pwc-archive/papers-with-abstracts huggingface dataset:
ConvSRC: SmartPhone based Periocular Recognition using Deep Convolutional Neural Network and Sparsity Augmented Collaborative Representation
Authors: Amani Alahmadi, Muhammad Hussain, Hatim AboAlSamh, Mansour Zuair
Date: 2018-01-16 00:00:00

Smartphone based periocular recognition has gained significant attention from
biometric research community because of the limitations of biometric modalities
like face, iris etc. Most of the existing methods for periocular recognition
employ hand-crafted features. Recently, learning based image representation
techniques like deep Convolutional Neural Network (CNN) have shown outstanding
performance in many visual recognition tasks. CNN needs a huge volume of data
for its learning, but for periocular recognition only limited amount of data is
available. The solution is to use CNN pre-trained on the dataset from the
related domain, in this case the challenge is to extract efficiently the
discriminative features. Using a pertained CNN model (VGG-Net), we propose a
simple, efficient and compact image representation technique that takes into
account the wealth of information and sparsity existing in the activations of
the convolutional layers and employs principle component analysis. For
recognition, we use an efficient and robust Sparse Augmented Collaborative
Representation based Classification (SA-CRC) technique. For thorough evaluation
of ConvSRC (the proposed system), experiments were carried out on the VISOB
challenging database which was presented for periocular recognition competition
in ICIP2016. The obtained results show the superiority of ConvSRC over the
state-of-the-art methods; it obtains a GMR of more than 99% at FMR = 10-3 and
outperforms the first winner of ICIP2016 challenge by 10%.
An Automated System for Epilepsy Detection using EEG Brain Signals based on Deep Learning Approach
Authors: Ihsan Ullah, Muhammad Hussain, Emad-ul-Haq Qazi, Hatim AboAlSamh
Date: 2018-01-16 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Epilepsy is a neurological disorder and for its detection, encephalography
(EEG) is a commonly used clinical approach. Manual inspection of EEG brain
signals is a time-consuming and laborious process, which puts heavy burden on
neurologists and affects their performance. Several automatic techniques have
been proposed using traditional approaches to assist neurologists in detecting
binary epilepsy scenarios e.g. seizure vs. non-seizure or normal vs. ictal.
These methods do not perform well when classifying ternary case e.g. ictal vs.
normal vs. inter-ictal; the maximum accuracy for this case by the
state-of-the-art-methods is 97+-1%. To overcome this problem, we propose a
system based on deep learning, which is an ensemble of pyramidal
one-dimensional convolutional neural network (P-1D-CNN) models. In a CNN model,
the bottleneck is the large number of learnable parameters. P-1D-CNN works on
the concept of refinement approach and it results in 60% fewer parameters
compared to traditional CNN models. Further to overcome the limitations of
small amount of data, we proposed augmentation schemes for learning P-1D-CNN
model. In almost all the cases concerning epilepsy detection, the proposed
system gives an accuracy of 99.1+-0.9% on the University of Bonn dataset.
Deep Canonically Correlated LSTMs
Authors: Neil Mallinar, Corbin Rosset
Date: 2018-01-16 00:00:00
Tasks: Time Series, Time Series Analysis

We examine Deep Canonically Correlated LSTMs as a way to learn nonlinear
transformations of variable length sequences and embed them into a correlated,
fixed dimensional space. We use LSTMs to transform multi-view time-series data
non-linearly while learning temporal relationships within the data. We then
perform correlation analysis on the outputs of these neural networks to find a
correlated subspace through which we get our final representation via
projection. This work follows from previous work done on Deep Canonical
Correlation (DCCA), in which deep feed-forward neural networks were used to
learn nonlinear transformations of data while maximizing correlation.
Deep learning bank distress from news and numerical financial data
Authors: Paola Cerchiello, Giancarlo Nicola, Samuel Ronnqvist, Peter Sarlin
Date: 2017-06-29 00:00:00
Tasks: Deep Learning

