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Nodule Detection with Convolutional Neural Network Using Apache Spark and GPU Frameworks

Nikitha Johnsirani Venkatesan, Dong Ryeol Shin, Choon Sung Nam
2021 Applied Sciences  
Using abrupt radiation generates noise in CT scans.  ...  We propose an optimal Convolutional Neural Network model in which Gaussian noise is removed for better classification and increased training accuracy.  ...  This integrated mechanism, namely Gaussian noise aware autoencoder, can ensure accurate and reliable detection of lung nodules even in the presence of Gaussian noise.  ... 
doi:10.3390/app11062838 fatcat:axcyrh5aunga7i5iz465dpd7fe

Heterogeneous Ensemble-Based Spike-Driven Few-Shot Online Learning

Shuangming Yang, Bernabe Linares-Barranco, Badong Chen
2022 Frontiers in Neuroscience  
More importantly, the proposed HESFOL model emphasizes the application of modern entropy-based machine learning methods in state-of-the-art spike-driven learning algorithms.  ...  Therefore, our study provides new perspectives for further integration of advanced entropy theory in machine learning to improve the learning performance of SNNs, which could be of great merit to applied  ...  RESULTS Details of the Few-Shot Learning Performance on Spike Patterns With Non-Gaussian Noise In the first task, spiking patterns with the non-Gaussian noise are used to test the few-shot learning  ... 
doi:10.3389/fnins.2022.850932 pmid:35615277 pmcid:PMC9124799 fatcat:walcddjcgjaydmuakhleclh32m

A loss function for classification based on a robust similarity metric

Abhishek Singh, Jose C. Principe
2010 The 2010 International Joint Conference on Neural Networks (IJCNN)  
in the presence of an impulsive observation noise, which can be simulated using a mixture of Gaussians: 0.95N (0, 10 −4 ) + 0.05N (0, 10) (3-21) Clearly, in this density, the Gaussian distribution with  ...  The MEE criterion also exhibits similar characteristics in presence of impulsive noise.  ... 
doi:10.1109/ijcnn.2010.5596485 dblp:conf/ijcnn/SinghP10 fatcat:vouc55gzordjba6pee7px3hmby

Underwater Vehicle Positioning by Correntropy-Based Fuzzy Multi-Sensor Fusion

Nabil Shaukat, Muhammad Moinuddin, Pablo Otero
2021 Sensors  
the presence of outliers.  ...  These non-Gaussian outliers are difficult to handle with conventional Kalman-based methods and their fuzzy variants.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s21186165 pmid:34577372 fatcat:uetg2ibv3raibot5mrja7r45la

An Introduction to Information Theoretic Learning, Part II: Applications

Daniel Silva, Denis Fantinato, Janio Canuto, Leonardo Duarte, Aline Neves, Ricardo Suyama, Jugurta Montalvão, Romis Attux
2016 Journal of Communication and Information Systems  
This is the second part of the introductory tutorial about information theoretic learning, which, after the theoretical foundations presented in Part I, now discusses the concepts of correntropy, a new  ...  However, with respect to non-classical scenarios, e.g., for sparse / correlated signals or even in presence of non-Gaussian noise, the same assertion cannot be hold.  ...  On the other hand, MSE-based estimation is biased if the noise PDF has non-zero mean, which leads to performance degradation when in the presence of impulsive noise.  ... 
doi:10.14209/jcis.2016.7 fatcat:fjsomfgggfglvcpkklr4jvmrne

A Robust Matching Pursuit Algorithm Using Information Theoretic Learning [article]

Miaohua Zhang, Yongsheng Gao, Changming Sun, Michael Blumenstein
2020 arXiv   pre-print
; (2) a non-second order statistic measurement and minimization method is developed to improve the robustness of OMP by overcoming the limitation of Gaussianity inherent in cost function based on second-order  ...  noises or outliers in the observation data.  ...  Acknowledgements This work is supported in part by Australian Research Council (ARC) under Discovery Grants DP140101075.  ... 
arXiv:2005.04541v1 fatcat:lbicglqc7fdhfhui4lpdt5227i

Active Coefficient Detection Maximum Correntropy Criterion Algorithm For Sparse Channel Estimation Under Non-Gaussian Environments

Zeyang Sun, Yingsong Li, Zhengxiong Jiang, Wanlu Shi
2019 IEEE Access  
In this paper, a kind of active coefficient detection (ACD)-based maximum correntropy criterion (MCC) algorithm is proposed to estimate a sparse multi-path channel under the non-Gaussian environments.  ...  Various computer simulation experiments are carried out to investigate the performance of the proposed ACD-based MCC algorithms under different impulsive noises.  ...  THE PERFORMANCE OF THE PROPOSED ACD-BASED MCC ALGORITHMS IN STUDENT.T DISTRIBUTION NOISE ENVIRONMENT Another non-Gaussian noise environment using Student.  ... 
doi:10.1109/access.2019.2924028 fatcat:rcxjn7jxd5fovn6fjqn3kvwv4q

