pwc-archive/papers-with-abstracts huggingface dataset:
Product Function Need Recognition via Semi-supervised Attention Network
Authors: Hu Xu, Sihong Xie, Lei Shu, Philip S. Yu
Date: 2017-12-06 00:00:00

Functionality is of utmost importance to customers when they purchase
products. However, it is unclear to customers whether a product can really
satisfy their needs on functions. Further, missing functions may be
intentionally hidden by the manufacturers or the sellers. As a result, a
customer needs to spend a fair amount of time before purchasing or just
purchase the product on his/her own risk. In this paper, we first identify a
novel QA corpus that is dense on product functionality information
\footnote{The annotated corpus can be found at
\url{https://www.cs.uic.edu/~hxu/}.}. We then design a neural network called
Semi-supervised Attention Network (SAN) to discover product functions from
questions. This model leverages unlabeled data as contextual information to
perform semi-supervised sequence labeling. We conduct experiments to show that
the extracted function have both high coverage and accuracy, compared with a
wide spectrum of baselines.
Single Image Super-resolution via a Lightweight Residual Convolutional Neural Network
Authors: Yudong Liang, Ze Yang, Kai Zhang, Yihui He, Jinjun Wang, Nanning Zheng
Date: 2017-03-23 00:00:00
Tasks: Image Super-Resolution, SSIM, Super-Resolution

Recent years have witnessed great success of convolutional neural network
(CNN) for various problems both in low and high level visions. Especially
noteworthy is the residual network which was originally proposed to handle
high-level vision problems and enjoys several merits. This paper aims to extend
the merits of residual network, such as skip connection induced fast training,
for a typical low-level vision problem, i.e., single image super-resolution. In
general, the two main challenges of existing deep CNN for supper-resolution lie
in the gradient exploding/vanishing problem and large numbers of parameters or
computational cost as CNN goes deeper. Correspondingly, the skip connections or
identity mapping shortcuts are utilized to avoid gradient exploding/vanishing
problem. In addition, the skip connections have naturally centered the
activation which led to better performance. To tackle with the second problem,
a lightweight CNN architecture which has carefully designed width, depth and
skip connections was proposed. In particular, a strategy of gradually varying
the shape of network has been proposed for residual network. Different residual
architectures for image super-resolution have also been compared. Experimental
results have demonstrated that the proposed CNN model can not only achieve
state-of-the-art PSNR and SSIM results for single image super-resolution but
also produce visually pleasant results. This paper has extended the mmm 2017
oral conference paper with a considerable new analyses and more experiments
especially from the perspective of centering activations and ensemble behaviors
of residual network.
Detecting Curve Text in the Wild: New Dataset and New Solution
Authors: Liu Yuliang, Jin Lianwen, Zhang Shuaitao, Zhang Sheng
Date: 2017-12-06 00:00:00
Tasks: Curved Text Detection, Scene Text Detection, Text Detection

Scene text detection has been made great progress in recent years. The
detection manners are evolving from axis-aligned rectangle to rotated rectangle
and further to quadrangle. However, current datasets contain very little curve
text, which can be widely observed in scene images such as signboard, product
name and so on. To raise the concerns of reading curve text in the wild, in
this paper, we construct a curve text dataset named CTW1500, which includes
over 10k text annotations in 1,500 images (1000 for training and 500 for
testing). Based on this dataset, we pioneering propose a polygon based curve
text detector (CTD) which can directly detect curve text without empirical
combination. Moreover, by seamlessly integrating the recurrent transverse and
longitudinal offset connection (TLOC), the proposed method can be end-to-end
trainable to learn the inherent connection among the position offsets. This
allows the CTD to explore context information instead of predicting points
independently, resulting in more smooth and accurate detection. We also propose
two simple but effective post-processing methods named non-polygon suppress
(NPS) and polygonal non-maximum suppression (PNMS) to further improve the
detection accuracy. Furthermore, the proposed approach in this paper is
designed in an universal manner, which can also be trained with rectangular or
quadrilateral bounding boxes without extra efforts. Experimental results on
CTW-1500 demonstrate our method with only a light backbone can outperform
state-of-the-art methods with a large margin. By evaluating only in the curve
or non-curve subset, the CTD + TLOC can still achieve the best results. Code is
available at https://github.com/Yuliang-Liu/Curve-Text-Detector.
LocNet: Global localization in 3D point clouds for mobile vehicles
Authors: Huan Yin, Li Tang, Xiaqing Ding, Yue Wang, Rong Xiong
Date: 2017-12-06 00:00:00
Tasks: Pose Estimation, Representation Learning

