pwc-archive/papers-with-abstracts huggingface dataset:
Leishmaniasis Parasite Segmentation and Classification using Deep Learning
Authors: Marc Górriz, Albert Aparicio, Berta Raventós, Verónica Vilaplana, Elisa Sayrol, Daniel López-Codina
Date: 2018-12-30 00:00:00
Tasks: Classification, Deep Learning, General Classification

Leishmaniasis is considered a neglected disease that causes thousands of
deaths annually in some tropical and subtropical countries. There are various
techniques to diagnose leishmaniasis of which manual microscopy is considered
to be the gold standard. There is a need for the development of automatic
techniques that are able to detect parasites in a robust and unsupervised
manner. In this paper we present a procedure for automatizing the detection
process based on a deep learning approach. We train a U-net model that
successfully segments leismania parasites and classifies them into
promastigotes, amastigotes and adhered parasites.
Unsupervised learning by a nonlinear network with Hebbian excitatory and anti-Hebbian inhibitory neurons
Authors: H. Sebastian Seung
Date: 2018-12-30 00:00:00

This paper introduces a rate-based nonlinear neural network in which
excitatory (E) neurons receive feedforward excitation from sensory (S) neurons,
and inhibit each other through disynaptic pathways mediated by inhibitory (I)
interneurons. Correlation-based plasticity of disynaptic inhibition serves to
incompletely decorrelate E neuron activity, pushing the E neurons to learn
distinct sensory features. The plasticity equations additionally contain
"extra" terms fostering competition between excitatory synapses converging onto
the same postsynaptic neuron and inhibitory synapses diverging from the same
presynaptic neuron. The parameters of competition between S$\to$E connections
can be adjusted to make learned features look more like "parts" or "wholes."
The parameters of competition between I-E connections can be adjusted to set
the typical decorrelatedness and sparsity of E neuron activity. Numerical
simulations of unsupervised learning show that relatively few I neurons can be
sufficient for achieving good decorrelation, and increasing the number of I
neurons makes decorrelation more complete. Excitatory and inhibitory inputs to
active E neurons are approximately balanced as a result of learning.
Fingerprint Presentation Attack Detection: Generalization and Efficiency
Authors: Tarang Chugh, Anil K. Jain
Date: 2018-12-30 00:00:00
Tasks: Clustering

We study the problem of fingerprint presentation attack detection (PAD) under
unknown PA materials not seen during PAD training. A dataset of 5,743 bonafide
and 4,912 PA images of 12 different materials is used to evaluate a
state-of-the-art PAD, namely Fingerprint Spoof Buster. We utilize 3D t-SNE
visualization and clustering of material characteristics to identify a
representative set of PA materials that cover most of PA feature space. We
observe that a set of six PA materials, namely Silicone, 2D Paper, Play Doh,
Gelatin, Latex Body Paint and Monster Liquid Latex provide a good
representative set that should be included in training to achieve
generalization of PAD. We also propose an optimized Android app of Fingerprint
Spoof Buster that can run on a commodity smartphone (Xiaomi Redmi Note 4)
without a significant drop in PAD performance (from TDR = 95.7% to 95.3% @ FDR
= 0.2%) which can make a PA prediction in less than 300ms.
Learning to Selectively Transfer: Reinforced Transfer Learning for Deep Text Matching
Authors: Chen Qu, Feng Ji, Minghui Qiu, Liu Yang, Zhiyu Min, Haiqing Chen, Jun Huang, W. Bruce Croft
Date: 2018-12-30 00:00:00
Tasks: Information Retrieval, Natural Language Inference, Paraphrase Identification, Question Answering, Retrieval, Text Matching, Transfer Learning