Table of Contents [Edics]

2019 IEEE Signal Processing Letters  
Nehorai838 Non-Iterative Subspace-Based DOA Estimation in the Presence of Nonuniform Noise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Cevher 883 Cyclic Frequency Estimation by Compressed Cyclic Correntropy Spectrum in Impulsive Noise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/lsp.2019.2912093 fatcat:eczpj36ahjbcbcombxu5zqd4de

Robust Self-Supervised Convolutional Neural Network for Subspace Clustering and Classification [article]

Dario Sitnik, Ivica Kopriva
2020 arXiv   pre-print
S^2ConvSCN clusters data coming from nonlinear manifolds by learning the linear self-representation model in the feature space.  ...  Robustness to data corruptions is achieved by using the correntropy induced metric (CIM) of the error.  ...  We gratefully acknowledge the support of NVIDIA Corporation with the donation of the QUADRO P6000 GPU used for this research.  ... 
arXiv:2004.03375v1 fatcat:a65leimhzvbs3gpwllxgxaoo5m

2020 Index IEEE Signal Processing Letters Vol. 27

2020 IEEE Signal Processing Letters  
., +, LSP 2020 1045-1049 Estimating the Number of Sinusoids in Additive Sub-Gaussian Noise With Finite Measurements.  ...  Tian, L., +, LSP 2020 1974-1978 Estimating the Number of Sinusoids in Additive Sub-Gaussian Noise With Finite Measurements.  ... 
doi:10.1109/lsp.2021.3055468 fatcat:wfdtkv6fmngihjdqultujzv4by

A Unified Weight Learning and Low-Rank Regression Model for Robust Complex Error Modeling [article]

Miaohua Zhang, Yongsheng Gao, Jun Zhou
2020 arXiv   pre-print
For the random noise, we define a generalized correntropy (GC) function to match the error distribution.  ...  One of the most important problems in regression-based error model is modeling the complex representation error caused by various corruptions and environment changes in images.  ...  Acknowledgments This work is supported in part by the Industrial Transformation Research Hub Grant IH180100002.  ... 
arXiv:2005.04619v4 fatcat:5asvj7gnjbdknjnyxudr5arer4

Table of Contents

2020 IEEE Signal Processing Letters  
Dedecius and R.Žemlička 625 Statistical Behavior of Teager-Kaiser Energy Operator in Presence of White Gaussian Noise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Yang 1065 A Multi-Target Track-Before-Detect Particle Filter Using Superpositional Data in Non-Gaussian Noise .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Frayne 141 Generalized Zero-Shot Learning With Multi-Channel Gaussian Mixture VAE . . . . . . . . . . . . . . . . . . . . . . . J. Shao and X.  ... 
doi:10.1109/lsp.2020.3040844 fatcat:xpovskhrvfgctk3hhufuvpyyne

2020 Index IEEE/ACM Transactions on Audio, Speech, and Language Processing Vol. 28

2020 IEEE/ACM Transactions on Audio Speech and Language Processing  
., +, TASLP 2020 876-888 Deep learning On Cross-Corpus Generalization of Deep Learning Based Speech Enhancement.  ...  ., +, TASLP 2020 225-239 Robust Generalized Maximum Correntropy Criterion Algorithms for Active Noise Control.  ...  T Target tracking Multi-Hypothesis Square-Root Cubature Kalman Particle Filter for Speaker Tracking in Noisy and Reverberant Environments. Zhang, Q., +, TASLP 2020 1183 -1197  ... 
doi:10.1109/taslp.2021.3055391 fatcat:7vmstynfqvaprgz6qy3ekinkt4

Table of contents

2021 IEEE Communications Letters  
Sun 1206 Deep Learning for MMSE Estimation of a Gaussian Source in the Presence of Bursty Impulsive Noise ............... .................................................................... I.  ...  Zhong 1353 Performance Analysis of Maximum-Correntropy Based Detection for SCMA .............................................. ........................................................................  ... 
doi:10.1109/lcomm.2021.3067248 fatcat:y47u64lpjnd3detsj6drkayhuy

A Marked Point Process Framework for Extracellular Electrical Potentials

Carlos A. Loza, Michael S. Okun, José C. Príncipe
2017 Frontiers in Systems Neuroscience  
The cost function incorporates a robust estimation component based on correntropy to mitigate the outliers caused by the inherent noise in the EEP.  ...  Lastly, the background EEP activity is explicitly modeled as the non-sparse component of the collected signal to further improve the delineation of the multi-frequency phasic events in time.  ...  The robust correntropy-based SVD technique utilizes the Gaussian kernel in order to go beyond the benchmark of secondorder statistical moments.  ... 
doi:10.3389/fnsys.2017.00095 pmid:29326562 pmcid:PMC5741641 fatcat:4dnzgq3stfhxrkypffsfqqjmzq
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