pwc-archive/papers-with-abstracts huggingface dataset:
Medical Time Series Classification with Hierarchical Attention-based Temporal Convolutional Networks: A Case Study of Myotonic Dystrophy Diagnosis
Authors: Lei Lin, Beilei Xu, Wencheng Wu, Trevor Richardson, Edgar A. Bernal
Date: 2019-03-28 00:00:00
Tasks: Computational Efficiency, General Classification, Time Series, Time Series Analysis, Time Series Classification

Myotonia, which refers to delayed muscle relaxation after contraction, is the
main symptom of myotonic dystrophy patients. We propose a hierarchical
attention-based temporal convolutional network (HA-TCN) for myotonic dystrohpy
diagnosis from handgrip time series data, and introduce mechanisms that enable
model explainability. We compare the performance of the HA-TCN model against
that of benchmark TCN models, LSTM models with and without attention
mechanisms, and SVM approaches with handcrafted features. In terms of
classification accuracy and F1 score, we found all deep learning models have
similar levels of performance, and they all outperform SVM. Further, the HA-TCN
model outperforms its TCN counterpart with regards to computational efficiency
regardless of network depth, and in terms of performance particularly when the
number of hidden layers is small. Lastly, HA-TCN models can consistently
identify relevant time series segments in the relaxation phase of the handgrip
time series, and exhibit increased robustness to noise when compared to
attention-based LSTM models.
InfoMask: Masked Variational Latent Representation to Localize Chest Disease
Authors: Saeid Asgari Taghanaki, Mohammad Havaei, Tess Berthier, Francis Dutil, Lisa Di Jorio, Ghassan Hamarneh, Yoshua Bengio
Date: 2019-03-28 00:00:00
Tasks: Medical Image Analysis, Multiple Instance Learning

The scarcity of richly annotated medical images is limiting supervised deep learning based solutions to medical image analysis tasks, such as localizing discriminatory radiomic disease signatures. Therefore, it is desirable to leverage unsupervised and weakly supervised models. Most recent weakly supervised localization methods apply attention maps or region proposals in a multiple instance learning formulation. While attention maps can be noisy, leading to erroneously highlighted regions, it is not simple to decide on an optimal window/bag size for multiple instance learning approaches. In this paper, we propose a learned spatial masking mechanism to filter out irrelevant background signals from attention maps. The proposed method minimizes mutual information between a masked variational representation and the input while maximizing the information between the masked representation and class labels. This results in more accurate localization of discriminatory regions. We tested the proposed model on the ChestX-ray8 dataset to localize pneumonia from chest X-ray images without using any pixel-level or bounding-box annotations.
AutoSlim: Towards One-Shot Architecture Search for Channel Numbers
Authors: Jiahui Yu, Thomas Huang
Date: 2019-03-27 00:00:00
Proceeding: ICLR 2020 1
Tasks: Neural Architecture Search, Reinforcement Learning

We study how to set channel numbers in a neural network to achieve better accuracy under constrained resources (e.g., FLOPs, latency, memory footprint or model size). A simple and one-shot solution, named AutoSlim, is presented. Instead of training many network samples and searching with reinforcement learning, we train a single slimmable network to approximate the network accuracy of different channel configurations. We then iteratively evaluate the trained slimmable model and greedily slim the layer with minimal accuracy drop. By this single pass, we can obtain the optimized channel configurations under different resource constraints. We present experiments with MobileNet v1, MobileNet v2, ResNet-50 and RL-searched MNasNet on ImageNet classification. We show significant improvements over their default channel configurations. We also achieve better accuracy than recent channel pruning methods and neural architecture search methods. Notably, by setting optimized channel numbers, our AutoSlim-MobileNet-v2 at 305M FLOPs achieves 74.2% top-1 accuracy, 2.4% better than default MobileNet-v2 (301M FLOPs), and even 0.2% better than RL-searched MNasNet (317M FLOPs). Our AutoSlim-ResNet-50 at 570M FLOPs, without depthwise convolutions, achieves 1.3% better accuracy than MobileNet-v1 (569M FLOPs). Code and models will be available at: https://github.com/JiahuiYu/slimmable_networks
Radiological images and machine learning: trends, perspectives, and prospects
Authors: Zhenwei Zhang, Ervin Sejdic
Date: 2019-03-27 00:00:00
Tasks: BIG-bench Machine Learning, Content-Based Image Retrieval, Decision Making, Image Registration, Image Retrieval, Image Segmentation, Medical Image Segmentation, Retrieval, Semantic Segmentation

