October 17, 2019

3380 words 16 mins read

Paper Group ANR 873

Investigating the Impact of CNN Depth on Neonatal Seizure Detection Performance. Change Point Methods on a Sequence of Graphs. Elements of Effective Deep Reinforcement Learning towards Tactical Driving Decision Making. DeepPap: Deep Convolutional Networks for Cervical Cell Classification. Towards Single-phase Single-stage Detection of Pulmonary Nod …

Investigating the Impact of CNN Depth on Neonatal Seizure Detection Performance


Title	Investigating the Impact of CNN Depth on Neonatal Seizure Detection Performance
Authors	Alison O’Shea, Gordon Lightbody, Geraldine Boylan, Andriy Temko
Abstract	This study presents a novel, deep, fully convolutional architecture which is optimized for the task of EEG-based neonatal seizure detection. Architectures of different depths were designed and tested; varying network depth impacts convolutional receptive fields and the corresponding learned feature complexity. Two deep convolutional networks are compared with a shallow SVM-based neonatal seizure detector, which relies on the extraction of hand-crafted features. On a large clinical dataset, of over 800 hours of multichannel unedited EEG, containing 1389 seizure events, the deep 11-layer architecture significantly outperforms the shallower architectures, improving the AUC90 from 82.6% to 86.8%. Combining the end-to-end deep architecture with the feature-based shallow SVM further improves the AUC90 to 87.6%. The fusion of classifiers of different depths gives greatly improved performance and reduced variability, making the combined classifier more clinically reliable.
Tasks	EEG, Seizure Detection
Published	2018-06-08
URL	http://arxiv.org/abs/1806.03044v1
PDF	http://arxiv.org/pdf/1806.03044v1.pdf
PWC	https://paperswithcode.com/paper/investigating-the-impact-of-cnn-depth-on
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Change Point Methods on a Sequence of Graphs


Title	Change Point Methods on a Sequence of Graphs
Authors	Daniele Zambon, Cesare Alippi, Lorenzo Livi
Abstract	Given a finite sequence of graphs, e.g., coming from technological, biological, and social networks, the paper proposes a methodology to identify possible changes in stationarity in the stochastic process generating the graphs. In order to cover a large class of applications, we consider the general family of attributed graphs where both topology (number of vertexes and edge configuration) and related attributes are allowed to change also in the stationary case. Novel Change Point Methods (CPMs) are proposed, that (i) map graphs into a vector domain; (ii) apply a suitable statistical test in the vector space; (iii) detect the change –if any– according to a confidence level and provide an estimate for its time occurrence. Two specific multivariate CPMs have been designed: one that detects shifts in the distribution mean, the other addressing generic changes affecting the distribution. We ground our proposal with theoretical results showing how to relate the inference attained in the numerical vector space to the graph domain, and vice versa. We also show how to extend the methodology for handling multiple change points in the same sequence. Finally, the proposed CPMs have been validated on real data sets coming from epileptic-seizure detection problems and on labeled data sets for graph classification. Results show the effectiveness of what proposed in relevant application scenarios.
Tasks	Graph Classification, Seizure Detection
Published	2018-05-18
URL	http://arxiv.org/abs/1805.07113v2
PDF	http://arxiv.org/pdf/1805.07113v2.pdf
PWC	https://paperswithcode.com/paper/change-point-methods-on-a-sequence-of-graphs
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Elements of Effective Deep Reinforcement Learning towards Tactical Driving Decision Making


Title	Elements of Effective Deep Reinforcement Learning towards Tactical Driving Decision Making
Authors	Jingchu Liu, Pengfei Hou, Lisen Mu, Yinan Yu, Chang Huang
Abstract	Tactical driving decision making is crucial for autonomous driving systems and has attracted considerable interest in recent years. In this paper, we propose several practical components that can speed up deep reinforcement learning algorithms towards tactical decision making tasks: 1) non-uniform action skipping as a more stable alternative to action-repetition frame skipping, 2) a counter-based penalty for lanes on which ego vehicle has less right-of-road, and 3) heuristic inference-time action masking for apparently undesirable actions. We evaluate the proposed components in a realistic driving simulator and compare them with several baselines. Results show that the proposed scheme provides superior performance in terms of safety, efficiency, and comfort.
Tasks	Autonomous Driving, Decision Making
Published	2018-02-01
URL	http://arxiv.org/abs/1802.00332v1
PDF	http://arxiv.org/pdf/1802.00332v1.pdf
PWC	https://paperswithcode.com/paper/elements-of-effective-deep-reinforcement
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DeepPap: Deep Convolutional Networks for Cervical Cell Classification