Deep text matching approaches have been widely studied for many applications
including question answering and information retrieval systems. To deal with a
domain that has insufficient labeled data, these approaches can be used in a
Transfer Learning (TL) setting to leverage labeled data from a resource-rich
source domain. To achieve better performance, source domain data selection is
essential in this process to prevent the "negative transfer" problem. However,
the emerging deep transfer models do not fit well with most existing data
selection methods, because the data selection policy and the transfer learning
model are not jointly trained, leading to sub-optimal training efficiency.
  In this paper, we propose a novel reinforced data selector to select
high-quality source domain data to help the TL model. Specifically, the data
selector "acts" on the source domain data to find a subset for optimization of
the TL model, and the performance of the TL model can provide "rewards" in turn
to update the selector. We build the reinforced data selector based on the
actor-critic framework and integrate it to a DNN based transfer learning model,
resulting in a Reinforced Transfer Learning (RTL) method. We perform a thorough
experimental evaluation on two major tasks for text matching, namely,
paraphrase identification and natural language inference. Experimental results
show the proposed RTL can significantly improve the performance of the TL
model. We further investigate different settings of states, rewards, and policy
optimization methods to examine the robustness of our method. Last, we conduct
a case study on the selected data and find our method is able to select source
domain data whose Wasserstein distance is close to the target domain data. This
is reasonable and intuitive as such source domain data can provide more
transferability power to the model.
Monte-Carlo Sampling applied to Multiple Instance Learning for Histological Image Classification
Authors: Marc Combalia, Veronica Vilaplana
Date: 2018-12-30 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Multiple Instance Learning

We propose a patch sampling strategy based on a sequential Monte-Carlo method
for high resolution image classification in the context of Multiple Instance
Learning. When compared with grid sampling and uniform sampling techniques, it
achieves higher generalization performance. We validate the strategy on two
artificial datasets and two histological datasets for breast cancer and sun
exposure classification.
On Cross-validation for Sparse Reduced Rank Regression
Authors: Yiyuan She, Hoang Tran
Date: 2018-12-30 00:00:00
Tasks: regression

In high-dimensional data analysis, regularization methods pursuing sparsity
and/or low rank have received a lot of attention recently. To provide a proper
amount of shrinkage, it is typical to use a grid search and a model comparison
criterion to find the optimal regularization parameters. However, we show that
fixing the parameters across all folds may result in an inconsistency issue,
and it is more appropriate to cross-validate projection-selection patterns to
obtain the best coefficient estimate. Our in-sample error studies in jointly
sparse and rank-deficient models lead to a new class of information criteria
with four scale-free forms to bypass the estimation of the noise level. By use
of an identity, we propose a novel scale-free calibration to help
cross-validation achieve the minimax optimal error rate non-asymptotically.
Experiments support the efficacy of the proposed methods.
Linear solution to the minimal absolute pose rolling shutter problem
Authors: Zuzana Kukelova, Cenek Albl, Akihiro Sugimoto, Tomas Pajdla
Date: 2018-12-30 00:00:00

This paper presents new efficient solutions to the rolling shutter camera
absolute pose problem. Unlike the state-of-the-art polynomial solvers, we
approach the problem using simple and fast linear solvers in an iterative
scheme. We present several solutions based on fixing different sets of
variables and investigate the performance of them thoroughly. We design a new
alternation strategy that estimates all parameters in each iteration linearly
by fixing just the non-linear terms. Our best 6-point solver, based on the new
alternation technique, shows an identical or even better performance than the
state-of-the-art R6P solver and is two orders of magnitude faster. In addition,
a linear non-iterative solver is presented that requires a non-minimal number
of 9 correspondences but provides even better results than the state-of-the-art
R6P. Moreover, all proposed linear solvers provide a single solution while the
state-of-the-art R6P provides up to 20 solutions which have to be pruned by
expensive verification.
Comparison between DeepESNs and gated RNNs on multivariate time-series prediction
Authors: Claudio Gallicchio, Alessio Micheli, Luca Pedrelli
Date: 2018-12-30 00:00:00
Tasks: Prediction, Time Series, Time Series Analysis, Time Series Prediction