Global localization in 3D point clouds is a challenging problem of estimating
the pose of vehicles without any prior knowledge. In this paper, a solution to
this problem is presented by achieving place recognition and metric pose
estimation in the global prior map. Specifically, we present a semi-handcrafted
representation learning method for LiDAR point clouds using siamese LocNets,
which states the place recognition problem to a similarity modeling problem.
With the final learned representations by LocNet, a global localization
framework with range-only observations is proposed. To demonstrate the
performance and effectiveness of our global localization system, KITTI dataset
is employed for comparison with other algorithms, and also on our long-time
multi-session datasets for evaluation. The result shows that our system can
achieve high accuracy.
Distribution-Based Categorization of Classifier Transfer Learning
Authors: Ricardo Gamelas Sousa, Luís A. Alexandre, Jorge M. Santos, Luís M. Silva, Joaquim Marques de Sá
Date: 2017-12-06 00:00:00
Tasks: General Classification, Transfer Learning

Transfer Learning (TL) aims to transfer knowledge acquired in one problem,
the source problem, onto another problem, the target problem, dispensing with
the bottom-up construction of the target model. Due to its relevance, TL has
gained significant interest in the Machine Learning community since it paves
the way to devise intelligent learning models that can easily be tailored to
many different applications. As it is natural in a fast evolving area, a wide
variety of TL methods, settings and nomenclature have been proposed so far.
However, a wide range of works have been reporting different names for the same
concepts. This concept and terminology mixture contribute however to obscure
the TL field, hindering its proper consideration. In this paper we present a
review of the literature on the majority of classification TL methods, and also
a distribution-based categorization of TL with a common nomenclature suitable
to classification problems. Under this perspective three main TL categories are
presented, discussed and illustrated with examples.
Guided Labeling using Convolutional Neural Networks
Authors: Sebastian Stabinger, Antonio Rodriguez-Sanchez
Date: 2017-12-06 00:00:00
Tasks: Deep Learning

Over the last couple of years, deep learning and especially convolutional
neural networks have become one of the work horses of computer vision. One
limiting factor for the applicability of supervised deep learning to more areas
is the need for large, manually labeled datasets. In this paper we propose an
easy to implement method we call guided labeling, which automatically
determines which samples from an unlabeled dataset should be labeled. We show
that using this procedure, the amount of samples that need to be labeled is
reduced considerably in comparison to labeling images arbitrarily.
A Generalised Seizure Prediction with Convolutional Neural Networks for Intracranial and Scalp Electroencephalogram Data Analysis
Authors: Nhan Duy Truong, Anh Duy Nguyen, Levin Kuhlmann, Mohammad Reza Bonyadi, Jiawei Yang, Omid Kavehei
Date: 2017-07-06 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Feature Engineering, Prediction, Seizure prediction, Sensitivity