Title	DeepPap: Deep Convolutional Networks for Cervical Cell Classification
Authors	Ling Zhang, Le Lu, Isabella Nogues, Ronald M. Summers, Shaoxiong Liu, Jianhua Yao
Abstract	Automation-assisted cervical screening via Pap smear or liquid-based cytology (LBC) is a highly effective cell imaging based cancer detection tool, where cells are partitioned into “abnormal” and “normal” categories. However, the success of most traditional classification methods relies on the presence of accurate cell segmentations. Despite sixty years of research in this field, accurate segmentation remains a challenge in the presence of cell clusters and pathologies. Moreover, previous classification methods are only built upon the extraction of hand-crafted features, such as morphology and texture. This paper addresses these limitations by proposing a method to directly classify cervical cells - without prior segmentation - based on deep features, using convolutional neural networks (ConvNets). First, the ConvNet is pre-trained on a natural image dataset. It is subsequently fine-tuned on a cervical cell dataset consisting of adaptively re-sampled image patches coarsely centered on the nuclei. In the testing phase, aggregation is used to average the prediction scores of a similar set of image patches. The proposed method is evaluated on both Pap smear and LBC datasets. Results show that our method outperforms previous algorithms in classification accuracy (98.3%), area under the curve (AUC) (0.99) values, and especially specificity (98.3%), when applied to the Herlev benchmark Pap smear dataset and evaluated using five-fold cross-validation. Similar superior performances are also achieved on the HEMLBC (H&E stained manual LBC) dataset. Our method is promising for the development of automation-assisted reading systems in primary cervical screening.
Tasks
Published	2018-01-25
URL	http://arxiv.org/abs/1801.08616v1
PDF	http://arxiv.org/pdf/1801.08616v1.pdf
PWC	https://paperswithcode.com/paper/deeppap-deep-convolutional-networks-for
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Towards Single-phase Single-stage Detection of Pulmonary Nodules in Chest CT Imaging


Title	Towards Single-phase Single-stage Detection of Pulmonary Nodules in Chest CT Imaging
Authors	Zhongliu Xie
Abstract	Detection of pulmonary nodules in chest CT imaging plays a crucial role in early diagnosis of lung cancer. Manual examination is highly time-consuming and error prone, calling for computer-aided detection, both to improve efficiency and reduce misdiagnosis. Over the years, a range of systems have been proposed, mostly following a two-phase paradigm with: 1) candidate detection, 2) false positive reduction. Recently, deep learning has become a dominant force in algorithm development. As for candidate detection, prior art was mainly based on the two-stage Faster R-CNN framework, which starts with an initial sub-net to generate a set of class-agnostic region proposals, followed by a second sub-net to perform classification and bounding-box regression. In contrast, we abandon the conventional two-phase paradigm and two-stage framework altogether and propose to train a single network for end-to-end nodule detection instead, without transfer learning or further post-processing. Our feature learning model is a modification of the ResNet and feature pyramid network combined, powered by RReLU activation. The major challenge is the condition of extreme inter-class and intra-class sample imbalance, where the positives are overwhelmed by a large negative pool, which is mostly composed of easy and a handful of hard negatives. Direct training on all samples can seriously undermine training efficacy. We propose a patch-based sampling strategy over a set of regularly updating anchors, which narrows sampling scope to all positives and only hard negatives, effectively addressing this issue. As a result, our approach substantially outperforms prior art in terms of both accuracy and speed. Finally, the prevailing FROC evaluation over [1/8, 1/4, 1/2, 1, 2, 4, 8] false positives per scan, is far from ideal in real clinical environments. We suggest FROC over [1, 2, 4] false positives as a better metric.
Tasks	Transfer Learning
Published	2018-07-16
URL	http://arxiv.org/abs/1807.05972v1
PDF	http://arxiv.org/pdf/1807.05972v1.pdf
PWC	https://paperswithcode.com/paper/towards-single-phase-single-stage-detection
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A Robust AUC Maximization Framework with Simultaneous Outlier Detection and Feature Selection for Positive-Unlabeled Classification