We propose an experimental comparison between Deep Echo State Networks (DeepESNs) and gated Recurrent Neural Networks (RNNs) on multivariate time-series prediction tasks. In particular, we compare reservoir and fully-trained RNNs able to represent signals featured by multiple time-scales dynamics. The analysis is performed in terms of efficiency and prediction accuracy on 4 polyphonic music tasks. Our results show that DeepESN is able to outperform ESN in terms of prediction accuracy and efficiency. Whereas, between fully-trained approaches, Gated Recurrent Units (GRU) outperforms Long Short-Term Memory (LSTM) and simple RNN models in most cases. Overall, DeepESN turned out to be extremely more efficient than others RNN approaches and the best solution in terms of prediction accuracy on 3 out of 4 tasks.
Improving forecasting accuracy of time series data using a new ARIMA-ANN hybrid method and empirical mode decomposition
Authors: Ümit Çavuş Büyükşahin, Şeyda Ertekin
Date: 2018-12-30 00:00:00
Tasks: Time Series, Time Series Analysis, Time Series Forecasting

Many applications in different domains produce large amount of time series
data. Making accurate forecasting is critical for many decision makers. Various
time series forecasting methods exist which use linear and nonlinear models
separately or combination of both. Studies show that combining of linear and
nonlinear models can be effective to improve forecasting performance. However,
some assumptions that those existing methods make, might restrict their
performance in certain situations. We provide a new Autoregressive Integrated
Moving Average (ARIMA)-Artificial Neural Network(ANN) hybrid method that work
in a more general framework. Experimental results show that strategies for
decomposing the original data and for combining linear and nonlinear models
throughout the hybridization process are key factors in the forecasting
performance of the methods. By using appropriate strategies, our hybrid method
can be an effective way to improve forecasting accuracy obtained by traditional
hybrid methods and also either of the individual methods used separately.
CoSpace: Common Subspace Learning from Hyperspectral-Multispectral Correspondences
Authors: Danfeng Hong, Naoto Yokoya, Jocelyn Chanussot, Xiao Xiang Zhu
Date: 2018-12-30 00:00:00
Tasks: Classification, General Classification, Land Cover Classification

With a large amount of open satellite multispectral imagery (e.g., Sentinel-2
and Landsat-8), considerable attention has been paid to global multispectral
land cover classification. However, its limited spectral information hinders
further improving the classification performance. Hyperspectral imaging enables
discrimination between spectrally similar classes but its swath width from
space is narrow compared to multispectral ones. To achieve accurate land cover
classification over a large coverage, we propose a cross-modality feature
learning framework, called common subspace learning (CoSpace), by jointly
considering subspace learning and supervised classification. By locally
aligning the manifold structure of the two modalities, CoSpace linearly learns
a shared latent subspace from hyperspectral-multispectral(HS-MS)
correspondences. The multispectral out-of-samples can be then projected into
the subspace, which are expected to take advantages of rich spectral
information of the corresponding hyperspectral data used for learning, and thus
leads to a better classification. Extensive experiments on two simulated HSMS
datasets (University of Houston and Chikusei), where HS-MS data sets have
trade-offs between coverage and spectral resolution, are performed to
demonstrate the superiority and effectiveness of the proposed method in
comparison with previous state-of-the-art methods.
Impact of Ground Truth Annotation Quality on Performance of Semantic Image Segmentation of Traffic Conditions
Authors: Vlad Taran, Yuri Gordienko, Alexandr Rokovyi, Oleg Alienin, Sergii Stirenko
Date: 2018-12-30 00:00:00
Tasks: Autonomous Driving, Image Segmentation, Scene Understanding, Segmentation, Semantic Segmentation