The application of machine learning to radiological images is an increasingly
active research area that is expected to grow in the next five to ten years.
Recent advances in machine learning have the potential to recognize and
classify complex patterns from different radiological imaging modalities such
as x-rays, computed tomography, magnetic resonance imaging and positron
emission tomography imaging. In many applications, machine learning based
systems have shown comparable performance to human decision-making. The
applications of machine learning are the key ingredients of future clinical
decision making and monitoring systems. This review covers the fundamental
concepts behind various machine learning techniques and their applications in
several radiological imaging areas, such as medical image segmentation, brain
function studies and neurological disease diagnosis, as well as computer-aided
systems, image registration, and content-based image retrieval systems.
Synchronistically, we will briefly discuss current challenges and future
directions regarding the application of machine learning in radiological
imaging. By giving insight on how take advantage of machine learning powered
applications, we expect that clinicians can prevent and diagnose diseases more
accurately and efficiently.
The Semantic Web Rule Language Expressiveness Extensions-A Survey
Authors: Abba Lawan, Abdur Rakib
Date: 2019-03-27 00:00:00
Tasks: Survey

The Semantic Web Rule Language (SWRL) is a direct extension of OWL 2 DL with
a subset of RuleML, and it is designed to be the rule language of the Semantic
Web. This paper explores the state-of-the-art of SWRL's expressiveness
extensions proposed over time. As a motivation, the effectiveness of the
SWRL/OWL combination in modeling domain facts is discussed while some of the
common expressive limitations of the combination are also highlighted. The
paper then classifies and presents the relevant language extensions of the SWRL
and their added expressive powers to the original SWRL definition. Furthermore,
it provides a comparative analysis of the syntax and semantics of the proposed
extensions. In conclusion, the decidability requirement and usability of each
expressiveness extension are evaluated towards an efficient inclusion into the
OWL ontologies.
Fairness in Algorithmic Decision Making: An Excursion Through the Lens of Causality
Authors: Aria Khademi, Sanghack Lee, David Foley, Vasant Honavar
Date: 2019-03-27 00:00:00
Tasks: Attribute, Decision Making, Fairness

As virtually all aspects of our lives are increasingly impacted by
algorithmic decision making systems, it is incumbent upon us as a society to
ensure such systems do not become instruments of unfair discrimination on the
basis of gender, race, ethnicity, religion, etc. We consider the problem of
determining whether the decisions made by such systems are discriminatory,
through the lens of causal models. We introduce two definitions of group
fairness grounded in causality: fair on average causal effect (FACE), and fair
on average causal effect on the treated (FACT). We use the Rubin-Neyman
potential outcomes framework for the analysis of cause-effect relationships to
robustly estimate FACE and FACT. We demonstrate the effectiveness of our
proposed approach on synthetic data. Our analyses of two real-world data sets,
the Adult income data set from the UCI repository (with gender as the protected
attribute), and the NYC Stop and Frisk data set (with race as the protected
attribute), show that the evidence of discrimination obtained by FACE and FACT,
or lack thereof, is often in agreement with the findings from other studies. We
further show that FACT, being somewhat more nuanced compared to FACE, can yield
findings of discrimination that differ from those obtained using FACE.
Cookie Synchronization: Everything You Always Wanted to Know But Were Afraid to Ask
Authors: Panagiotis Papadopoulos, Nicolas Kourtellis, Evangelos P. Markatos
Date: 2018-05-26 00:00:00