Title	A Robust AUC Maximization Framework with Simultaneous Outlier Detection and Feature Selection for Positive-Unlabeled Classification
Authors	Ke Ren, Haichuan Yang, Yu Zhao, Mingshan Xue, Hongyu Miao, Shuai Huang, Ji Liu
Abstract	The positive-unlabeled (PU) classification is a common scenario in real-world applications such as healthcare, text classification, and bioinformatics, in which we only observe a few samples labeled as “positive” together with a large volume of “unlabeled” samples that may contain both positive and negative samples. Building robust classifier for the PU problem is very challenging, especially for complex data where the negative samples overwhelm and mislabeled samples or corrupted features exist. To address these three issues, we propose a robust learning framework that unifies AUC maximization (a robust metric for biased labels), outlier detection (for excluding wrong labels), and feature selection (for excluding corrupted features). The generalization error bounds are provided for the proposed model that give valuable insight into the theoretical performance of the method and lead to useful practical guidance, e.g., to train a model, we find that the included unlabeled samples are sufficient as long as the sample size is comparable to the number of positive samples in the training process. Empirical comparisons and two real-world applications on surgical site infection (SSI) and EEG seizure detection are also conducted to show the effectiveness of the proposed model.
Tasks	EEG, Feature Selection, Outlier Detection, Seizure Detection, Text Classification
Published	2018-03-18
URL	http://arxiv.org/abs/1803.06604v1
PDF	http://arxiv.org/pdf/1803.06604v1.pdf
PWC	https://paperswithcode.com/paper/a-robust-auc-maximization-framework-with
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JointDNN: An Efficient Training and Inference Engine for Intelligent Mobile Cloud Computing Services


Title	JointDNN: An Efficient Training and Inference Engine for Intelligent Mobile Cloud Computing Services
Authors	Amir Erfan Eshratifar, Mohammad Saeed Abrishami, Massoud Pedram
Abstract	Deep learning models are being deployed in many mobile intelligent applications. End-side services, such as intelligent personal assistants, autonomous cars, and smart home services often employ either simple local models on the mobile or complex remote models on the cloud. However, recent studies have shown that partitioning the DNN computations between the mobile and cloud can increase the latency and energy efficiencies. In this paper, we propose an efficient, adaptive, and practical engine, JointDNN, for collaborative computation between a mobile device and cloud for DNNs in both inference and training phase. JointDNN not only provides an energy and performance efficient method of querying DNNs for the mobile side but also benefits the cloud server by reducing the amount of its workload and communications compared to the cloud-only approach. Given the DNN architecture, we investigate the efficiency of processing some layers on the mobile device and some layers on the cloud server. We provide optimization formulations at layer granularity for forward- and backward-propagations in DNNs, which can adapt to mobile battery limitations and cloud server load constraints and quality of service. JointDNN achieves up to 18 and 32 times reductions on the latency and mobile energy consumption of querying DNNs compared to the status-quo approaches, respectively.
Tasks
Published	2018-01-25
URL	https://arxiv.org/abs/1801.08618v2
PDF	https://arxiv.org/pdf/1801.08618v2.pdf
PWC	https://paperswithcode.com/paper/jointdnn-an-efficient-training-and-inference
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Predicting the Effects of News Sentiments on the Stock Market


Title	Predicting the Effects of News Sentiments on the Stock Market
Authors	Dev Shah, Haruna Isah, Farhana Zulkernine
Abstract	Stock market forecasting is very important in the planning of business activities. Stock price prediction has attracted many researchers in multiple disciplines including computer science, statistics, economics, finance, and operations research. Recent studies have shown that the vast amount of online information in the public domain such as Wikipedia usage pattern, news stories from the mainstream media, and social media discussions can have an observable effect on investors opinions towards financial markets. The reliability of the computational models on stock market prediction is important as it is very sensitive to the economy and can directly lead to financial loss. In this paper, we retrieved, extracted, and analyzed the effects of news sentiments on the stock market. Our main contributions include the development of a sentiment analysis dictionary for the financial sector, the development of a dictionary-based sentiment analysis model, and the evaluation of the model for gauging the effects of news sentiments on stocks for the pharmaceutical market. Using only news sentiments, we achieved a directional accuracy of 70.59% in predicting the trends in short-term stock price movement.
Tasks	Sentiment Analysis, Stock Market Prediction, Stock Price Prediction
Published	2018-12-11
URL	http://arxiv.org/abs/1812.04199v1
PDF	http://arxiv.org/pdf/1812.04199v1.pdf
PWC	https://paperswithcode.com/paper/predicting-the-effects-of-news-sentiments-on
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Deep Classification of Epileptic Signals


