pwc-archive/papers-with-abstracts huggingface dataset:
Detection of Tooth caries in Bitewing Radiographs using Deep Learning
Authors: Muktabh Mayank Srivastava, Pratyush Kumar, Lalit Pradhan, Srikrishna Varadarajan
Date: 2017-11-20 00:00:00
Tasks: Deep Learning, Sensitivity

We develop a Computer Aided Diagnosis (CAD) system, which enhances the
performance of dentists in detecting wide range of dental caries. The CAD
System achieves this by acting as a second opinion for the dentists with way
higher sensitivity on the task of detecting cavities than the dentists
themselves. We develop annotated dataset of more than 3000 bitewing radiographs
and utilize it for developing a system for automated diagnosis of dental
caries. Our system consists of a deep fully convolutional neural network (FCNN)
consisting 100+ layers, which is trained to mark caries on bitewing
radiographs. We have compared the performance of our proposed system with three
certified dentists for marking dental caries. We exceed the average performance
of the dentists in both recall (sensitivity) and F1-Score (agreement with
truth) by a very large margin. Working example of our system is shown in Figure
1.
Estimating Missing Data in Temporal Data Streams Using Multi-directional Recurrent Neural Networks
Authors: Jinsung Yoon, William R. Zame, Mihaela van der Schaar
Date: 2017-11-23 00:00:00
Tasks: Matrix Completion, Multivariate Time Series Imputation, Prognosis

Missing data is a ubiquitous problem. It is especially challenging in medical
settings because many streams of measurements are collected at different - and
often irregular - times. Accurate estimation of those missing measurements is
critical for many reasons, including diagnosis, prognosis and treatment.
Existing methods address this estimation problem by interpolating within data
streams or imputing across data streams (both of which ignore important
information) or ignoring the temporal aspect of the data and imposing strong
assumptions about the nature of the data-generating process and/or the pattern
of missing data (both of which are especially problematic for medical data). We
propose a new approach, based on a novel deep learning architecture that we
call a Multi-directional Recurrent Neural Network (M-RNN) that interpolates
within data streams and imputes across data streams. We demonstrate the power
of our approach by applying it to five real-world medical datasets. We show
that it provides dramatically improved estimation of missing measurements in
comparison to 11 state-of-the-art benchmarks (including Spline and Cubic
Interpolations, MICE, MissForest, matrix completion and several RNN methods);
typical improvements in Root Mean Square Error are between 35% - 50%.
Additional experiments based on the same five datasets demonstrate that the
improvements provided by our method are extremely robust.
fpgaConvNet: A Toolflow for Mapping Diverse Convolutional Neural Networks on Embedded FPGAs
Authors: Stylianos I. Venieris, Christos-Savvas Bouganis
Date: 2017-11-23 00:00:00
Tasks: GPU

In recent years, Convolutional Neural Networks (ConvNets) have become an
enabling technology for a wide range of novel embedded Artificial Intelligence
systems. Across the range of applications, the performance needs vary
significantly, from high-throughput video surveillance to the very low-latency
requirements of autonomous cars. In this context, FPGAs can provide a potential
platform that can be optimally configured based on the different performance
needs. However, the complexity of ConvNet models keeps increasing making their
mapping to an FPGA device a challenging task. This work presents fpgaConvNet,
an end-to-end framework for mapping ConvNets on FPGAs. The proposed framework
employs an automated design methodology based on the Synchronous Dataflow (SDF)
paradigm and defines a set of SDF transformations in order to efficiently
explore the architectural design space. By selectively optimising for
throughput, latency or multiobjective criteria, the presented tool is able to
efficiently explore the design space and generate hardware designs from
high-level ConvNet specifications, explicitly optimised for the performance
metric of interest. Overall, our framework yields designs that improve the
performance by up to 6.65x over highly optimised embedded GPU designs for the
same power constraints in embedded environments.
Long-Term Image Boundary Prediction
Authors: Apratim Bhattacharyya, Mateusz Malinowski, Bernt Schiele, Mario Fritz
Date: 2016-11-27 00:00:00
Tasks: Prediction