User data is the primary input of digital advertising, fueling the free Internet as we know it. As a result, web companies invest a lot in elaborate tracking mechanisms to acquire user data that can sell to data markets and advertisers. However, with same-origin policy, and cookies as a primary identification mechanism on the web, each tracker knows the same user with a different ID. To mitigate this, Cookie Synchronization (CSync) came to the rescue, facilitating an information sharing channel between third parties that may or not have direct access to the website the user visits. In the background, with CSync, they merge user data they own, but also reconstruct a user's browsing history, bypassing the same origin policy. In this paper, we perform a first to our knowledge in-depth study of CSync in the wild, using a year-long weblog from 850 real mobile users. Through our study, we aim to understand the characteristics of the CSync protocol and the impact it has on web users' privacy. For this, we design and implement CONRAD, a holistic mechanism to detect CSync events at real time, and the privacy loss on the user side, even when the synced IDs are obfuscated. Using CONRAD, we find that 97% of the regular web users are exposed to CSync: most of them within the first week of their browsing, and the median userID gets leaked, on average, to 3.5 different domains. Finally, we see that CSync increases the number of domains that track the user by a factor of 6.75.
A Multi Hidden Recurrent Neural Network with a Modified Grey Wolf Optimizer
Authors: Tarik A. Rashid, Dosti K. Abbas, Yalin K. Turel
Date: 2019-03-27 00:00:00

Identifying university students' weaknesses results in better learning and
can function as an early warning system to enable students to improve. However,
the satisfaction level of existing systems is not promising. New and dynamic
hybrid systems are needed to imitate this mechanism. A hybrid system (a
modified Recurrent Neural Network with an adapted Grey Wolf Optimizer) is used
to forecast students' outcomes. This proposed system would improve instruction
by the faculty and enhance the students' learning experiences. The results show
that a modified recurrent neural network with an adapted Grey Wolf Optimizer
has the best accuracy when compared with other models.
Recurrent Neural Networks For Accurate RSSI Indoor Localization
Authors: Minh Tu Hoang, Brosnan Yuen, Xiaodai Dong, Tao Lu, Robert Westendorp, Kishore Reddy
Date: 2019-03-27 00:00:00
Tasks: Indoor Localization, Position

This paper proposes recurrent neuron networks (RNNs) for a fingerprinting indoor localization using WiFi. Instead of locating user's position one at a time as in the cases of conventional algorithms, our RNN solution aims at trajectory positioning and takes into account the relation among the received signal strength indicator (RSSI) measurements in a trajectory. Furthermore, a weighted average filter is proposed for both input RSSI data and sequential output locations to enhance the accuracy among the temporal fluctuations of RSSI. The results using different types of RNN including vanilla RNN, long short-term memory (LSTM), gated recurrent unit (GRU) and bidirectional LSTM (BiLSTM) are presented. On-site experiments demonstrate that the proposed structure achieves an average localization error of $0.75$ m with $80\%$ of the errors under $1$ m, which outperforms the conventional KNN algorithms and probabilistic algorithms by approximately $30\%$ under the same test environment.
Zero-shot Image Recognition Using Relational Matching, Adaptation and Calibration
Authors: Debasmit Das, C. S. George Lee
Date: 2019-03-27 00:00:00
Tasks: Domain Adaptation, General Classification, image-classification, Image Classification, Zero-Shot Learning

Zero-shot learning (ZSL) for image classification focuses on recognizing
novel categories that have no labeled data available for training. The learning
is generally carried out with the help of mid-level semantic descriptors
associated with each class. This semantic-descriptor space is generally shared
by both seen and unseen categories. However, ZSL suffers from hubness, domain
discrepancy and biased-ness towards seen classes. To tackle these problems, we
propose a three-step approach to zero-shot learning. Firstly, a mapping is
learned from the semantic-descriptor space to the image-feature space. This
mapping learns to minimize both one-to-one and pairwise distances between
semantic embeddings and the image features of the corresponding classes.
Secondly, we propose test-time domain adaptation to adapt the semantic
embedding of the unseen classes to the test data. This is achieved by finding
correspondences between the semantic descriptors and the image features.
Thirdly, we propose scaled calibration on the classification scores of the seen
classes. This is necessary because the ZSL model is biased towards seen classes
as the unseen classes are not used in the training. Finally, to validate the
proposed three-step approach, we performed experiments on four benchmark
datasets where the proposed method outperformed previous results. We also
studied and analyzed the performance of each component of our proposed ZSL
framework.
Stable prediction with radiomics data
Authors: Carel F. W. Peeters, Caroline Übelhör, Steven W. Mes, Roland Martens, Thomas Koopman, Pim de Graaf, Floris H. P. van Velden, Ronald Boellaard, Jonas A. Castelijns, Dennis E. te Beest, Martijn W. Heymans, Mark A. van de Wiel
Date: 2019-03-27 00:00:00
Tasks: Classification, feature selection, General Classification, Prediction

