pwc-archive/papers-with-abstracts huggingface dataset:
Liver segmentation in CT images using three dimensional to two dimensional fully convolutional network
Authors: Shima Rafiei, Ebrahim Nasr-Esfahani, S. M. Reza Soroushmehr, Nader Karimi, Shadrokh Samavi, Kayvan Najarian
Date: 2018-02-21 00:00:00
Tasks: Liver Segmentation, Organ Segmentation, Segmentation

The need for CT scan analysis is growing for pre-diagnosis and therapy of
abdominal organs. Automatic organ segmentation of abdominal CT scan can help
radiologists analyze the scans faster and segment organ images with fewer
errors. However, existing methods are not efficient enough to perform the
segmentation process for victims of accidents and emergencies situations. In
this paper we propose an efficient liver segmentation with our 3D to 2D fully
connected network (3D-2D-FCN). The segmented mask is enhanced by means of
conditional random field on the organ's border. Consequently, we segment a
target liver in less than a minute with Dice score of 93.52.
Entropy-SGD optimizes the prior of a PAC-Bayes bound: Generalization properties of Entropy-SGD and data-dependent priors
Authors: Gintare Karolina Dziugaite, Daniel M. Roy
Date: 2017-12-26 00:00:00
Proceeding: ICML 2018 7
Tasks: Generalization Bounds, valid

We show that Entropy-SGD (Chaudhari et al., 2017), when viewed as a learning
algorithm, optimizes a PAC-Bayes bound on the risk of a Gibbs (posterior)
classifier, i.e., a randomized classifier obtained by a risk-sensitive
perturbation of the weights of a learned classifier. Entropy-SGD works by
optimizing the bound's prior, violating the hypothesis of the PAC-Bayes theorem
that the prior is chosen independently of the data. Indeed, available
implementations of Entropy-SGD rapidly obtain zero training error on random
labels and the same holds of the Gibbs posterior. In order to obtain a valid
generalization bound, we rely on a result showing that data-dependent priors
obtained by stochastic gradient Langevin dynamics (SGLD) yield valid PAC-Bayes
bounds provided the target distribution of SGLD is {\epsilon}-differentially
private. We observe that test error on MNIST and CIFAR10 falls within the
(empirically nonvacuous) risk bounds computed under the assumption that SGLD
reaches stationarity. In particular, Entropy-SGLD can be configured to yield
relatively tight generalization bounds and still fit real labels, although
these same settings do not obtain state-of-the-art performance.
On the diffusion approximation of nonconvex stochastic gradient descent
Authors: Wenqing Hu, Chris Junchi Li, Lei LI, Jian-Guo Liu
Date: 2017-05-22 00:00:00

We study the Stochastic Gradient Descent (SGD) method in nonconvex
optimization problems from the point of view of approximating diffusion
processes. We prove rigorously that the diffusion process can approximate the
SGD algorithm weakly using the weak form of master equation for probability
evolution. In the small step size regime and the presence of omnidirectional
noise, our weak approximating diffusion process suggests the following dynamics
for the SGD iteration starting from a local minimizer (resp.~saddle point): it
escapes in a number of iterations exponentially (resp.~almost linearly)
dependent on the inverse stepsize. The results are obtained using the theory
for random perturbations of dynamical systems (theory of large deviations for
local minimizers and theory of exiting for unstable stationary points). In
addition, we discuss the effects of batch size for the deep neural networks,
and we find that small batch size is helpful for SGD algorithms to escape
unstable stationary points and sharp minimizers. Our theory indicates that one
should increase the batch size at later stage for the SGD to be trapped in flat
minimizers for better generalization.
Tag-Enhanced Tree-Structured Neural Networks for Implicit Discourse Relation Classification
Authors: Yizhong Wang, Sujian Li, Jingfeng Yang, Xu sun, Houfeng Wang
Date: 2018-03-03 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: General Classification, Implicit Discourse Relation Classification, Relation, Relation Classification, Semantic Composition, TAG