Boundary estimation in images and videos has been a very active topic of
research, and organizing visual information into boundaries and segments is
believed to be a corner stone of visual perception. While prior work has
focused on estimating boundaries for observed frames, our work aims at
predicting boundaries of future unobserved frames. This requires our model to
learn about the fate of boundaries and corresponding motion patterns --
including a notion of "intuitive physics". We experiment on natural video
sequences along with synthetic sequences with deterministic physics-based and
agent-based motions. While not being our primary goal, we also show that fusion
of RGB and boundary prediction leads to improved RGB predictions.
Open Evaluation Tool for Layout Analysis of Document Images
Authors: Michele Alberti, Manuel Bouillon, Rolf Ingold, Marcus Liwicki
Date: 2017-11-23 00:00:00

This paper presents an open tool for standardizing the evaluation process of
the layout analysis task of document images at pixel level. We introduce a new
evaluation tool that is both available as a standalone Java application and as
a RESTful web service. This evaluation tool is free and open-source in order to
be a common tool that anyone can use and contribute to. It aims at providing as
many metrics as possible to investigate layout analysis predictions, and also
provide an easy way of visualizing the results. This tool evaluates document
segmentation at pixel level, and support multi-labeled pixel ground truth.
Finally, this tool has been successfully used for the ICDAR2017 competition on
Layout Analysis for Challenging Medieval Manuscripts.
Modelling Domain Relationships for Transfer Learning on Retrieval-based Question Answering Systems in E-commerce
Authors: Jianfei Yu, Minghui Qiu, Jing Jiang, Jun Huang, Shuangyong Song, Wei Chu, Haiqing Chen
Date: 2017-11-23 00:00:00
Tasks: Chatbot, Natural Language Inference, Paraphrase Identification, Question Answering, Retrieval, Sentence, Transfer Learning

In this paper, we study transfer learning for the PI and NLI problems, aiming
to propose a general framework, which can effectively and efficiently adapt the
shared knowledge learned from a resource-rich source domain to a resource- poor
target domain. Specifically, since most existing transfer learning methods only
focus on learning a shared feature space across domains while ignoring the
relationship between the source and target domains, we propose to
simultaneously learn shared representations and domain relationships in a
unified framework. Furthermore, we propose an efficient and effective hybrid
model by combining a sentence encoding- based method and a sentence
interaction-based method as our base model. Extensive experiments on both
paraphrase identification and natural language inference demonstrate that our
base model is efficient and has promising performance compared to the competing
models, and our transfer learning method can help to significantly boost the
performance. Further analysis shows that the inter-domain and intra-domain
relationship captured by our model are insightful. Last but not least, we
deploy our transfer learning model for PI into our online chatbot system, which
can bring in significant improvements over our existing system. Finally, we
launch our new system on the chatbot platform Eva in our E-commerce site
AliExpress.
Parallel transport in shape analysis: a scalable numerical scheme
Authors: Maxime Louis, Alexandre Bône, Benjamin Charlier, Stanley Durrleman
Date: 2017-11-23 00:00:00

The analysis of manifold-valued data requires efficient tools from Riemannian
geometry to cope with the computational complexity at stake. This complexity
arises from the always-increasing dimension of the data, and the absence of
closed-form expressions to basic operations such as the Riemannian logarithm.
In this paper, we adapt a generic numerical scheme recently introduced for
computing parallel transport along geodesics in a Riemannian manifold to
finite-dimensional manifolds of diffeomorphisms. We provide a qualitative and
quantitative analysis of its behavior on high-dimensional manifolds, and
investigate an application with the prediction of brain structures progression.
Prediction of the progression of subcortical brain structures in Alzheimer's disease from baseline
Authors: Alexandre Bône, Maxime Louis, Alexandre Routier, Jorge Samper, Michael Bacci, Benjamin Charlier, Olivier Colliot, Stanley Durrleman
Date: 2017-11-23 00:00:00

