检索结果-内蒙古大学图书馆

Adversarial learning with Cost-Sensitive Classes

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Shen, Haojing Chen, Sihong Wang, Ran Wang, Xizhao Big Data Institute College of Computer Science and Software Engineering Guangdong Key Lab. of Intelligent Information Processing Shenzhen University Guangdong Shenzhen518060 China The College of Mathematics and Statistics Shenzhen University Shenzhen518060 China The Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen University Shenzhen518060 China

It is necessary to improve the performance of some special classes or to particularly protect them from attacks in adversarial learning. This paper proposes a framework combining cost-sensitive classification and adversarial learning together to train a model that can distinguish between protected and unprotected classes, such that the protected classes are less vulnerable to adversarial examples. We find in this framework an interesting phenomenon during the training of deep neural networks, called Min-Max property, that is, the absolute values of most parameters in the convolutional layer approach zero while the absolute values of a few parameters are significantly larger becoming bigger. Based on this Min-Max property which is formulated and analyzed in a view of random distribution, we further build a new defense model against adversarial examples for adversarial robustness improvement. An advantage of the built model is that it performs better than the standard one and can combine with adversarial training to achieve an improved performance. It is experimentally confirmed that, regarding the average accuracy of all classes, our model is almost as same as the existing models when an attack does not occur and is better than the existing models when an attack occurs. Specifically, regarding the accuracy of protected classes, the proposed model is much better than the existing models when an attack occurs. © 2021, CC BY.

关键词： Deep neural networks

Comparative performance analysis of the resnet backbones of mask RCNN to segment the signs of COVID-19 in chest CT scans

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Aleem, Muhammad Raj, Rahul Khan, Arshad Ghulam Ishaq Khan Institute of Engineering Sciences and Technology Topi KPK 23640 Pakistan Independent Researcher Machine Learning in NLP and Computer Vision Data Science professional Southampton United Kingdom

COVID-19 has been detrimental in terms of the number of fatalities and rising number of critical patients across the world. According to the UNDP (United National Development Programme) Socio-Economic programme, aimed at the COVID-19 crisis, the pandemic is far more than a health crisis: it is affecting societies and economies at their core 2. There has been greater developments recently in the chest X-ray-based imaging technique as part of the COVID-19 diagnosis especially using Convolution Neural Networks (CNN) for recognising and classifying images. However, given the limitation of supervised labelled imaging data, the classification and predictive risk modelling of medical diagnosis tend to compromise. This paper aims to identify and monitor the effects of COVID-19 on the human lungs by employing Deep Neural Networks on axial CT (Chest Computed Tomography) scan of lungs. We have adopted Mask RCNN, with ResNet50 and ResNet101 as its backbone, to segment the regions, affected by COVID-19 coronavirus. Using the regions of human lungs, where symptoms have manifested, the model classifies condition of the patient as either "Mild" or "Alarming". Moreover, the model is deployed on the Google Cloud Platform (GCP) to simulate the online usage of the model for performance evaluation and accuracy improvement. The ResNet101 backbone model produces an F1 score of 0.85 and faster prediction scores with an average time of 9.04 seconds per inference. Copyright © 2020, The Authors. All rights reserved.

关键词： Computer aided diagnosis

Towards DMC accuracy across chemical space with scalable ∆-QML

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Huang, Bing von Lilienfeld, O. Anatole Krogel, Jaron T. Benali, Anouar University of Vienna Faculty of Physics Kolingasse 14-16 Vienna1090 Austria Departments of Chemistry Materials Science and Engineering and Physics University of Toronto St. George Campus TorontoON Canada Vector Institute for Artificial Intelligence TorontoONM5S 1M1 Canada Machine Learning Group Technische Universität Berlin Institute for the Foundations of Learning and Data Berlin10587 Germany Materials Science and Technology Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Computational Sciences Division Argonne National Laboratory ArgonneIL60439 United States

In the past decade, quantum diffusion Monte Carlo (DMC) has been demonstrated to successfully predict the energetics and properties of a wide range of molecules and solids by numerically solving the electronic many-body Schrödinger equation. We show that when coupled with quantum machine learning (QML) based surrogate methods the computational burden can be alleviated such that QMC shows clear potential to under-gird the formation of high quality descriptions across chemical space. We discuss three crucial approximations necessary to accomplish this: The fixed node approximation, universal and accurate references for chemical bond dissociation energies, and scalable minimal amons set based QML (AQML) models. Numerical evidence presented includes converged DMC results for over one thousand small organic molecules with up to 5 heavy atoms used as amons, and 50 medium sized organic molecules with 9 heavy atoms to validate the AQML predictions. Numerical evidence collected for ∆-AQML models suggests that already modestly sized QMC training data sets of amons suffice to predict total energies with near chemical accuracy throughout chemical space. Copyright © 2022, The Authors. All rights reserved.