Identifying implicit discourse relations between text spans is a challenging
task because it requires understanding the meaning of the text. To tackle this
task, recent studies have tried several deep learning methods but few of them
exploited the syntactic information. In this work, we explore the idea of
incorporating syntactic parse tree into neural networks. Specifically, we
employ the Tree-LSTM model and Tree-GRU model, which are based on the tree
structure, to encode the arguments in a relation. Moreover, we further leverage
the constituent tags to control the semantic composition process in these
tree-structured neural networks. Experimental results show that our method
achieves state-of-the-art performance on PDTB corpus.
The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches
Authors: Md Zahangir Alom, Tarek M. Taha, Christopher Yakopcic, Stefan Westberg, Paheding Sidike, Mst Shamima Nasrin, Brian C Van Esesn, Abdul A S. Awwal, Vijayan K. Asari
Date: 2018-03-03 00:00:00
Tasks: Deep Learning, Deep Reinforcement Learning, Generative Adversarial Network, Machine Translation, Reinforcement Learning, speech-recognition, Speech Recognition, Survey

Deep learning has demonstrated tremendous success in variety of application
domains in the past few years. This new field of machine learning has been
growing rapidly and applied in most of the application domains with some new
modalities of applications, which helps to open new opportunity. There are
different methods have been proposed on different category of learning
approaches, which includes supervised, semi-supervised and un-supervised
learning. The experimental results show state-of-the-art performance of deep
learning over traditional machine learning approaches in the field of Image
Processing, Computer Vision, Speech Recognition, Machine Translation, Art,
Medical imaging, Medical information processing, Robotics and control,
Bio-informatics, Natural Language Processing (NLP), Cyber security, and many
more. This report presents a brief survey on development of DL approaches,
including Deep Neural Network (DNN), Convolutional Neural Network (CNN),
Recurrent Neural Network (RNN) including Long Short Term Memory (LSTM) and
Gated Recurrent Units (GRU), Auto-Encoder (AE), Deep Belief Network (DBN),
Generative Adversarial Network (GAN), and Deep Reinforcement Learning (DRL). In
addition, we have included recent development of proposed advanced variant DL
techniques based on the mentioned DL approaches. Furthermore, DL approaches
have explored and evaluated in different application domains are also included
in this survey. We have also comprised recently developed frameworks, SDKs, and
benchmark datasets that are used for implementing and evaluating deep learning
approaches. There are some surveys have published on Deep Learning in Neural
Networks [1, 38] and a survey on RL [234]. However, those papers have not
discussed the individual advanced techniques for training large scale deep
learning models and the recently developed method of generative models [1].
Beyond Patient Monitoring: Conversational Agents Role in Telemedicine & Healthcare Support For Home-Living Elderly Individuals
Authors: Ahmed Fadhil
Date: 2018-03-03 00:00:00

There is a need for systems to dynamically interact with ageing populations
to gather information, monitor health condition and provide support, especially
after hospital discharge or at-home settings. Several smart devices have been
delivered by digital health, bundled with telemedicine systems, smartphone and
other digital services. While such solutions offer personalised data and
suggestions, the real disruptive step comes from the interaction of new digital
ecosystem, represented by chatbots. Chatbots will play a leading role by
embodying the function of a virtual assistant and bridging the gap between
patients and clinicians. Powered by AI and machine learning algorithms,
chatbots are forecasted to save healthcare costs when used in place of a human
or assist them as a preliminary step of helping to assess a condition and
providing self-care recommendations. This paper describes integrating chatbots
into telemedicine systems intended for elderly patient after their hospital
discharge. The paper discusses possible ways to utilise chatbots to assist
healthcare providers and support patients with their condition.
Enhancement of land-use change modeling using convolutional neural networks and convolutional denoising autoencoders
Authors: Guodong Du, Liang Yuan, Kong Joo Shin, Shunsuke Managi
Date: 2018-03-03 00:00:00
Tasks: Denoising