Title	Deep Classification of Epileptic Signals
Authors	David Ahmedt-Aristizabal, Clinton Fookes, Kien Nguyen, Sridha Sridharan
Abstract	Electrophysiological observation plays a major role in epilepsy evaluation. However, human interpretation of brain signals is subjective and prone to misdiagnosis. Automating this process, especially seizure detection relying on scalp-based Electroencephalography (EEG) and intracranial EEG, has been the focus of research over recent decades. Nevertheless, its numerous challenges have inhibited a definitive solution. Inspired by recent advances in deep learning, we propose a new classification approach for EEG time series based on Recurrent Neural Networks (RNNs) via the use of Long-Short Term Memory (LSTM) networks. The proposed deep network effectively learns and models discriminative temporal patterns from EEG sequential data. Especially, the features are automatically discovered from the raw EEG data without any pre-processing step, eliminating humans from laborious feature design task. We also show that, in the epilepsy scenario, simple architectures can achieve competitive performance. Using simple architectures significantly benefits in the practical scenario considering their low computation complexity and reduced requirement for large training datasets. Using a public dataset, a multi-fold cross-validation scheme exhibited an average validation accuracy of 95.54% and an average AUC of 0.9582 of the ROC curve among all sets defined in the experiment. This work reinforces the benefits of deep learning to be further attended in clinical applications and neuroscientific research.
Tasks	EEG, Seizure Detection, Time Series
Published	2018-01-11
URL	http://arxiv.org/abs/1801.03610v1
PDF	http://arxiv.org/pdf/1801.03610v1.pdf
PWC	https://paperswithcode.com/paper/deep-classification-of-epileptic-signals
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Label Sanitization against Label Flipping Poisoning Attacks


Title	Label Sanitization against Label Flipping Poisoning Attacks
Authors	Andrea Paudice, Luis Muñoz-González, Emil C. Lupu
Abstract	Many machine learning systems rely on data collected in the wild from untrusted sources, exposing the learning algorithms to data poisoning. Attackers can inject malicious data in the training dataset to subvert the learning process, compromising the performance of the algorithm producing errors in a targeted or an indiscriminate way. Label flipping attacks are a special case of data poisoning, where the attacker can control the labels assigned to a fraction of the training points. Even if the capabilities of the attacker are constrained, these attacks have been shown to be effective to significantly degrade the performance of the system. In this paper we propose an efficient algorithm to perform optimal label flipping poisoning attacks and a mechanism to detect and relabel suspicious data points, mitigating the effect of such poisoning attacks.
Tasks	data poisoning
Published	2018-03-02
URL	http://arxiv.org/abs/1803.00992v2
PDF	http://arxiv.org/pdf/1803.00992v2.pdf
PWC	https://paperswithcode.com/paper/label-sanitization-against-label-flipping
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Learning Incremental Triplet Margin for Person Re-identification


Title	Learning Incremental Triplet Margin for Person Re-identification
Authors	Yingying Zhang, Qiaoyong Zhong, Liang Ma, Di Xie, Shiliang Pu
Abstract	Person re-identification (ReID) aims to match people across multiple non-overlapping video cameras deployed at different locations. To address this challenging problem, many metric learning approaches have been proposed, among which triplet loss is one of the state-of-the-arts. In this work, we explore the margin between positive and negative pairs of triplets and prove that large margin is beneficial. In particular, we propose a novel multi-stage training strategy which learns incremental triplet margin and improves triplet loss effectively. Multiple levels of feature maps are exploited to make the learned features more discriminative. Besides, we introduce global hard identity searching method to sample hard identities when generating a training batch. Extensive experiments on Market-1501, CUHK03, and DukeMTMCreID show that our approach yields a performance boost and outperforms most existing state-of-the-art methods.
Tasks	Metric Learning, Person Re-Identification
Published	2018-12-17
URL	http://arxiv.org/abs/1812.06576v1
PDF	http://arxiv.org/pdf/1812.06576v1.pdf
PWC	https://paperswithcode.com/paper/learning-incremental-triplet-margin-for
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Kalman filter demystified: from intuition to probabilistic graphical model to real case in financial markets


Title	Kalman filter demystified: from intuition to probabilistic graphical model to real case in financial markets
Authors	Eric Benhamou
Abstract	In this paper, we revisit the Kalman filter theory. After giving the intuition on a simplified financial markets example, we revisit the maths underlying it. We then show that Kalman filter can be presented in a very different fashion using graphical models. This enables us to establish the connection between Kalman filter and Hidden Markov Models. We then look at their application in financial markets and provide various intuitions in terms of their applicability for complex systems such as financial markets. Although this paper has been written more like a self contained work connecting Kalman filter to Hidden Markov Models and hence revisiting well known and establish results, it contains new results and brings additional contributions to the field. First, leveraging on the link between Kalman filter and HMM, it gives new algorithms for inference for extended Kalman filters. Second, it presents an alternative to the traditional estimation of parameters using EM algorithm thanks to the usage of CMA-ES optimization. Third, it examines the application of Kalman filter and its Hidden Markov models version to financial markets, providing various dynamics assumptions and tests. We conclude by connecting Kalman filter approach to trend following technical analysis system and showing their superior performances for trend following detection.
Tasks
Published	2018-11-28
URL	http://arxiv.org/abs/1811.11618v2
PDF	http://arxiv.org/pdf/1811.11618v2.pdf
PWC	https://paperswithcode.com/paper/kalman-filter-demystified-from-intuition-to
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Label Denoising with Large Ensembles of Heterogeneous Neural Networks