Motivation: Radiomics refers to the high-throughput mining of quantitative
features from radiographic images. It is a promising field in that it may
provide a non-invasive solution for screening and classification. Standard
machine learning classification and feature selection techniques, however, tend
to display inferior performance in terms of (the stability of) predictive
performance. This is due to the heavy multicollinearity present in radiomic
data. We set out to provide an easy-to-use approach that deals with this
problem.
  Results: We developed a four-step approach that projects the original
high-dimensional feature space onto a lower-dimensional latent-feature space,
while retaining most of the covariation in the data. It consists of (i)
penalized maximum likelihood estimation of a redundancy filtered correlation
matrix. The resulting matrix (ii) is the input for a maximum likelihood factor
analysis procedure. This two-stage maximum-likelihood approach can be used to
(iii) produce a compact set of stable features that (iv) can be directly used
in any (regression-based) classifier or predictor. It outperforms other
classification (and feature selection) techniques in both external and internal
validation settings regarding survival in squamous cell cancers.
Echo State Networks with Self-Normalizing Activations on the Hyper-Sphere
Authors: Pietro Verzelli, Cesare Alippi, Lorenzo Livi
Date: 2019-03-27 00:00:00
Tasks: Benchmarking

Among the various architectures of Recurrent Neural Networks, Echo State Networks (ESNs) emerged due to their simplified and inexpensive training procedure. These networks are known to be sensitive to the setting of hyper-parameters, which critically affect their behaviour. Results show that their performance is usually maximized in a narrow region of hyper-parameter space called edge of chaos. Finding such a region requires searching in hyper-parameter space in a sensible way: hyper-parameter configurations marginally outside such a region might yield networks exhibiting fully developed chaos, hence producing unreliable computations. The performance gain due to optimizing hyper-parameters can be studied by considering the memory--nonlinearity trade-off, i.e., the fact that increasing the nonlinear behavior of the network degrades its ability to remember past inputs, and vice-versa. In this paper, we propose a model of ESNs that eliminates critical dependence on hyper-parameters, resulting in networks that provably cannot enter a chaotic regime and, at the same time, denotes nonlinear behaviour in phase space characterised by a large memory of past inputs, comparable to the one of linear networks. Our contribution is supported by experiments corroborating our theoretical findings, showing that the proposed model displays dynamics that are rich-enough to approximate many common nonlinear systems used for benchmarking.
Optimization of Inf-Convolution Regularized Nonconvex Composite Problems
Authors: Emanuel Laude, Tao Wu, Daniel Cremers
Date: 2019-03-27 00:00:00

In this work, we consider nonconvex composite problems that involve
inf-convolution with a Legendre function, which gives rise to an anisotropic
generalization of the proximal mapping and Moreau-envelope. In a convex setting
such problems can be solved via alternating minimization of a splitting
formulation, where the consensus constraint is penalized with a Legendre
function. In contrast, for nonconvex models it is in general unclear that this
approach yields stationary points to the infimal convolution problem. To this
end we analytically investigate local regularity properties of the
Moreau-envelope function under prox-regularity, which allows us to establish
the equivalence between stationary points of the splitting model and the
original inf-convolution model. We apply our theory to characterize stationary
points of the penalty objective, which is minimized by the elastic averaging
SGD (EASGD) method for distributed training. Numerically, we demonstrate the
practical relevance of the proposed approach on the important task of
distributed training of deep neural networks.
Rallying Adversarial Techniques against Deep Learning for Network Security
Authors: Joseph Clements, Yuzhe Yang, Ankur Sharma, Hongxin Hu, Yingjie Lao
Date: 2019-03-27 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning, Intrusion Detection, Network Intrusion Detection

