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Dying ReLU and Initialization: Theory and Numerical Examples

Dying ReLU and Initialization: Theory and Numerical Examples
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Year:    2020

Author:    Lu Lu, Yeonjong Shin, Yanhui Su, George Em Karniadakis

Communications in Computational Physics, Vol. 28 (2020), Iss. 5 : pp. 1671–1706

Abstract

The dying ReLU refers to the problem when ReLU neurons become inactive and only output 0 for any input. There are many empirical and heuristic explanations of why ReLU neurons die. However, little is known about its theoretical analysis. In this paper, we rigorously prove that a deep ReLU network will eventually die in probability as the depth goes to infinite. Several methods have been proposed to alleviate the dying ReLU. Perhaps, one of the simplest treatments is to modify the initialization procedure. One common way of initializing weights and biases uses symmetric probability distributions, which suffers from the dying ReLU. We thus propose a new initialization procedure, namely, a randomized asymmetric initialization. We show that the new initialization can effectively prevent the dying ReLU. All parameters required for the new initialization are theoretically designed. Numerical examples are provided to demonstrate the effectiveness of the new initialization procedure.

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Journal Article Details

Publisher Name:    Global Science Press

Language:    English

DOI:    https://doi.org/10.4208/cicp.OA-2020-0165

Communications in Computational Physics, Vol. 28 (2020), Iss. 5 : pp. 1671–1706

Published online:    2020-01

AMS Subject Headings:    Global Science Press

Copyright:    COPYRIGHT: © Global Science Press

Pages:    36

Keywords:    Neural network Dying ReLU Vanishing/Exploding gradient Randomized asymmetric initialization.

Author Details

Lu Lu

Yeonjong Shin

Yanhui Su

George Em Karniadakis

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    https://doi.org/10.1016/j.future.2021.07.002 [Citations: 12]

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    Whitaker, Tim | Whitley, Darrell

    2023 International Joint Conference on Neural Networks (IJCNN), (2023), P.1

    https://doi.org/10.1109/IJCNN54540.2023.10191397 [Citations: 1]

  75. An Audio Signal-based COVID-19 Detection Methodology using Modified DenseNet121

    Saha, Shrabana | Bhadra, Rajarshi | Kar, Subhajit

    2021 IEEE 18th India Council International Conference (INDICON), (2021), P.1

    https://doi.org/10.1109/INDICON52576.2021.9691766 [Citations: 2]

  76. Improved weight initialization for deep and narrow feedforward neural network

    Lee, Hyunwoo | Kim, Yunho | Yang, Seung Yeop | Choi, Hayoung

    Neural Networks, Vol. 176 (2024), Iss. P.106362

    https://doi.org/10.1016/j.neunet.2024.106362 [Citations: 1]

  77. Attention-Based Neural Network for Cardiac MRI Segmentation: Application to Strain and Volume Computation

    Portal, Nicolas | Achard, Catherine | Khan, Saud | Nguyen, Vincent | Prigent, Mikael | Zarai, Mohamed | Bouazizi, Khaoula | Sylvain, Johanne | Redheuil, Alban | Montalescot, Gilles | Kachenoura, Nadjia | Dietenbeck, Thomas

    IRBM, Vol. 45 (2024), Iss. 4 P.100850

    https://doi.org/10.1016/j.irbm.2024.100850 [Citations: 0]

  78. Development and validation of a two-stage convolutional neural network algorithm for segmentation of MRI white matter hyperintensities for longitudinal studies in CADASIL

    Demeusy, Valentin | Roche, Florent | Vincent, Fabrice | Taha, May | Zhang, Ruiting | Jouvent, Eric | Chabriat, Hugues | Lebenberg, Jessica

    Computers in Biology and Medicine, Vol. 180 (2024), Iss. P.108936

    https://doi.org/10.1016/j.compbiomed.2024.108936 [Citations: 0]

  79. Empirical study of the modulus as activation function in computer vision applications

    Vallés-Pérez, Iván | Soria-Olivas, Emilio | Martínez-Sober, Marcelino | Serrano-López, Antonio J. | Vila-Francés, Joan | Gómez-Sanchís, Juan