We propose a method to predict the subject-specific longitudinal progression
of brain structures extracted from baseline MRI, and evaluate its performance
on Alzheimer's disease data. The disease progression is modeled as a trajectory
on a group of diffeomorphisms in the context of large deformation diffeomorphic
metric mapping (LDDMM). We first exhibit the limited predictive abilities of
geodesic regression extrapolation on this group. Building on the recent concept
of parallel curves in shape manifolds, we then introduce a second predictive
protocol which personalizes previously learned trajectories to new subjects,
and investigate the relative performances of two parallel shifting paradigms.
This design only requires the baseline imaging data. Finally, coefficients
encoding the disease dynamics are obtained from longitudinal cognitive
measurements for each subject, and exploited to refine our methodology which is
demonstrated to successfully predict the follow-up visits.
Transfer learning for multi-center classification of chronic obstructive pulmonary disease
Authors: Veronika Cheplygina, Isabel Pino Peña, Jesper Holst Pedersen, David A. Lynch, Lauge Sørensen, Marleen de Bruijne
Date: 2017-01-18 00:00:00
Tasks: Classification, Computed Tomography (CT), General Classification, Transfer Learning, Weakly-supervised Learning

Chronic obstructive pulmonary disease (COPD) is a lung disease which can be
quantified using chest computed tomography (CT) scans. Recent studies have
shown that COPD can be automatically diagnosed using weakly supervised learning
of intensity and texture distributions. However, up till now such classifiers
have only been evaluated on scans from a single domain, and it is unclear
whether they would generalize across domains, such as different scanners or
scanning protocols. To address this problem, we investigate classification of
COPD in a multi-center dataset with a total of 803 scans from three different
centers, four different scanners, with heterogenous subject distributions. Our
method is based on Gaussian texture features, and a weighted logistic
classifier, which increases the weights of samples similar to the test data. We
show that Gaussian texture features outperform intensity features previously
used in multi-center classification tasks. We also show that a weighting
strategy based on a classifier that is trained to discriminate between scans
from different domains, can further improve the results. To encourage further
research into transfer learning methods for classification of COPD, upon
acceptance of the paper we will release two feature datasets used in this study
on http://bigr.nl/research/projects/copd
Attended End-to-end Architecture for Age Estimation from Facial Expression Videos
Authors: Wenjie Pei, Hamdi Dibeklioğlu, Tadas Baltrušaitis, David M. J. Tax
Date: 2017-11-23 00:00:00
Tasks: Age Estimation

The main challenges of age estimation from facial expression videos lie not only in the modeling of the static facial appearance, but also in the capturing of the temporal facial dynamics. Traditional techniques to this problem focus on constructing handcrafted features to explore the discriminative information contained in facial appearance and dynamics separately. This relies on sophisticated feature-refinement and framework-design. In this paper, we present an end-to-end architecture for age estimation, called Spatially-Indexed Attention Model (SIAM), which is able to simultaneously learn both the appearance and dynamics of age from raw videos of facial expressions. Specifically, we employ convolutional neural networks to extract effective latent appearance representations and feed them into recurrent networks to model the temporal dynamics. More importantly, we propose to leverage attention models for salience detection in both the spatial domain for each single image and the temporal domain for the whole video as well. We design a specific spatially-indexed attention mechanism among the convolutional layers to extract the salient facial regions in each individual image, and a temporal attention layer to assign attention weights to each frame. This two-pronged approach not only improves the performance by allowing the model to focus on informative frames and facial areas, but it also offers an interpretable correspondence between the spatial facial regions as well as temporal frames, and the task of age estimation. We demonstrate the strong performance of our model in experiments on a large, gender-balanced database with 400 subjects with ages spanning from 8 to 76 years. Experiments reveal that our model exhibits significant superiority over the state-of-the-art methods given sufficient training data.
Beyond RGB: Very High Resolution Urban Remote Sensing With Multimodal Deep Networks
Authors: Nicolas Audebert, Bertrand Le Saux, Sébastien Lefèvre
Date: 2017-11-23 00:00:00
Tasks: Semantic Segmentation