The neighborhood effect is a key driving factor for the land-use change (LUC)
process. This study applies convolutional neural networks (CNN) to capture
neighborhood characteristics from satellite images and to enhance the
performance of LUC modeling. We develop a hybrid CNN model (conv-net) to
predict the LU transition probability by combining satellite images and
geographical features. A spatial weight layer is designed to incorporate the
distance-decay characteristics of neighborhood effect into conv-net. As an
alternative model, we also develop a hybrid convolutional denoising autoencoder
and multi-layer perceptron model (CDAE-net), which specifically learns latent
representations from satellite images and denoises the image data. Finally, a
DINAMICA-based cellular automata (CA) model simulates the LU pattern. The
results show that the convolutional-based models improve the modeling
performances compared with a model that accepts only the geographical features.
Overall, conv-net outperforms CDAE-net in terms of LUC predictive performance.
Nonetheless, CDAE-net performs better when the data are noisy.
Student Beats the Teacher: Deep Neural Networks for Lateral Ventricles Segmentation in Brain MR
Authors: Mohsen Ghafoorian, Jonas Teuwen, Rashindra Manniesing, Frank-Erik de Leeuw, Bram van Ginneken, Nico Karssemeijer, Bram Platel
Date: 2018-01-15 00:00:00
Tasks: Segmentation

Ventricular volume and its progression are known to be linked to several
brain diseases such as dementia and schizophrenia. Therefore accurate
measurement of ventricle volume is vital for longitudinal studies on these
disorders, making automated ventricle segmentation algorithms desirable. In the
past few years, deep neural networks have shown to outperform the classical
models in many imaging domains. However, the success of deep networks is
dependent on manually labeled data sets, which are expensive to acquire
especially for higher dimensional data in the medical domain. In this work, we
show that deep neural networks can be trained on much-cheaper-to-acquire
pseudo-labels (e.g., generated by other automated less accurate methods) and
still produce more accurate segmentations compared to the quality of the
labels. To show this, we use noisy segmentation labels generated by a
conventional region growing algorithm to train a deep network for lateral
ventricle segmentation. Then on a large manually annotated test set, we show
that the network significantly outperforms the conventional region growing
algorithm which was used to produce the training labels for the network. Our
experiments report a Dice Similarity Coefficient (DSC) of $0.874$ for the
trained network compared to $0.754$ for the conventional region growing
algorithm ($p < 0.001$).
A Structural Correlation Filter Combined with A Multi-task Gaussian Particle Filter for Visual Tracking
Authors: Manna Dai, Shuying Cheng, Xiangjian He, Dadong Wang
Date: 2018-03-03 00:00:00
Tasks: Visual Tracking

In this paper, we propose a novel structural correlation filter combined with
a multi-task Gaussian particle filter (KCF-GPF) model for robust visual
tracking. We first present an assemble structure where several KCF trackers as
weak experts provide a preliminary decision for a Gaussian particle filter to
make a final decision. The proposed method is designed to exploit and
complement the strength of a KCF and a Gaussian particle filter. Compared with
the existing tracking methods based on correlation filters or particle filters,
the proposed tracker has several advantages. First, it can detect the tracked
target in a large-scale search scope via weak KCF trackers and evaluate the
reliability of weak trackers\rq decisions for a Gaussian particle filter to
make a strong decision, and hence it can tackle fast motions, appearance
variations, occlusions and re-detections. Second, it can effectively handle
large-scale variations via a Gaussian particle filter. Third, it can be
amenable to fully parallel implementation using importance sampling without
resampling, thereby it is convenient for VLSI implementation and can lower the
computational costs. Extensive experiments on the OTB-2013 dataset containing
50 challenging sequences demonstrate that the proposed algorithm performs
favourably against 16 state-of-the-art trackers.
Towards Automatic & Personalised Mobile Health Interventions: An Interactive Machine Learning Perspective
Authors: Ahmed Fadhil
Date: 2018-03-03 00:00:00
Tasks: BIG-bench Machine Learning