In this paper we focus our attention on the exploitation of the information
contained in financial news to enhance the performance of a classifier of bank
distress. Such information should be analyzed and inserted into the predictive
model in the most efficient way and this task deals with all the issues related
to text analysis and specifically analysis of news media. Among the different
models proposed for such purpose, we investigate one of the possible deep
learning approaches, based on a doc2vec representation of the textual data, a
kind of neural network able to map the sequential and symbolic text input onto
a reduced latent semantic space. Afterwards, a second supervised neural network
is trained combining news data with standard financial figures to classify
banks whether in distressed or tranquil states, based on a small set of known
distress events. Then the final aim is not only the improvement of the
predictive performance of the classifier but also to assess the importance of
news data in the classification process. Does news data really bring more
useful information not contained in standard financial variables? Our results
seem to confirm such hypothesis.
Geometry in Active Learning for Binary and Multi-class Image Segmentation
Authors: Ksenia Konyushkova, Raphael Sznitman, Pascal Fua
Date: 2016-06-29 00:00:00
Tasks: Active Learning, Image Segmentation, Semantic Segmentation

We propose an active learning approach to image segmentation that exploits
geometric priors to speed up and streamline the annotation process. It can be
applied for both background-foreground and multi-class segmentation tasks in 2D
images and 3D image volumes. Our approach combines geometric smoothness priors
in the image space with more traditional uncertainty measures to estimate which
pixels or voxels are the most informative, and thus should to be annotated
next. For multi-class settings, we additionally introduce two novel criteria
for uncertainty. In the 3D case, we use the resulting uncertainty measure to
select voxels lying on a planar patch, which makes batch annotation much more
convenient for the end user compared to the setting where voxels are randomly
distributed in a volume. The planar patch is found using a branch-and-bound
algorithm that looks for a 2D patch in a 3D volume where the most informative
instances are located. We evaluate our approach on Electron Microscopy and
Magnetic Resonance image volumes, as well as on regular images of horses and
faces. We demonstrate a substantial performance increase over other approaches
thanks to the use of geometric priors.
StressedNets: Efficient Feature Representations via Stress-induced Evolutionary Synthesis of Deep Neural Networks
Authors: Mohammad Javad Shafiee, Brendan Chwyl, Francis Li, Rongyan Chen, Michelle Karg, Christian Scharfenberger, Alexander Wong
Date: 2018-01-16 00:00:00
Tasks: object-detection, Object Detection

The computational complexity of leveraging deep neural networks for
extracting deep feature representations is a significant barrier to its
widespread adoption, particularly for use in embedded devices. One particularly
promising strategy to addressing the complexity issue is the notion of
evolutionary synthesis of deep neural networks, which was demonstrated to
successfully produce highly efficient deep neural networks while retaining
modeling performance. Here, we further extend upon the evolutionary synthesis
strategy for achieving efficient feature extraction via the introduction of a
stress-induced evolutionary synthesis framework, where stress signals are
imposed upon the synapses of a deep neural network during training to induce
stress and steer the synthesis process towards the production of more efficient
deep neural networks over successive generations and improved model fidelity at
a greater efficiency. The proposed stress-induced evolutionary synthesis
approach is evaluated on a variety of different deep neural network
architectures (LeNet5, AlexNet, and YOLOv2) on different tasks (object
classification and object detection) to synthesize efficient StressedNets over
multiple generations. Experimental results demonstrate the efficacy of the
proposed framework to synthesize StressedNets with significant improvement in
network architecture efficiency (e.g., 40x for AlexNet and 33x for YOLOv2) and
speed improvements (e.g., 5.5x inference speed-up for YOLOv2 on an Nvidia Tegra
X1 mobile processor).
The variational Laplace approach to approximate Bayesian inference
Authors: Jean Daunizeau
Date: 2017-03-02 00:00:00
Tasks: Bayesian Inference, Model Selection, parameter estimation