关键词： Forecasting

Reinforcement learning in continuous time and space: a stochastic control approach

学校读者我要写书评

暂无评论

The Journal of Machine Learning Research

The Journal of machine learning Research 2020年第1期21卷 8145-8178页

作者： Haoran Wang Thaleia Zariphopoulou Xun Yu Zhou CAI Data Science and Machine Learning The Vanguard Group Inc. Malvern PA Department of Mathematics and IROM The University of Texas at Austin Austin TX and Oxford-Man Institute University of Oxford Oxford UK Department of Industrial Engineering and Operations Research The Data Science Institute Columbia University New York NY

We consider reinforcement learning (RL) in continuous time with continuous feature and action spaces. We motivate and devise an exploratory formulation for the feature dynamics that captures learning under exploration, with the resulting optimization problem being a revitalization of the classical relaxed stochastic control. We then study the problem of achieving the best trade-off between exploration and exploitation by considering an entropy-regularized reward function. We carry out a complete analysis of the problem in the linear-quadratic (LQ) setting and deduce that the optimal feedback control distribution for balancing exploitation and exploration is Gaussian. This in turn interprets the widely adopted Gaussian exploration in RL, beyond its simplicity for sampling. Moreover, the exploitation and exploration are captured respectively by the mean and variance of the Gaussian distribution. We characterize the cost of exploration, which, for the LQ case, is shown to be proportional to the entropy regularization weight and inversely proportional to the discount rate. Finally, as the weight of exploration decays to zero, we prove the convergence of the solution of the entropy-regularized LQ problem to the one of the classical LQ problem.

关键词： reinforcement learning entropy regularization stochastic control relaxed control linear-quadratic Gaussian distribution

Distributional Drift Adaptation with Temporal Conditional Variational Autoencoder for Multivariate Time Series Forecasting

学校读者我要写书评

暂无评论

arXiv 2022年

作者： He, Hui Zhang, Qi Yi, Kun Shi, Kaize Niu, Zhendong Cao, Longbing The School of Medical Technology Beijing Institute of Technology Beijing100081 China The School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China The Department of Computer Science Tongji University Shanghai201804 China The Data Science and Machine Intelligence Lab University of Technology SydneyNSW2007 Australia The Engineering Research Center of Integration and Application of Digital Learning Technology Ministry of Education China The DataX Research Centre School of Computing Macquarie University SydneyNSW2109 Australia

Due to the non-stationary nature, the distribution of real-world multivariate time series (MTS) changes over time, which is known as distribution drift. Most existing MTS forecasting models greatly suffer from distribution drift and degrade the forecasting performance over time. Existing methods address distribution drift via adapting to the latest arrived data or self-correcting per the meta knowledge derived from future data. Despite their great success in MTS forecasting, these methods hardly capture the intrinsic distribution changes, especially from a distributional perspective. Accordingly, we propose a novel framework temporal conditional variational autoencoder (TCVAE) to model the dynamic distributional dependencies over time between historical observations and future data in MTSs and infer the dependencies as a temporal conditional distribution to leverage latent variables. Specifically, a novel temporal Hawkes attention mechanism represents temporal factors subsequently fed into feed-forward networks to estimate the prior Gaussian distribution of latent variables. The representation of temporal factors further dynamically adjusts the structures of Transformer-based encoder and decoder to distribution changes by leveraging a gated attention mechanism. Moreover, we introduce conditional continuous normalization flow to transform the prior Gaussian to a complex and form-free distribution to facilitate flexible inference of the temporal conditional distribution. Extensive experiments conducted on six real-world MTS datasets demonstrate the TCVAE’s superior robustness and effectiveness over the state-of-the-art MTS forecasting baselines. We further illustrate the TCVAE applicability through multifaceted case studies and visualization in real-world *** Codes 68Txx Copyright © 2022, The Authors. All rights reserved.

关键词： Forecasting

Transformer-based normative modelling for anomaly detection of early schizophrenia

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Da Costa, Pedro F. Dafflon, Jessica Mendes, Sergio Leonardo Sato, João Ricardo Jorge Cardoso, M. Leech, Robert Jones, Emily J.H. Pinaya, Walter H.L. Institute of Psychiatry Psychology & Neuroscience King’s College London United Kingdom Centre for Brain and Cognitive Development Birkbeck College London United Kingdom Data Science and Sharing Team National Institute of Mental Health BethesdaMD United States Machine Learning Team National Institute of Mental Health BethesdaMD United States Center of Mathematics Computing and Cognition Universidade Federal do ABC Brazil School of Biomedical Engineering & Imaging Sciences King’s College London United Kingdom

Despite the impact of psychiatric disorders on clinical health, early-stage diagnosis remains a challenge. machine learning studies have shown that classifiers tend to be overly narrow in the diagnosis prediction task. The overlap between conditions leads to high heterogeneity among participants that is not adequately captured by classification models. To address this issue, normative approaches have surged as an alternative method. By using a generative model to learn the distribution of healthy brain data patterns, we can identify the presence of pathologies as deviations or outliers from the distribution learned by the model. In particular, deep generative models showed great results as normative models to identify neurological lesions in the brain. However, unlike most neurological lesions, psychiatric disorders present subtle changes widespread in several brain regions, making these alterations challenging to identify. In this work, we evaluate the performance of transformer-based normative models to detect subtle brain changes expressed in adolescents and young adults. We trained our model on 3D MRI scans of neurotypical individuals (N=1,765). Then, we obtained the likelihood of neurotypical controls and psychiatric patients with early-stage schizophrenia from an independent dataset (N=93) from the Human Connectome Project. Using the predicted likelihood of the scans as a proxy for a normative score, we obtained an AUROC of 0.82 when assessing the difference between controls and individuals with early-stage schizophrenia. Our approach surpassed recent normative methods based on brain age and Gaussian Process, showing the promising use of deep generative models to help in individualised analyses. © 2022, CC BY.