Preparation of high-quality datasets for the urban scene understanding is a
labor-intensive task, especially, for datasets designed for the autonomous
driving applications. The application of the coarse ground truth (GT)
annotations of these datasets without detriment to the accuracy of semantic
image segmentation (by the mean intersection over union - mIoU) could simplify
and speedup the dataset preparation and model fine tuning before its practical
application. Here the results of the comparative analysis for semantic
segmentation accuracy obtained by PSPNet deep learning architecture are
presented for fine and coarse annotated images from Cityscapes dataset. Two
scenarios were investigated: scenario 1 - the fine GT images for training and
prediction, and scenario 2 - the fine GT images for training and the coarse GT
images for prediction. The obtained results demonstrated that for the most
important classes the mean accuracy values of semantic image segmentation for
coarse GT annotations are higher than for the fine GT ones, and the standard
deviation values are vice versa. It means that for some applications some
unimportant classes can be excluded and the model can be tuned further for some
classes and specific regions on the coarse GT dataset without loss of the
accuracy even. Moreover, this opens the perspectives to use deep neural
networks for the preparation of such coarse GT datasets.
Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)
Authors: Guangxu Zhu, Yong Wang, Kaibin Huang
Date: 2018-12-30 00:00:00
Tasks: Scheduling

The popularity of mobile devices results in the availability of enormous data
and computational resources at the network edge. To leverage the data and
resources, a new machine learning paradigm, called edge learning, has emerged
where learning algorithms are deployed at the edge for providing fast and
intelligent services to mobile users. While computing speeds are advancing
rapidly, the communication latency is becoming the bottleneck of fast edge
learning. To address this issue, this work is focused on designing a low
latency multi-access scheme for edge learning. We consider a popular framework,
federated edge learning (FEEL), where edge-server and on-device learning are
synchronized to train a model without violating user-data privacy. It is
proposed that model updates simultaneously transmitted by devices over
broadband channels should be analog aggregated "over-the-air" by exploiting the
superposition property of a multi-access channel. Thereby, "interference" is
harnessed to provide fast implementation of the model aggregation. This results
in dramatical latency reduction compared with the traditional orthogonal access
(i.e., OFDMA). In this work, the performance of FEEL is characterized targeting
a single-cell random network. First, due to power alignment between devices as
required for aggregation, a fundamental tradeoff is shown to exist between the
update-reliability and the expected update-truncation ratio. This motivates the
design of an opportunistic scheduling scheme for FEEL that selects devices
within a distance threshold. This scheme is shown using real datasets to yield
satisfactory learning performance in the presence of high mobility. Second,
both the multi-access latency of the proposed analog aggregation and the OFDMA
scheme are analyzed. Their ratio, which quantifies the latency reduction of the
former, is proved to scale almost linearly with device population.
A High-Performance CNN Method for Offline Handwritten Chinese Character Recognition and Visualization
Authors: Pavlo Melnyk, Zhiqiang You, Keqin Li
Date: 2018-12-30 00:00:00
Tasks: Offline Handwritten Chinese Character Recognition

Recent researches introduced fast, compact and efficient convolutional neural networks (CNNs) for offline handwritten Chinese character recognition (HCCR). However, many of them did not address the problem of network interpretability. We propose a new architecture of a deep CNN with high recognition performance which is capable of learning deep features for visualization. A special characteristic of our model is the bottleneck layers which enable us to retain its expressiveness while reducing the number of multiply-accumulate operations and the required storage. We introduce a modification of global weighted average pooling (GWAP) - global weighted output average pooling (GWOAP). This paper demonstrates how they allow us to calculate class activation maps (CAMs) in order to indicate the most relevant input character image regions used by our CNN to identify a certain class. Evaluating on the ICDAR-2013 offline HCCR competition dataset, we show that our model enables a relative 0.83% error reduction while having 49% fewer parameters and the same computational cost compared to the current state-of-the-art single-network method trained only on handwritten data. Our solution outperforms even recent residual learning approaches.
Partially Non-Recurrent Controllers for Memory-Augmented Neural Networks
Authors: Naoya Taguchi, Yoshimasa Tsuruoka
Date: 2018-12-30 00:00:00