In this work, we investigate various methods to deal with semantic labeling
of very high resolution multi-modal remote sensing data. Especially, we study
how deep fully convolutional networks can be adapted to deal with multi-modal
and multi-scale remote sensing data for semantic labeling. Our contributions
are threefold: a) we present an efficient multi-scale approach to leverage both
a large spatial context and the high resolution data, b) we investigate early
and late fusion of Lidar and multispectral data, c) we validate our methods on
two public datasets with state-of-the-art results. Our results indicate that
late fusion make it possible to recover errors steaming from ambiguous data,
while early fusion allows for better joint-feature learning but at the cost of
higher sensitivity to missing data.
Markov chain Hebbian learning algorithm with ternary synaptic units
Authors: Guhyun Kim, Vladimir Kornijcuk, Dohun Kim, Inho Kim, Jaewook Kim, Hyo Cheon Woo, Ji Hun Kim, Cheol Seong Hwang, Doo Seok Jeong
Date: 2017-11-23 00:00:00
Tasks: BIG-bench Machine Learning, Handwritten Digit Recognition, Memorization

In spite of remarkable progress in machine learning techniques, the
state-of-the-art machine learning algorithms often keep machines from real-time
learning (online learning) due in part to computational complexity in parameter
optimization. As an alternative, a learning algorithm to train a memory in real
time is proposed, which is named as the Markov chain Hebbian learning
algorithm. The algorithm pursues efficient memory use during training in that
(i) the weight matrix has ternary elements (-1, 0, 1) and (ii) each update
follows a Markov chain--the upcoming update does not need past weight memory.
The algorithm was verified by two proof-of-concept tasks (handwritten digit
recognition and multiplication table memorization) in which numbers were taken
as symbols. Particularly, the latter bases multiplication arithmetic on memory,
which may be analogous to humans' mental arithmetic. The memory-based
multiplication arithmetic feasibly offers the basis of factorization,
supporting novel insight into the arithmetic.
Identification of Strong Edges in AMP Chain Graphs
Authors: Jose M. Peña
Date: 2017-11-23 00:00:00

The essential graph is a distinguished member of a Markov equivalence class
of AMP chain graphs. However, the directed edges in the essential graph are not
necessarily strong or invariant, i.e. they may not be shared by every member of
the equivalence class. Likewise for the undirected edges. In this paper, we
develop a procedure for identifying which edges in an essential graph are
strong. We also show how this makes it possible to bound some causal effects
when the true chain graph is unknown.
Bias-Compensated Normalized Maximum Correntropy Criterion Algorithm for System Identification with Noisy Input
Authors: Wentao Ma, Dongqiao Zheng, Yuanhao Li, ZhiYu Zhang, Badong Chen
Date: 2017-11-23 00:00:00

This paper proposed a bias-compensated normalized maximum correntropy
criterion (BCNMCC) algorithm charactered by its low steady-state misalignment
for system identification with noisy input in an impulsive output noise
environment. The normalized maximum correntropy criterion (NMCC) is derived
from a correntropy based cost function, which is rather robust with respect to
impulsive noises. To deal with the noisy input, we introduce a bias-compensated
vector (BCV) to the NMCC algorithm, and then an unbiasedness criterion and some
reasonable assumptions are used to compute the BCV. Taking advantage of the
BCV, the bias caused by the input noise can be effectively suppressed. System
identification simulation results demonstrate that the proposed BCNMCC
algorithm can outperform other related algorithms with noisy input especially
in an impulsive output noise environment.
Global optimization for low-dimensional switching linear regression and bounded-error estimation
Authors: Fabien Lauer
Date: 2017-07-18 00:00:00
Tasks: global-optimization, regression, valid

The paper provides global optimization algorithms for two particularly
difficult nonconvex problems raised by hybrid system identification: switching
linear regression and bounded-error estimation. While most works focus on local
optimization heuristics without global optimality guarantees or with guarantees
valid only under restrictive conditions, the proposed approach always yields a
solution with a certificate of global optimality. This approach relies on a
branch-and-bound strategy for which we devise lower bounds that can be
efficiently computed. In order to obtain scalable algorithms with respect to
the number of data, we directly optimize the model parameters in a continuous
optimization setting without involving integer variables. Numerical experiments
show that the proposed algorithms offer a higher accuracy than convex
relaxations with a reasonable computational burden for hybrid system
identification. In addition, we discuss how bounded-error estimation is related
to robust estimation in the presence of outliers and exact recovery under
sparse noise, for which we also obtain promising numerical results.
Robustness of Maximum Correntropy Estimation Against Large Outliers
Authors: Badong Chen, Lei Xing, Haiquan Zhao, Bin Xu, Jose C. Principe
Date: 2017-03-23 00:00:00
Tasks: parameter estimation