Seizure prediction has attracted a growing attention as one of the most
challenging predictive data analysis efforts in order to improve the life of
patients living with drug-resistant epilepsy and tonic seizures. Many
outstanding works have been reporting great results in providing a sensible
indirect (warning systems) or direct (interactive neural-stimulation) control
over refractory seizures, some of which achieved high performance. However,
many works put heavily handcraft feature extraction and/or carefully tailored
feature engineering to each patient to achieve very high sensitivity and low
false prediction rate for a particular dataset. This limits the benefit of
their approaches if a different dataset is used. In this paper we apply
Convolutional Neural Networks (CNNs) on different intracranial and scalp
electroencephalogram (EEG) datasets and proposed a generalized retrospective
and patient-specific seizure prediction method. We use Short-Time Fourier
Transform (STFT) on 30-second EEG windows with 50% overlapping to extract
information in both frequency and time domains. A standardization step is then
applied on STFT components across the whole frequency range to prevent high
frequencies features being influenced by those at lower frequencies. A
convolutional neural network model is used for both feature extraction and
classification to separate preictal segments from interictal ones. The proposed
approach achieves sensitivity of 81.4%, 81.2%, 82.3% and false prediction rate
(FPR) of 0.06/h, 0.16/h, 0.22/h on Freiburg Hospital intracranial EEG (iEEG)
dataset, Children's Hospital of Boston-MIT scalp EEG (sEEG) dataset, and Kaggle
American Epilepsy Society Seizure Prediction Challenge's dataset, respectively.
Our prediction method is also statistically better than an unspecific random
predictor for most of patients in all three datasets.
Enabling Early Audio Event Detection with Neural Networks
Authors: Huy Phan, Philipp Koch, Ian McLoughlin, Alfred Mertins
Date: 2017-12-06 00:00:00
Tasks: Event Detection

This paper presents a methodology for early detection of audio events from
audio streams. Early detection is the ability to infer an ongoing event during
its initial stage. The proposed system consists of a novel inference step
coupled with dual parallel tailored-loss deep neural networks (DNNs). The DNNs
share a similar architecture except for their loss functions, i.e. weighted
loss and multitask loss, which are designed to efficiently cope with issues
common to audio event detection. The inference step is newly introduced to make
use of the network outputs for recognizing ongoing events. The monotonicity of
the detection function is required for reliable early detection, and will also
be proved. Experiments on the ITC-Irst database show that the proposed system
achieves state-of-the-art detection performance. Furthermore, even partial
events are sufficient to achieve good performance similar to that obtained when
an entire event is observed, enabling early event detection.
Multi-channel Encoder for Neural Machine Translation
Authors: Hao Xiong, Zhongjun He, Xiaoguang Hu, Hua Wu
Date: 2017-12-06 00:00:00
Tasks: Decoder, Machine Translation, NMT, Sentence, Translation

Attention-based Encoder-Decoder has the effective architecture for neural
machine translation (NMT), which typically relies on recurrent neural networks
(RNN) to build the blocks that will be lately called by attentive reader during
the decoding process. This design of encoder yields relatively uniform
composition on source sentence, despite the gating mechanism employed in
encoding RNN. On the other hand, we often hope the decoder to take pieces of
source sentence at varying levels suiting its own linguistic structure: for
example, we may want to take the entity name in its raw form while taking an
idiom as a perfectly composed unit. Motivated by this demand, we propose
Multi-channel Encoder (MCE), which enhances encoding components with different
levels of composition. More specifically, in addition to the hidden state of
encoding RNN, MCE takes 1) the original word embedding for raw encoding with no
composition, and 2) a particular design of external memory in Neural Turing
Machine (NTM) for more complex composition, while all three encoding strategies
are properly blended during decoding. Empirical study on Chinese-English
translation shows that our model can improve by 6.52 BLEU points upon a strong
open source NMT system: DL4MT1. On the WMT14 English- French task, our single
shallow system achieves BLEU=38.8, comparable with the state-of-the-art deep
models.
Separating Reflection and Transmission Images in the Wild
Authors: Patrick Wieschollek, Orazio Gallo, Jinwei Gu, Jan Kautz
Date: 2017-12-06 00:00:00
Proceeding: ECCV 2018 9
Tasks: Synthetic Data Generation

The reflections caused by common semi-reflectors, such as glass windows, can
impact the performance of computer vision algorithms. State-of-the-art methods
can remove reflections on synthetic data and in controlled scenarios. However,
they are based on strong assumptions and do not generalize well to real-world
images. Contrary to a common misconception, real-world images are challenging
even when polarization information is used. We present a deep learning approach
to separate the reflected and the transmitted components of the recorded
irradiance, which explicitly uses the polarization properties of light. To
train it, we introduce an accurate synthetic data generation pipeline, which
simulates realistic reflections, including those generated by curved and
non-ideal surfaces, non-static scenes, and high-dynamic-range scenes.
Deep learning analysis of the myocardium in coronary CT angiography for identification of patients with functionally significant coronary artery stenosis
Authors: Majd Zreik, Nikolas Lessmann, Robbert W. van Hamersvelt, Jelmer M. Wolterink, Michiel Voskuil, Max A. Viergever, Tim Leiner, Ivana Išgum
Date: 2017-11-24 00:00:00
Tasks: Anatomy, Myocardium Segmentation, Specificity