Variational approaches to approximate Bayesian inference provide very
efficient means of performing parameter estimation and model selection. Among
these, so-called variational-Laplace or VL schemes rely on Gaussian
approximations to posterior densities on model parameters. In this note, we
review the main variants of VL approaches, that follow from considering
nonlinear models of continuous and/or categorical data. En passant, we also
derive a few novel theoretical results that complete the portfolio of existing
analyses of variational Bayesian approaches, including investigations of their
asymptotic convergence. We also suggest practical ways of extending existing VL
approaches to hierarchical generative models that include (e.g., precision)
hyperparameters.
Learning Deep Features for One-Class Classification
Authors: Pramuditha Perera, Vishal M. Patel
Date: 2018-01-16 00:00:00
Tasks: Anomaly Detection, Descriptive, General Classification, Novelty Detection, One-Class Classification, Template Matching

We propose a deep learning-based solution for the problem of feature learning in one-class classification. The proposed method operates on top of a Convolutional Neural Network (CNN) of choice and produces descriptive features while maintaining a low intra-class variance in the feature space for the given class. For this purpose two loss functions, compactness loss and descriptiveness loss are proposed along with a parallel CNN architecture. A template matching-based framework is introduced to facilitate the testing process. Extensive experiments on publicly available anomaly detection, novelty detection and mobile active authentication datasets show that the proposed Deep One-Class (DOC) classification method achieves significant improvements over the state-of-the-art.
Sampling Matters in Deep Embedding Learning
Authors: Chao-yuan Wu, R. Manmatha, Alexander J. Smola, Philipp Krähenbühl
Date: 2017-06-23 00:00:00
Proceeding: ICCV 2017 10
Tasks: Clustering, Face Verification, Image Retrieval, Metric Learning, Retrieval, Triplet, Zero-Shot Learning

Deep embeddings answer one simple question: How similar are two images?
Learning these embeddings is the bedrock of verification, zero-shot learning,
and visual search. The most prominent approaches optimize a deep convolutional
network with a suitable loss function, such as contrastive loss or triplet
loss. While a rich line of work focuses solely on the loss functions, we show
in this paper that selecting training examples plays an equally important role.
We propose distance weighted sampling, which selects more informative and
stable examples than traditional approaches. In addition, we show that a simple
margin based loss is sufficient to outperform all other loss functions. We
evaluate our approach on the Stanford Online Products, CAR196, and the
CUB200-2011 datasets for image retrieval and clustering, and on the LFW dataset
for face verification. Our method achieves state-of-the-art performance on all
of them.
Manifold Approximation by Moving Least-Squares Projection (MMLS)
Authors: Barak Sober, David Levin
Date: 2016-06-22 00:00:00
Tasks: Dimensionality Reduction

In order to avoid the curse of dimensionality, frequently encountered in Big Data analysis, there was a vast development in the field of linear and nonlinear dimension reduction techniques in recent years. These techniques (sometimes referred to as manifold learning) assume that the scattered input data is lying on a lower dimensional manifold, thus the high dimensionality problem can be overcome by learning the lower dimensionality behavior. However, in real life applications, data is often very noisy. In this work, we propose a method to approximate $\mathcal{M}$ a $d$-dimensional $C^{m+1}$ smooth submanifold of $\mathbb{R}^n$ ($d \ll n$) based upon noisy scattered data points (i.e., a data cloud). We assume that the data points are located "near" the lower dimensional manifold and suggest a non-linear moving least-squares projection on an approximating $d$-dimensional manifold. Under some mild assumptions, the resulting approximant is shown to be infinitely smooth and of high approximation order (i.e., $O(h^{m+1})$, where $h$ is the fill distance and $m$ is the degree of the local polynomial approximation). The method presented here assumes no analytic knowledge of the approximated manifold and the approximation algorithm is linear in the large dimension $n$. Furthermore, the approximating manifold can serve as a framework to perform operations directly on the high dimensional data in a computationally efficient manner. This way, the preparatory step of dimension reduction, which induces distortions to the data, can be avoided altogether.
Re-ID done right: towards good practices for person re-identification
Authors: Jon Almazan, Bojana Gajic, Naila Murray, Diane Larlus
Date: 2018-01-16 00:00:00
Tasks: Attribute, Person Re-Identification