    Engineering Applications of Artificial Intelligence, Vol. 120 (2023), Iss. P.105863

    https://doi.org/10.1016/j.engappai.2023.105863 [Citations: 8]
  80. Computational Intelligence in Healthcare Applications

    An intelligent diagnostic technique using deep convolutional neural network

    Saha, Shrabana | Bhadra, Rajarshi | Kar, Subhajit

    2022

    https://doi.org/10.1016/B978-0-323-99031-8.00021-1 [Citations: 0]

  81. Three‐Dimensional Permeability Inversion Using Convolutional Neural Networks and Positron Emission Tomography

    Huang, Zitong | Kurotori, Takeshi | Pini, Ronny | Benson, Sally M. | Zahasky, Christopher

    Water Resources Research, Vol. 58 (2022), Iss. 3

    https://doi.org/10.1029/2021WR031554 [Citations: 11]

  82. Multipeak Wavelength Detection of Spectrally Overlapped Fiber Bragg Grating Sensors Through a CNN-Based Autoencoder

    Rudloff, Gabriel | Soto, Marcelo A.

    IEEE Sensors Journal, Vol. 24 (2024), Iss. 13 P.20674

    https://doi.org/10.1109/JSEN.2024.3400819 [Citations: 0]

  83. Image Neural Network Classifier by Detail Attributes

    Sai, S. V. | Nikonov, V. S.

    Pattern Recognition and Image Analysis, Vol. 33 (2023), Iss. 2 P.179

    https://doi.org/10.1134/S1054661823020116 [Citations: 0]
  84. Lecture Notes in Computational Intelligence and Decision Making

    Tunable Activation Functions for Deep Neural Networks

    Bilonoh, Bohdan | Bodyanskiy, Yevgeniy | Kolchygin, Bohdan | Mashtalir, Sergii

    2022

    https://doi.org/10.1007/978-3-030-82014-5_43 [Citations: 1]

  85. A Deep Convolutional Autoencoder Architecture for Automatic Image Colorization

    Cevallos, Stefano | Perez, Noel | Riofrio, Daniel | Benitez, Diego | Moyano, Ricardo Flores | Baldeon-Calisto, Maria

    2022 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI), (2022), P.1

    https://doi.org/10.1109/ColCACI56938.2022.9905295 [Citations: 1]

  86. The Phenomenon of Correlated Representations in Contrastive Learning

    Klüttermann, Simon | Rutinowski, Jérôme | Müller, Emmanuel

    2024 International Joint Conference on Neural Networks (IJCNN), (2024), P.1

    https://doi.org/10.1109/IJCNN60899.2024.10649913 [Citations: 0]

  87. Less is More? Reducing Biases and Overfitting in Machine Learning Return Predictions

    Howard, Clint

    SSRN Electronic Journal, Vol. (2023), Iss.

    https://doi.org/10.2139/ssrn.4497739 [Citations: 0]

  88. DREAM: Debugging and Repairing AutoML Pipelines

    Zhang, Xiaoyu | Zhai, Juan | Ma, Shiqing | Guan, Xiaohong | Shen, Chao

    ACM Transactions on Software Engineering and Methodology, Vol. (2024), Iss.

    https://doi.org/10.1145/3702992 [Citations: 0]

  89. Segmenting 3D geometry of left coronary artery from coronary CT angiography using deep learning for hemodynamic evaluation

    Sadid, Sadman R | Kabir, Mohammed S | Mahmud, Samreen T | Islam, Md Saiful | Islam, A H M Waliul | Arafat, M Tarik

    Biomedical Physics & Engineering Express, Vol. 8 (2022), Iss. 6 P.065033

    https://doi.org/10.1088/2057-1976/ac9e03 [Citations: 0]

  90. Knowledge Graph enhanced Aspect-Based Sentiment Analysis Incorporating External Knowledge

    Teo, Autumn | Wang, Zhaoxia | Pen, Haibo | Subagdja, Budhitama | Ho, Seng-Beng | Quek, Boon Kiat

    2023 IEEE International Conference on Data Mining Workshops (ICDMW), (2023), P.791

    https://doi.org/10.1109/ICDMW60847.2023.00107 [Citations: 3]

  91. Operationalising fairness in medical AI adoption: detection of early Alzheimer’s disease with 2D CNN

    Heising, Luca | Angelopoulos, Spyros

    BMJ Health & Care Informatics, Vol. 29 (2022), Iss. 1 P.e100485

    https://doi.org/10.1136/bmjhci-2021-100485 [Citations: 8]

  92. Loss of plasticity in deep continual learning

    Dohare, Shibhansh | Hernandez-Garcia, J. Fernando | Lan, Qingfeng | Rahman, Parash | Mahmood, A. Rupam | Sutton, Richard S.