In patients with coronary artery stenoses of intermediate severity, the
functional significance needs to be determined. Fractional flow reserve (FFR)
measurement, performed during invasive coronary angiography (ICA), is most
often used in clinical practice. To reduce the number of ICA procedures, we
present a method for automatic identification of patients with functionally
significant coronary artery stenoses, employing deep learning analysis of the
left ventricle (LV) myocardium in rest coronary CT angiography (CCTA). The
study includes consecutively acquired CCTA scans of 166 patients with FFR
measurements. To identify patients with a functionally significant coronary
artery stenosis, analysis is performed in several stages. First, the LV
myocardium is segmented using a multiscale convolutional neural network (CNN).
To characterize the segmented LV myocardium, it is subsequently encoded using
unsupervised convolutional autoencoder (CAE). Thereafter, patients are
classified according to the presence of functionally significant stenosis using
an SVM classifier based on the extracted and clustered encodings. Quantitative
evaluation of LV myocardium segmentation in 20 images resulted in an average
Dice coefficient of 0.91 and an average mean absolute distance between the
segmented and reference LV boundaries of 0.7 mm. Classification of patients was
evaluated in the remaining 126 CCTA scans in 50 10-fold cross-validation
experiments and resulted in an area under the receiver operating characteristic
curve of 0.74 +- 0.02. At sensitivity levels 0.60, 0.70 and 0.80, the
corresponding specificity was 0.77, 0.71 and 0.59, respectively. The results
demonstrate that automatic analysis of the LV myocardium in a single CCTA scan
acquired at rest, without assessment of the anatomy of the coronary arteries,
can be used to identify patients with functionally significant coronary artery
stenosis.
Convolutional Neural Networks for Medical Diagnosis from Admission Notes
Authors: Christy Li, Dimitris Konomis, Graham Neubig, Pengtao Xie, Carol Cheng, Eric Xing
Date: 2017-12-06 00:00:00
Tasks: Decision Making, Diagnostic, Medical Diagnosis, text-classification, Text Classification

$\textbf{Objective}$ Develop an automatic diagnostic system which only uses
textual admission information from Electronic Health Records (EHRs) and assist
clinicians with a timely and statistically proved decision tool. The hope is
that the tool can be used to reduce mis-diagnosis.
  $\textbf{Materials and Methods}$ We use the real-world clinical notes from
MIMIC-III, a freely available dataset consisting of clinical data of more than
forty thousand patients who stayed in intensive care units of the Beth Israel
Deaconess Medical Center between 2001 and 2012. We proposed a Convolutional
Neural Network model to learn semantic features from unstructured textual input
and automatically predict primary discharge diagnosis.
  $\textbf{Results}$ The proposed model achieved an overall 96.11% accuracy and
80.48% weighted F1 score values on 10 most frequent disease classes,
significantly outperforming four strong baseline models by at least 12.7% in
weighted F1 score.
  $\textbf{Discussion}$ Experimental results imply that the CNN model is
suitable for supporting diagnosis decision making in the presence of complex,
noisy and unstructured clinical data while at the same time using fewer layers
and parameters that other traditional Deep Network models.
  $\textbf{Conclusion}$ Our model demonstrated capability of representing
complex medical meaningful features from unstructured clinical notes and
prediction power for commonly misdiagnosed frequent diseases. It can use easily
adopted in clinical setting to provide timely and statistically proved decision
support.
  $\textbf{Keywords}$ Convolutional neural network, text classification,
discharge diagnosis prediction, admission information from EHRs.
A Local Analysis of Block Coordinate Descent for Gaussian Phase Retrieval
Authors: David Barmherzig, Ju Sun
Date: 2017-12-06 00:00:00
Tasks: Retrieval