Training a deep architecture using a ranking loss has become standard for the
person re-identification task. Increasingly, these deep architectures include
additional components that leverage part detections, attribute predictions,
pose estimators and other auxiliary information, in order to more effectively
localize and align discriminative image regions. In this paper we adopt a
different approach and carefully design each component of a simple deep
architecture and, critically, the strategy for training it effectively for
person re-identification. We extensively evaluate each design choice, leading
to a list of good practices for person re-identification. By following these
practices, our approach outperforms the state of the art, including more
complex methods with auxiliary components, by large margins on four benchmark
datasets. We also provide a qualitative analysis of our trained representation
which indicates that, while compact, it is able to capture information from
localized and discriminative regions, in a manner akin to an implicit attention
mechanism.
Social Network based Short-Term Stock Trading System
Authors: Paolo Cremonesi, Chiara Francalanci, Alessandro Poli, Roberto Pagano, Luca Mazzoni, Alberto Maggioni, Mehdi Elahi
Date: 2018-01-16 00:00:00
Tasks: Sentiment Analysis

This paper proposes a novel adaptive algorithm for the automated short-term
trading of financial instrument. The algorithm adopts a semantic sentiment
analysis technique to inspect the Twitter posts and to use them to predict the
behaviour of the stock market. Indeed, the algorithm is specifically developed
to take advantage of both the sentiment and the past values of a certain
financial instrument in order to choose the best investment decision. This
allows the algorithm to ensure the maximization of the obtainable profits by
trading on the stock market. We have conducted an investment simulation and
compared the performance of our proposed with a well-known benchmark (DJTATO
index) and the optimal results, in which an investor knows in advance the
future price of a product. The result shows that our approach outperforms the
benchmark and achieves the performance score close to the optimal result.
Joint Probabilistic Linear Discriminant Analysis
Authors: Luciana Ferrer
Date: 2017-04-07 00:00:00
Tasks: Speaker Recognition

Standard probabilistic linear discriminant analysis (PLDA) for speaker
recognition assumes that the sample's features (usually, i-vectors) are given
by a sum of three terms: a term that depends on the speaker identity, a term
that models the within-speaker variability and is assumed independent across
samples, and a final term that models any remaining variability and is also
independent across samples. In this work, we propose a generalization of this
model where the within-speaker variability is not necessarily assumed
independent across samples but dependent on another discrete variable. This
variable, which we call the channel variable as in the standard PLDA approach,
could be, for example, a discrete category for the channel characteristics, the
language spoken by the speaker, the type of speech in the sample
(conversational, monologue, read), etc. The value of this variable is assumed
to be known during training but not during testing. Scoring is performed, as in
standard PLDA, by computing a likelihood ratio between the null hypothesis that
the two sides of a trial belong to the same speaker versus the alternative
hypothesis that the two sides belong to different speakers. The two likelihoods
are computed by marginalizing over two hypothesis about the channels in both
sides of a trial: that they are the same and that they are different. This way,
we expect that the new model will be better at coping with same-channel versus
different-channel trials than standard PLDA, since knowledge about the channel
(or language, or speech style) is used during training and implicitly
considered during scoring.
Joint registration and synthesis using a probabilistic model for alignment of MRI and histological sections
Authors: Juan Eugenio Iglesias, Marc Modat, Loic Peter, Allison Stevens, Roberto Annunziata, Tom Vercauteren, Ed Lein, Bruce Fischl, Sebastien Ourselin
Date: 2018-01-16 00:00:00
Tasks: Bayesian Inference