关键词： Brain

Optimising knee injury detection with spatial attention and validating localisation ability

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Belton, Niamh Welaratne, Ivan Dahlan, Adil Hearne, Ronan T. Hagos, Misgina Tsighe Lawlor, Aonghus Curran, Kathleen M. Science Foundation Ireland Centre for Research Training in Machine Learning School of Medicine University College Dublin Department of Radiology Mater Misericordiae University Hospital Dublin Ireland School of Electronic Engineering University College Dublin School of Computer Science University College Dublin Insight Centre for Data Analytics University College Dublin Dublin Ireland

This work employs a pre-trained, multi-view Convolutional Neural Network (CNN) with a spatial attention block to optimise knee injury detection. An open-source Magnetic Resonance Imaging (MRI) data set with image-level labels was leveraged for this analysis. As MRI data is acquired from three planes, we compare our technique using data from a single-plane and multiple planes (multi-plane). For multi-plane, we investigate various methods of fusing the planes in the network. This analysis resulted in the novel 'MPFuseNet' network and state-of-the-art Area Under the Curve (AUC) scores for detecting Anterior Cruciate Ligament (ACL) tears and Abnormal MRIs, achieving AUC scores of 0.977 and 0.957 respectively. We then developed an objective metric, Penalised Localisation Accuracy (PLA), to validate the model's localisation ability. This metric compares binary masks generated from Grad-Cam output and the radiologist's annotations on a sample of MRIs. We also extracted explainability features in a model-agnostic approach that were then verified as clinically relevant by the radiologist. Copyright © 2021, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

VAE-GAN Based Zero-shot Outlier Detection 2020

学校读者我要写书评

暂无评论

VAE-GAN Based Zero-shot Outlier Detection

Proceedings of the 2020 4th International Symposium on Computer Science and Intelligent Control

作者： Bekkouch Imad Ibrahim Dragoş Constantin Nicolae Adil Khan Syed Imran Ali Asad Khattak Machine Learning and Knowledge Representation Lab Institute of Data Science & AI Innopolis Tatarstan Russia Institutul de cercetări pentru Inteligenta Artificiala 'Mihai Draganescu' România Department of Computer Engineering Kyung Hee University Yongin-si South Korea College of Technological Innovations Zayed University Abu Dhabi United Arab Emirates

ISBN: (纸本)9781450388894

Outlier detection is one of the main fields in machine learning and it has been growing rapidly due to its wide range of applications. In the last few years, deep learning-based methods have outperformed machine learning and handcrafted outlier detection techniques, and our method is no different. We present a new twist to generative models which leverages variational autoencoders as a source for uniform distributions which can be used to separate the inliers from the outliers. Both the generative and adversarial parts of the model are used to obtain three main losses (Reconstruction loss, KL-divergence, Discriminative loss) which in return are wrapped with a one-class SVM which is used to make the predictions. We evaluated our method against several datasets both for images and tabular data and it has shown great results for the zero-shot outlier detection problem and was able to easily generalize it for supervised outlier detection tasks on which the performance has increased. For comparison, we evaluated our method against several of the common outlier detection techniques such as DBSCAN-based outlier detection, GMM, K-means and one class SVM directly, and we have outperformed all of them on all datasets.

关键词： Deep learning Generative Models machine learning Outlier Detection

Network security management and implementation based on ACL

学校读者我要写书评

暂无评论

Network security management and implementation based on ACL

International Conference on Applications and Techniques in Cyber Intelligence, ATCI 2018

作者： Feng, Mengqing College of Computer and Information Engineering Zhengzhou University of Industrial Technology ZhengzhouHenan451150 China Machine Learning and Data Researching Institute Zhengzhou University of Industrial Technology ZhengzhouHenan451150 China

ISBN: (纸本)9783319987750

At present, small and medium-sized enterprises demand for the confidentiality of the information network is more and more high, in view of the network protection company to provide professional information security technology and equipment of the disadvantages of high cost, stronger by using Access Control lists (Access Control List, ACL) technology as an independent security technology of network security management requirements in terms of design, focus on the router using ACL technology to protect sensitive data in the network, through the demand analysis to build the network topological structure of the simulation diagram, in the process of implementing the main command, finally based on the needs of specific testing network, more intuitive reflect the ACL network protection fast implementation in small and medium-sized enterprises and the characteristics of its low cost high practicability. © Springer Nature Switzerland AG 2019.

关键词： Network security