular, as a
major breakthrough in the field, deep learning has proven as an extremely
powerful tool in many fields. Shall we embrace deep learning as the key to all?
Or, should we resist a 'black-box' solution? There are controversial opinions
in the remote sensing community. In this article, we analyze the challenges of
using deep learning for remote sensing data analysis, review the recent
advances, and provide resources to make deep learning in remote sensing
ridiculously simple to start with. More importantly, we advocate remote sensing
scientists to bring their expertise into deep learning, and use it as an
implicit general model to tackle unprecedented large-scale influential
challenges, such as climate change and urbanization.
DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset
Authors: Yan-ran Li, Hui Su, Xiaoyu Shen, Wenjie Li, Ziqiang Cao, Shuzi Niu
Date: 2017-10-11 00:00:00
Proceeding: IJCNLP 2017 11

We develop a high-quality multi-turn dialog dataset, DailyDialog, which is
intriguing in several aspects. The language is human-written and less noisy.
The dialogues in the dataset reflect our daily communication way and cover
various topics about our daily life. We also manually label the developed
dataset with communication intention and emotion information. Then, we evaluate
existing approaches on DailyDialog dataset and hope it benefit the research
field of dialog systems.
Decision support from financial disclosures with deep neural networks and transfer learning
Authors: Mathias Kraus, Stefan Feuerriegel
Date: 2017-10-11 00:00:00
Tasks: Decision Making, Deep Learning, Transfer Learning

Company disclosures greatly aid in the process of financial decision-making;
therefore, they are consulted by financial investors and automated traders
before exercising ownership in stocks. While humans are usually able to
correctly interpret the content, the same is rarely true of computerized
decision support systems, which struggle with the complexity and ambiguity of
natural language. A possible remedy is represented by deep learning, which
overcomes several shortcomings of traditional methods of text mining. For
instance, recurrent neural networks, such as long short-term memories, employ
hierarchical structures, together with a large number of hidden layers, to
automatically extract features from ordered sequences of words and capture
highly non-linear relationships such as context-dependent meanings. However,
deep learning has only recently started to receive traction, possibly because
its performance is largely untested. Hence, this paper studies the use of deep
neural networks for financial decision support. We additionally experiment with
transfer learning, in which we pre-train the network on a different corpus with
a length of 139.1 million words. Our results reveal a higher directional
accuracy as compared to traditional machine learning when predicting stock
price movements in response to financial disclosures. Our work thereby helps to
highlight the business value of deep learning and provides recommendations to
practitioners and executives.