Nonlinear registration of 2D histological sections with corresponding slices
of MRI data is a critical step of 3D histology reconstruction. This task is
difficult due to the large differences in image contrast and resolution, as
well as the complex nonrigid distortions produced when sectioning the sample
and mounting it on the glass slide. It has been shown in brain MRI registration
that better spatial alignment across modalities can be obtained by synthesizing
one modality from the other and then using intra-modality registration metrics,
rather than by using mutual information (MI) as metric. However, such an
approach typically requires a database of aligned images from the two
modalities, which is very difficult to obtain for histology/MRI.
  Here, we overcome this limitation with a probabilistic method that
simultaneously solves for registration and synthesis directly on the target
images, without any training data. In our model, the MRI slice is assumed to be
a contrast-warped, spatially deformed version of the histological section. We
use approximate Bayesian inference to iteratively refine the probabilistic
estimate of the synthesis and the registration, while accounting for each
other's uncertainty. Moreover, manually placed landmarks can be seamlessly
integrated in the framework for increased performance.
  Experiments on a synthetic dataset show that, compared with MI, the proposed
method makes it possible to use a much more flexible deformation model in the
registration to improve its accuracy, without compromising robustness.
Moreover, our framework also exploits information in manually placed landmarks
more efficiently than MI, since landmarks inform both synthesis and
registration - as opposed to registration alone. Finally, we show qualitative
results on the public Allen atlas, in which the proposed method provides a
clear improvement over MI based registration.
Latent Multi-task Architecture Learning
Authors: Sebastian Ruder, Joachim Bingel, Isabelle Augenstein, Anders Søgaard
Date: 2017-05-23 00:00:00
Tasks: Multi-Task Learning

Multi-task learning (MTL) allows deep neural networks to learn from related
tasks by sharing parameters with other networks. In practice, however, MTL
involves searching an enormous space of possible parameter sharing
architectures to find (a) the layers or subspaces that benefit from sharing,
(b) the appropriate amount of sharing, and (c) the appropriate relative weights
of the different task losses. Recent work has addressed each of the above
problems in isolation. In this work we present an approach that learns a latent
multi-task architecture that jointly addresses (a)--(c). We present experiments
on synthetic data and data from OntoNotes 5.0, including four different tasks
and seven different domains. Our extension consistently outperforms previous
approaches to learning latent architectures for multi-task problems and
achieves up to 15% average error reductions over common approaches to MTL.
Rank Selection of CP-decomposed Convolutional Layers with Variational Bayesian Matrix Factorization
Authors: Marcella Astrid, Seung-Ik Lee, Beom-Su Seo
Date: 2018-01-16 00:00:00
Tasks: image-classification, Image Classification

Convolutional Neural Networks (CNNs) is one of successful method in many
areas such as image classification tasks. However, the amount of memory and
computational cost needed for CNNs inference obstructs them to run efficiently
in mobile devices because of memory and computational ability limitation. One
of the method to compress CNNs is compressing the layers iteratively, i.e. by
layer-by-layer compression and fine-tuning, with CP-decomposition in
convolutional layers. To compress with CP-decomposition, rank selection is
important. In the previous approach rank selection that is based on sensitivity
of each layer, the average rank of the network was still arbitrarily selected.
Additionally, the rank of all layers were decided before whole process of
iterative compression, while the rank of a layer can be changed after
fine-tuning. Therefore, this paper proposes selecting rank of each layer using
Variational Bayesian Matrix Factorization (VBMF) which is more systematic than
arbitrary approach. Furthermore, to consider the change of each layer's rank
after fine-tuning of previous iteration, the method is applied just before
compressing the target layer, i.e. after fine-tuning of the previous iteration.
The results show better accuracy while also having more compression rate in
AlexNet's convolutional layers compression.