Memory-Augmented Neural Networks (MANNs) are a class of neural networks
equipped with an external memory, and are reported to be effective for tasks
requiring a large long-term memory and its selective use. The core module of a
MANN is called a controller, which is usually implemented as a recurrent neural
network (RNN) (e.g., LSTM) to enable the use of contextual information in
controlling the other modules. However, such an RNN-based controller often
allows a MANN to directly solve the given task by using the (small) internal
memory of the controller, and prevents the MANN from making the best use of the
external memory, thereby resulting in a suboptimally trained model. To address
this problem, we present a novel type of RNN-based controller that is partially
non-recurrent and avoids the direct use of its internal memory for solving the
task, while keeping the ability of using contextual information in controlling
the other modules. Our empirical experiments using Neural Turing Machines and
Differentiable Neural Computers on the Toy and bAbI tasks demonstrate that the
proposed controllers give substantially better results than standard RNN-based
controllers.
A General Deep Learning Framework for Network Reconstruction and Dynamics Learning
Authors: Zhang Zhang, Yi Zhao, Jing Liu, Shuo Wang, Ruyi Tao, Ruyue Xin, Jiang Zhang
Date: 2018-12-30 00:00:00
Tasks: Time Series, Time Series Analysis

Many complex processes can be viewed as dynamical systems on networks. However, in real cases, only the performances of the system are known, the network structure and the dynamical rules are not observed. Therefore, recovering latent network structure and dynamics from observed time series data are important tasks because it may help us to open the black box, and even to build up the model of a complex system automatically. Although this problem hosts a wealth of potential applications in biology, earth science, and epidemics etc., conventional methods have limitations. In this work, we introduce a new framework, Gumbel Graph Network (GGN), which is a model-free, data-driven deep learning framework to accomplish the reconstruction of both network connections and the dynamics on it. Our model consists of two jointly trained parts: a network generator that generating a discrete network with the Gumbel Softmax technique; and a dynamics learner that utilizing the generated network and one-step trajectory value to predict the states in future steps. We exhibit the universality of our framework on different kinds of time-series data: with the same structure, our model can be trained to accurately recover the network structure and predict future states on continuous, discrete, and binary dynamics, and outperforms competing network reconstruction methods.
DART: Domain-Adversarial Residual-Transfer Networks for Unsupervised Cross-Domain Image Classification
Authors: Xianghong Fang, Haoli Bai, Ziyi Guo, Bin Shen, Steven Hoi, Zenglin Xu
Date: 2018-12-30 00:00:00
Tasks: Classification, Domain Adaptation, General Classification, image-classification, Image Classification, Unsupervised Domain Adaptation

The accuracy of deep learning (e.g., convolutional neural networks) for an
image classification task critically relies on the amount of labeled training
data. Aiming to solve an image classification task on a new domain that lacks
labeled data but gains access to cheaply available unlabeled data, unsupervised
domain adaptation is a promising technique to boost the performance without
incurring extra labeling cost, by assuming images from different domains share
some invariant characteristics. In this paper, we propose a new unsupervised
domain adaptation method named Domain-Adversarial Residual-Transfer (DART)
learning of Deep Neural Networks to tackle cross-domain image classification
tasks. In contrast to the existing unsupervised domain adaption approaches, the
proposed DART not only learns domain-invariant features via adversarial
training, but also achieves robust domain-adaptive classification via a
residual-transfer strategy, all in an end-to-end training framework. We
evaluate the performance of the proposed method for cross-domain image
classification tasks on several well-known benchmark data sets, in which our
method clearly outperforms the state-of-the-art approaches.
Machine learning in resting-state fMRI analysis
Authors: Meenakshi Khosla, Keith Jamison, Gia H. Ngo, Amy Kuceyeski, Mert R. Sabuncu
Date: 2018-12-30 00:00:00
Tasks: BIG-bench Machine Learning

Machine learning techniques have gained prominence for the analysis of
resting-state functional Magnetic Resonance Imaging (rs-fMRI) data. Here, we
present an overview of various unsupervised and supervised machine learning
applications to rs-fMRI. We present a methodical taxonomy of machine learning
methods in resting-state fMRI. We identify three major divisions of
unsupervised learning methods with regard to their applications to rs-fMRI,
based on whether they discover principal modes of variation across space, time
or population. Next, we survey the algorithms and rs-fMRI feature
representations that have driven the success of supervised subject-level
predictions. The goal is to provide a high-level overview of the burgeoning
field of rs-fMRI from the perspective of machine learning applications.