Machine learning (ML) is the fastest growing field in computer science and
healthcare, providing future benefits in improved medical diagnoses, disease
analyses and prevention. In this paper, we introduce an application of
interactive machine learning (iML) in a telemedicine system, to enable
automatic and personalised interventions for lifestyle promotion. We first
present the high level architecture of the system and the components forming
the overall architecture. We then illustrate the interactive machine learning
process design. Prediction models are expected to be trained through the
participants' profiles, activity performance, and feedback from the caregiver.
Finally, we show some preliminary results during the system implementation and
discuss future directions. We envisage the proposed system to be digitally
implemented, and behaviourally designed to promote healthy lifestyle and
activities, and hence prevent users from the risk of chronic diseases.
A Conversational Interface to Improve Medication Adherence: Towards AI Support in Patient's Treatment
Authors: Ahmed Fadhil
Date: 2018-03-03 00:00:00
Tasks: Chatbot, Diagnostic

Medication adherence is of utmost importance for many chronic conditions,
regardless of the disease type. Engaging patients in self-tracking their
medication is a big challenge. One way to potentially reduce this burden is to
use reminders to promote wellness throughout all stages of life and improve
medication adherence. Chatbots have proven effectiveness in triggering users to
engage in certain activity, such as medication adherence. In this paper, we
discuss "Roborto", a chatbot to create an engaging interactive and intelligent
environment for patients and assist in positive lifestyle modification. We
introduce a way for healthcare providers to track patients adherence and
intervene whenever necessary. We describe the health, technical and behavioural
approaches to the problem of medication non-adherence and propose a diagnostic
and decision support tool. The proposed study will be implemented and validated
through a pilot experiment with users to measure the efficacy of the proposed
approach.
OIL: Observational Imitation Learning
Authors: Guohao Li, Matthias Müller, Vincent Casser, Neil Smith, Dominik L. Michels, Bernard Ghanem
Date: 2018-03-03 00:00:00
Tasks: Autonomous Driving, Autonomous Navigation, Imitation Learning, Reinforcement Learning, Reinforcement Learning (RL)

Recent work has explored the problem of autonomous navigation by imitating a teacher and learning an end-to-end policy, which directly predicts controls from raw images. However, these approaches tend to be sensitive to mistakes by the teacher and do not scale well to other environments or vehicles. To this end, we propose Observational Imitation Learning (OIL), a novel imitation learning variant that supports online training and automatic selection of optimal behavior by observing multiple imperfect teachers. We apply our proposed methodology to the challenging problems of autonomous driving and UAV racing. For both tasks, we utilize the Sim4CV simulator that enables the generation of large amounts of synthetic training data and also allows for online learning and evaluation. We train a perception network to predict waypoints from raw image data and use OIL to train another network to predict controls from these waypoints. Extensive experiments demonstrate that our trained network outperforms its teachers, conventional imitation learning (IL) and reinforcement learning (RL) baselines and even humans in simulation. The project website is available at https://sites.google.com/kaust.edu.sa/oil/ and a video at https://youtu.be/_rhq8a0qgeg
Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial Examples
Authors: Minhao Cheng, Jin-Feng Yi, Pin-Yu Chen, huan zhang, Cho-Jui Hsieh
Date: 2018-03-03 00:00:00
Tasks: image-classification, Image Classification, Machine Translation, Text Summarization, Translation

Crafting adversarial examples has become an important technique to evaluate the robustness of deep neural networks (DNNs). However, most existing works focus on attacking the image classification problem since its input space is continuous and output space is finite. In this paper, we study the much more challenging problem of crafting adversarial examples for sequence-to-sequence (seq2seq) models, whose inputs are discrete text strings and outputs have an almost infinite number of possibilities. To address the challenges caused by the discrete input space, we propose a projected gradient method combined with group lasso and gradient regularization. To handle the almost infinite output space, we design some novel loss functions to conduct non-overlapping attack and targeted keyword attack. We apply our algorithm to machine translation and text summarization tasks, and verify the effectiveness of the proposed algorithm: by changing less than 3 words, we can make seq2seq model to produce desired outputs with high success rates. On the other hand, we recognize that, compared with the well-evaluated CNN-based classifiers, seq2seq models are intrinsically more robust to adversarial attacks.
Some Considerations on Learning to Explore via Meta-Reinforcement Learning
Authors: Bradly C. Stadie, Ge Yang, Rein Houthooft, Xi Chen, Yan Duan, Yuhuai Wu, Pieter Abbeel, Ilya Sutskever
Date: 2018-03-03 00:00:00
Proceeding: ICLR 2018 1
Tasks: Meta Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We consider the problem of exploration in meta reinforcement learning. Two
new meta reinforcement learning algorithms are suggested: E-MAML and
E-$\text{RL}^2$. Results are presented on a novel environment we call `Krazy
World' and a set of maze environments. We show E-MAML and E-$\text{RL}^2$
deliver better performance on tasks where exploration is important.
Focal Loss Dense Detector for Vehicle Surveillance
Authors: Xiaoliang Wang, Peng Cheng, Xinchuan Liu, Benedict Uzochukwu
Date: 2018-03-03 00:00:00
Tasks: Object, object-detection, Object Detection, vehicle detection