    Nature, Vol. 632 (2024), Iss. 8026 P.768

    https://doi.org/10.1038/s41586-024-07711-7 [Citations: 2]

  93. CTPlantNet: A Hybrid CNN-Transformer Architecture for Plant Disease Classification

    Nasser, Adnane Ait | Akhloufi, Moulay A.

    2022 International Conference on Microelectronics (ICM), (2022), P.156

    https://doi.org/10.1109/ICM56065.2022.10005433 [Citations: 1]
  94. Image Analysis and Processing – ICIAP 2022

    Improving Autoencoder Training Performance for Hyperspectral Unmixing with Network Reinitialisation

    Książek, Kamil | Głomb, Przemysław | Romaszewski, Michał | Cholewa, Michał | Grabowski, Bartosz | Búza, Krisztián

    2022

    https://doi.org/10.1007/978-3-031-06427-2_33 [Citations: 1]

  95. Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators

    Lu, Lu | Jin, Pengzhan | Pang, Guofei | Zhang, Zhongqiang | Karniadakis, George Em

    Nature Machine Intelligence, Vol. 3 (2021), Iss. 3 P.218

    https://doi.org/10.1038/s42256-021-00302-5 [Citations: 952]

  96. N-AquaRAM: A Cost-Efficient Deep Learning Accelerator for Real-Time Aquaponic Monitoring

    Siddique, Ali | Iqbal, Muhammad Azhar | Sun, Jingqi | Zhang, Xu | Vai, Mang I. | Siddique, Sunbal

    Agricultural Research, Vol. (2024), Iss.

    https://doi.org/10.1007/s40003-024-00788-6 [Citations: 0]

  97. Polynomial ridge flowfield estimation

    Scillitoe, A. | Seshadri, P. | Wong, C. Y. | Duncan, A.

    Physics of Fluids, Vol. 33 (2021), Iss. 12

    https://doi.org/10.1063/5.0064000 [Citations: 3]

  98. Breast Cancer Detection Using SEResNeXt and Swin Transformer

    Talakola, Sashank | Pattupogula, Devi Vara Prasad | Vobugari, Raja Karthik | Ummadisetty, Balaji

    2023 International Conference on Electrical, Electronics, Communication and Computers (ELEXCOM), (2023), P.1

    https://doi.org/10.1109/ELEXCOM58812.2023.10370058 [Citations: 0]

  99. Less Is More: Adaptive Trainable Gradient Dropout for Deep Neural Networks

    Avgerinos, Christos | Vretos, Nicholas | Daras, Petros

    Sensors, Vol. 23 (2023), Iss. 3 P.1325

    https://doi.org/10.3390/s23031325 [Citations: 3]

  100. Recognize the surrounding: Development and evaluation of convolutional deep networks using gammatone spectrograms and raw audio signals

    Sen Gupta, Shantanu | Hossain, Shifat | Kim, Ki-Doo

    Expert Systems with Applications, Vol. 200 (2022), Iss. P.116998

    https://doi.org/10.1016/j.eswa.2022.116998 [Citations: 2]

  101. Delve Into Neural Activations: Toward Understanding Dying Neurons

    Jiang, Ziping | Wang, Yunpeng | Li, Chang-Tsun | Angelov, Plamen | Jiang, Richard

    IEEE Transactions on Artificial Intelligence, Vol. 4 (2023), Iss. 4 P.959

    https://doi.org/10.1109/TAI.2022.3180272 [Citations: 2]

  102. Serf: Towards better training of deep neural networks using log-Softplus ERror activation Function

    Nag, Sayan | Bhattacharyya, Mayukh | Mukherjee, Anuraag | Kundu, Rohit

    2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), (2023), P.5313

    https://doi.org/10.1109/WACV56688.2023.00529 [Citations: 5]

  103. XDeMo: a novel deep learning framework for DNA motif mining using transformer models

    Chaurasia, Rajashree | Ghose, Udayan

    Network Modeling Analysis in Health Informatics and Bioinformatics, Vol. 13 (2024), Iss. 1

    https://doi.org/10.1007/s13721-024-00463-4 [Citations: 0]

  104. Combining data assimilation and machine learning to estimate parameters of a convective‐scale model

    Legler, S. | Janjić, T.