The maximum correntropy criterion (MCC) has recently been successfully
applied in robust regression, classification and adaptive filtering, where the
correntropy is maximized instead of minimizing the well-known mean square error
(MSE) to improve the robustness with respect to outliers (or impulsive noises).
Considerable efforts have been devoted to develop various robust adaptive
algorithms under MCC, but so far little insight has been gained as to how the
optimal solution will be affected by outliers. In this work, we study this
problem in the context of parameter estimation for a simple linear
errors-in-variables (EIV) model where all variables are scalar. Under certain
conditions, we derive an upper bound on the absolute value of the estimation
error and show that the optimal solution under MCC can be very close to the
true value of the unknown parameter even with outliers (whose values can be
arbitrarily large) in both input and output variables. Illustrative examples
are presented to verify and clarify the theory.
Self-view Grounding Given a Narrated 360° Video
Authors: Shih-Han Chou, Yi-Chun Chen, Kuo-Hao Zeng, Hou-Ning Hu, Jianlong Fu, Min Sun
Date: 2017-11-23 00:00:00
Tasks: Sentence, Visual Grounding

Narrated 360{\deg} videos are typically provided in many touring scenarios to
mimic real-world experience. However, previous work has shown that smart
assistance (i.e., providing visual guidance) can significantly help users to
follow the Normal Field of View (NFoV) corresponding to the narrative. In this
project, we aim at automatically grounding the NFoVs of a 360{\deg} video given
subtitles of the narrative (referred to as "NFoV-grounding"). We propose a
novel Visual Grounding Model (VGM) to implicitly and efficiently predict the
NFoVs given the video content and subtitles. Specifically, at each frame, we
efficiently encode the panorama into feature map of candidate NFoVs using a
Convolutional Neural Network (CNN) and the subtitles to the same hidden space
using an RNN with Gated Recurrent Units (GRU). Then, we apply soft-attention on
candidate NFoVs to trigger sentence decoder aiming to minimize the reconstruct
loss between the generated and given sentence. Finally, we obtain the NFoV as
the candidate NFoV with the maximum attention without any human supervision. To
train VGM more robustly, we also generate a reverse sentence conditioning on
one minus the soft-attention such that the attention focuses on candidate NFoVs
less relevant to the given sentence. The negative log reconstruction loss of
the reverse sentence (referred to as "irrelevant loss") is jointly minimized to
encourage the reverse sentence to be different from the given sentence. To
evaluate our method, we collect the first narrated 360{\deg} videos dataset and
achieve state-of-the-art NFoV-grounding performance.
Kafnets: kernel-based non-parametric activation functions for neural networks
Authors: Simone Scardapane, Steven Van Vaerenbergh, Simone Totaro, Aurelio Uncini
Date: 2017-07-13 00:00:00

Neural networks are generally built by interleaving (adaptable) linear layers
with (fixed) nonlinear activation functions. To increase their flexibility,
several authors have proposed methods for adapting the activation functions
themselves, endowing them with varying degrees of flexibility. None of these
approaches, however, have gained wide acceptance in practice, and research in
this topic remains open. In this paper, we introduce a novel family of flexible
activation functions that are based on an inexpensive kernel expansion at every
neuron. Leveraging over several properties of kernel-based models, we propose
multiple variations for designing and initializing these kernel activation
functions (KAFs), including a multidimensional scheme allowing to nonlinearly
combine information from different paths in the network. The resulting KAFs can
approximate any mapping defined over a subset of the real line, either convex
or nonconvex. Furthermore, they are smooth over their entire domain, linear in
their parameters, and they can be regularized using any known scheme, including
the use of $\ell_1$ penalties to enforce sparseness. To the best of our
knowledge, no other known model satisfies all these properties simultaneously.
In addition, we provide a relatively complete overview on alternative
techniques for adapting the activation functions, which is currently lacking in
the literature. A large set of experiments validates our proposal.