Deep learning has been widely recognized as a promising approach in different
computer vision applications. Specifically, one-stage object detector and
two-stage object detector are regarded as the most important two groups of
Convolutional Neural Network based object detection methods. One-stage object
detector could usually outperform two-stage object detector in speed; However,
it normally trails in detection accuracy, compared with two-stage object
detectors. In this study, focal loss based RetinaNet, which works as one-stage
object detector, is utilized to be able to well match the speed of regular
one-stage detectors and also defeat two-stage detectors in accuracy, for
vehicle detection. State-of-the-art performance result has been showed on the
DETRAC vehicle dataset.
Vehicle Pose and Shape Estimation through Multiple Monocular Vision
Authors: Wenhao Ding, Shuaijun Li, Guilin Zhang, Xiangyu Lei, Huihuan Qian
Date: 2018-02-10 00:00:00

In this paper, we present an accurate approach to estimate vehicles' pose and
shape from off-board multiview images. The images are taken by monocular
cameras and have small overlaps. We utilize state-of-the-art convolutional
neural networks (CNNs) to extract vehicles' semantic keypoints and introduce a
Cross Projection Optimization (CPO) method to estimate the 3D pose. During the
iterative CPO process, an adaptive shape adjustment method named Hierarchical
Wireframe Constraint (HWC) is implemented to estimate the shape. Our approach
is evaluated under both simulated and real-world scenes for performance
verification. It's shown that our algorithm outperforms other existing
monocular and stereo methods for vehicles' pose and shape estimation. This
approach provides a new and robust solution for off-board visual vehicle
localization and tracking, which can be applied to massive surveillance camera
networks for intelligent transportation.
High-Quality Facial Photo-Sketch Synthesis Using Multi-Adversarial Networks
Authors: Lidan Wang, Vishwanath A. Sindagi, Vishal M. Patel
Date: 2017-10-27 00:00:00
Tasks: Face Sketch Synthesis, Image Quality Assessment, Image-to-Image Translation, Translation, Vocal Bursts Intensity Prediction

Synthesizing face sketches from real photos and its inverse have many
applications. However, photo/sketch synthesis remains a challenging problem due
to the fact that photo and sketch have different characteristics. In this work,
we consider this task as an image-to-image translation problem and explore the
recently popular generative models (GANs) to generate high-quality realistic
photos from sketches and sketches from photos. Recent GAN-based methods have
shown promising results on image-to-image translation problems and
photo-to-sketch synthesis in particular, however, they are known to have
limited abilities in generating high-resolution realistic images. To this end,
we propose a novel synthesis framework called Photo-Sketch Synthesis using
Multi-Adversarial Networks, (PS2-MAN) that iteratively generates low resolution
to high resolution images in an adversarial way. The hidden layers of the
generator are supervised to first generate lower resolution images followed by
implicit refinement in the network to generate higher resolution images.
Furthermore, since photo-sketch synthesis is a coupled/paired translation
problem, we leverage the pair information using CycleGAN framework. Both Image
Quality Assessment (IQA) and Photo-Sketch Matching experiments are conducted to
demonstrate the superior performance of our framework in comparison to existing
state-of-the-art solutions. Code available at:
https://github.com/lidan1/PhotoSketchMAN.