Title	Label Denoising with Large Ensembles of Heterogeneous Neural Networks
Authors	Pavel Ostyakov, Elizaveta Logacheva, Roman Suvorov, Vladimir Aliev, Gleb Sterkin, Oleg Khomenko, Sergey I. Nikolenko
Abstract	Despite recent advances in computer vision based on various convolutional architectures, video understanding remains an important challenge. In this work, we present and discuss a top solution for the large-scale video classification (labeling) problem introduced as a Kaggle competition based on the YouTube-8M dataset. We show and compare different approaches to preprocessing, data augmentation, model architectures, and model combination. Our final model is based on a large ensemble of video- and frame-level models but fits into rather limiting hardware constraints. We apply an approach based on knowledge distillation to deal with noisy labels in the original dataset and the recently developed mixup technique to improve the basic models.
Tasks	Data Augmentation, Denoising, Video Classification, Video Understanding
Published	2018-09-12
URL	http://arxiv.org/abs/1809.04403v2
PDF	http://arxiv.org/pdf/1809.04403v2.pdf
PWC	https://paperswithcode.com/paper/label-denoising-with-large-ensembles-of
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Adversarial adaptive 1-D convolutional neural networks for bearing fault diagnosis under varying working condition


Title	Adversarial adaptive 1-D convolutional neural networks for bearing fault diagnosis under varying working condition
Authors	Bo Zhang, Wei Li, Jie Hao, Xiao-Li Li, Meng Zhang
Abstract	Traditional intelligent fault diagnosis of rolling bearings work well only under a common assumption that the labeled training data (source domain) and unlabeled testing data (target domain) are drawn from the same distribution. However, in many real-world applications, this assumption does not hold, especially when the working condition varies. In this paper, a new adversarial adaptive 1-D CNN called A2CNN is proposed to address this problem. A2CNN consists of four parts, namely, a source feature extractor, a target feature extractor, a label classifier and a domain discriminator. The layers between the source and target feature extractor are partially untied during the training stage to take both training efficiency and domain adaptation into consideration. Experiments show that A2CNN has strong fault-discriminative and domain-invariant capacity, and therefore can achieve high accuracy under different working conditions. We also visualize the learned features and the networks to explore the reasons behind the high performance of our proposed model.
Tasks	Domain Adaptation
Published	2018-05-01
URL	http://arxiv.org/abs/1805.00778v3
PDF	http://arxiv.org/pdf/1805.00778v3.pdf
PWC	https://paperswithcode.com/paper/adversarial-adaptive-1-d-convolutional-neural
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Dimensionality Reduction in Deep Learning for Chest X-Ray Analysis of Lung Cancer


Title	Dimensionality Reduction in Deep Learning for Chest X-Ray Analysis of Lung Cancer
Authors	Yu. Gordienko, Yu. Kochura, O. Alienin, O. Rokovyi, S. Stirenko, Peng Gang, Jiang Hui, Wei Zeng
Abstract	Efficiency of some dimensionality reduction techniques, like lung segmentation, bone shadow exclusion, and t-distributed stochastic neighbor embedding (t-SNE) for exclusion of outliers, is estimated for analysis of chest X-ray (CXR) 2D images by deep learning approach to help radiologists identify marks of lung cancer in CXR. Training and validation of the simple convolutional neural network (CNN) was performed on the open JSRT dataset (dataset #01), the JSRT after bone shadow exclusion - BSE-JSRT (dataset #02), JSRT after lung segmentation (dataset #03), BSE-JSRT after lung segmentation (dataset #04), and segmented BSE-JSRT after exclusion of outliers by t-SNE method (dataset #05). The results demonstrate that the pre-processed dataset obtained after lung segmentation, bone shadow exclusion, and filtering out the outliers by t-SNE (dataset #05) demonstrates the highest training rate and best accuracy in comparison to the other pre-processed datasets.
Tasks	Dimensionality Reduction
Published	2018-01-19
URL	http://arxiv.org/abs/1801.06495v1
PDF	http://arxiv.org/pdf/1801.06495v1.pdf
PWC	https://paperswithcode.com/paper/dimensionality-reduction-in-deep-learning-for
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