Recent advances in artificial intelligence and the increasing need for powerful defensive measures in the domain of network security, have led to the adoption of deep learning approaches for use in network intrusion detection systems. These methods have achieved superior performance against conventional network attacks, which enable the deployment of practical security systems to unique and dynamic sectors. Adversarial machine learning, unfortunately, has recently shown that deep learning models are inherently vulnerable to adversarial modifications on their input data. Because of this susceptibility, the deep learning models deployed to power a network defense could in fact be the weakest entry point for compromising a network system. In this paper, we show that by modifying on average as little as 1.38 of the input features, an adversary can generate malicious inputs which effectively fool a deep learning based NIDS. Therefore, when designing such systems, it is crucial to consider the performance from not only the conventional network security perspective but also the adversarial machine learning domain.
Outlier-Robust Spatial Perception: Hardness, General-Purpose Algorithms, and Guarantees
Authors: Vasileios Tzoumas, Pasquale Antonante, Luca Carlone
Date: 2019-03-27 00:00:00
Tasks: Pose Estimation

Spatial perception is the backbone of many robotics applications, and spans a broad range of research problems, including localization and mapping, point cloud alignment, and relative pose estimation from camera images. Robust spatial perception is jeopardized by the presence of incorrect data association, and in general, outliers. Although techniques to handle outliers do exist, they can fail in unpredictable manners (e.g., RANSAC, robust estimators), or can have exponential runtime (e.g., branch-and-bound). In this paper, we advance the state of the art in outlier rejection by making three contributions. First, we show that even a simple linear instance of outlier rejection is inapproximable: in the worst-case one cannot design a quasi-polynomial time algorithm that computes an approximate solution efficiently. Our second contribution is to provide the first per-instance sub-optimality bounds to assess the approximation quality of a given outlier rejection outcome. Our third contribution is to propose a simple general-purpose algorithm, named adaptive trimming, to remove outliers. Our algorithm leverages recently-proposed global solvers that are able to solve outlier-free problems, and iteratively removes measurements with large errors. We demonstrate the proposed algorithm on three spatial perception problems: 3D registration, two-view geometry, and SLAM. The results show that our algorithm outperforms several state-of-the-art methods across applications while being a general-purpose method.
Gradient Descent with Early Stopping is Provably Robust to Label Noise for Overparameterized Neural Networks
Authors: Mingchen Li, Mahdi Soltanolkotabi, Samet Oymak
Date: 2019-03-27 00:00:00

Modern neural networks are typically trained in an over-parameterized regime where the parameters of the model far exceed the size of the training data. Such neural networks in principle have the capacity to (over)fit any set of labels including pure noise. Despite this, somewhat paradoxically, neural network models trained via first-order methods continue to predict well on yet unseen test data. This paper takes a step towards demystifying this phenomena. Under a rich dataset model, we show that gradient descent is provably robust to noise/corruption on a constant fraction of the labels despite overparameterization. In particular, we prove that: (i) In the first few iterations where the updates are still in the vicinity of the initialization gradient descent only fits to the correct labels essentially ignoring the noisy labels. (ii) to start to overfit to the noisy labels network must stray rather far from from the initialization which can only occur after many more iterations. Together, these results show that gradient descent with early stopping is provably robust to label noise and shed light on the empirical robustness of deep networks as well as commonly adopted heuristics to prevent overfitting.
Tree Search vs Optimization Approaches for Map Generation
Authors: Debosmita Bhaumik, Ahmed Khalifa, Michael Cerny Green, Julian Togelius
Date: 2019-03-27 00:00:00
Tasks: Evolutionary Algorithms, global-optimization, Sokoban

Search-based procedural content generation uses stochastic global optimization algorithms to search for game content. However, standard tree search algorithms can be competitive with evolution on some optimization problems. We investigate the applicability of several tree search methods to level generation and compare them systematically with several optimization algorithms, including evolutionary algorithms. We compare them on three different game level generation problems: Binary, Zelda, and Sokoban. We introduce two new representations that can help tree search algorithms deal with the large branching factor of the generation problem. We find that in general, optimization algorithms clearly outperform tree search algorithms, but given the right problem representation certain tree search algorithms perform similarly to optimization algorithms, and in one particular problem, we see surprisingly strong results from MCTS.