While convergence of the Alternating Direction Method of Multipliers (ADMM)
on convex problems is well studied, convergence on nonconvex problems is only
partially understood. In this paper, we consider the Gaussian phase retrieval
problem, formulated as a linear constrained optimization problem with a
biconvex objective. The particular structure allows for a novel application of
the ADMM. It can be shown that the dual variable is zero at the global
minimizer. This motivates the analysis of a block coordinate descent algorithm,
which is equivalent to the ADMM with the dual variable fixed to be zero. We
show that the block coordinate descent algorithm converges to the global
minimizer at a linear rate, when starting from a deterministically achievable
initialization point.
Adaptive Submodularity: Theory and Applications in Active Learning and Stochastic Optimization
Authors: Daniel Golovin, Andreas Krause
Date: 2010-03-21 00:00:00
Tasks: Active Learning, Marketing, Stochastic Optimization

Solving stochastic optimization problems under partial observability, where
one needs to adaptively make decisions with uncertain outcomes, is a
fundamental but notoriously difficult challenge. In this paper, we introduce
the concept of adaptive submodularity, generalizing submodular set functions to
adaptive policies. We prove that if a problem satisfies this property, a simple
adaptive greedy algorithm is guaranteed to be competitive with the optimal
policy. In addition to providing performance guarantees for both stochastic
maximization and coverage, adaptive submodularity can be exploited to
drastically speed up the greedy algorithm by using lazy evaluations. We
illustrate the usefulness of the concept by giving several examples of adaptive
submodular objectives arising in diverse applications including sensor
placement, viral marketing and active learning. Proving adaptive submodularity
for these problems allows us to recover existing results in these applications
as special cases, improve approximation guarantees and handle natural
generalizations.
Automatic Segmentation and Overall Survival Prediction in Gliomas using Fully Convolutional Neural Network and Texture Analysis
Authors: Varghese Alex, Mohammed Safwan, Ganapathy Krishnamurthi
Date: 2017-12-06 00:00:00
Tasks: General Classification, Prediction, Survival Prediction, Texture Classification

In this paper, we use a fully convolutional neural network (FCNN) for the
segmentation of gliomas from Magnetic Resonance Images (MRI). A fully
automatic, voxel based classification was achieved by training a 23 layer deep
FCNN on 2-D slices extracted from patient volumes. The network was trained on
slices extracted from 130 patients and validated on 50 patients. For the task
of survival prediction, texture and shape based features were extracted from T1
post contrast volume to train an XGBoost regressor. On BraTS 2017 validation
set, the proposed scheme achieved a mean whole tumor, tumor core and active
dice score of 0.83, 0.69 and 0.69 respectively and an accuracy of 52% for the
overall survival prediction.
Unsupervised Multi-Domain Image Translation with Domain-Specific Encoders/Decoders
Authors: Le Hui, Xiang Li, Jiaxin Chen, Hongliang He, Chen Gong, Jian Yang
Date: 2017-12-06 00:00:00
Tasks: Decoder, Domain Adaptation, Image-to-Image Translation, Incremental Learning, Translation, Unsupervised Image-To-Image Translation

Unsupervised Image-to-Image Translation achieves spectacularly advanced
developments nowadays. However, recent approaches mainly focus on one model
with two domains, which may face heavy burdens with large cost of $O(n^2)$
training time and model parameters, under such a requirement that $n$ domains
are freely transferred to each other in a general setting. To address this
problem, we propose a novel and unified framework named Domain-Bank, which
consists of a global shared auto-encoder and $n$ domain-specific
encoders/decoders, assuming that a universal shared-latent sapce can be
projected. Thus, we yield $O(n)$ complexity in model parameters along with a
huge reduction of the time budgets. Besides the high efficiency, we show the
comparable (or even better) image translation results over state-of-the-arts on
various challenging unsupervised image translation tasks, including face image
translation, fashion-clothes translation and painting style translation. We
also apply the proposed framework to domain adaptation and achieve
state-of-the-art performance on digit benchmark datasets. Further, thanks to
the explicit representation of the domain-specific decoders as well as the
universal shared-latent space, it also enables us to conduct incremental
learning to add a new domain encoder/decoder. Linear combination of different
domains' representations is also obtained by fusing the corresponding decoders.