    Quarterly Journal of the Royal Meteorological Society, Vol. 148 (2022), Iss. 743 P.860

    https://doi.org/10.1002/qj.4235 [Citations: 10]

  105. WITHDRAWN: Novel machine learning models for flow imaging microscopy sub-visible particle classification in protein formulations

    Bassett, Robert | Mehta, Dharmini | Thompson, Scott | Al-Imarah, Emad

    International Journal of Pharmaceutics, Vol. (2023), Iss. P.123192

    https://doi.org/10.1016/j.ijpharm.2023.123192 [Citations: 0]

  106. Active Neuron Least Squares: A Training Method for Multivariate Rectified Neural Networks

    Ainsworth, Mark | Shin, Yeonjong

    SIAM Journal on Scientific Computing, Vol. 44 (2022), Iss. 4 P.A2253

    https://doi.org/10.1137/21M1460764 [Citations: 2]

  107. The Application of Autoencoders for Hyperspectral Data Compression

    Minkin, Alexander

    2021 International Conference on Information Technology and Nanotechnology (ITNT), (2021), P.1

    https://doi.org/10.1109/ITNT52450.2021.9649318 [Citations: 3]

  108. A proof of convergence for gradient descent in the training of artificial neural networks for constant target functions

    Cheridito, Patrick | Jentzen, Arnulf | Riekert, Adrian | Rossmannek, Florian

    Journal of Complexity, Vol. 72 (2022), Iss. P.101646

    https://doi.org/10.1016/j.jco.2022.101646 [Citations: 14]

  109. Deep-learning-assisted and GPU-accelerated vector Doppler imaging with aliasing-resistant velocity estimation

    Nahas, Hassan | Yiu, Billy Y.S. | Chee, Adrian J.Y. | Au, Jason S. | Yu, Alfred C.H.

    Ultrasonics, Vol. 134 (2023), Iss. P.107050

    https://doi.org/10.1016/j.ultras.2023.107050 [Citations: 2]

  110. Modeling non-linear effects with neural networks in Relational Event Models

    Filippi-Mazzola, Edoardo | Wit, Ernst C.

    Social Networks, Vol. 79 (2024), Iss. P.25

    https://doi.org/10.1016/j.socnet.2024.05.004 [Citations: 0]

  111. X-ray driven peanut trait estimation: computer vision aided agri-system transformation

    Domhoefer, Martha | Chakraborty, Debarati | Hufnagel, Eva | Claußen, Joelle | Wörlein, Norbert | Voorhaar, Marijn | Anbazhagan, Krithika | Choudhary, Sunita | Pasupuleti, Janila | Baddam, Rekha | Kholova, Jana | Gerth, Stefan

    Plant Methods, Vol. 18 (2022), Iss. 1

    https://doi.org/10.1186/s13007-022-00909-8 [Citations: 4]

  112. Layer-Wise External Attention by Well-Localized Attention Map for Efficient Deep Anomaly Detection

    Nakanishi, Keiichi | Shiroma, Ryo | Hayakawa, Tokihisa | Katafuchi, Ryoya | Tokunaga, Terumasa

    SN Computer Science, Vol. 5 (2024), Iss. 5

    https://doi.org/10.1007/s42979-024-02912-3 [Citations: 0]

  113. A Study of Features and Deep Neural Network Architectures and Hyper-Parameters for Domestic Audio Classification

    Copiaco, Abigail | Ritz, Christian | Abdulaziz, Nidhal | Fasciani, Stefano

    Applied Sciences, Vol. 11 (2021), Iss. 11 P.4880

    https://doi.org/10.3390/app11114880 [Citations: 14]

  114. Soil Moisture Content Estimation Based on Sentinel-1 SAR Imagery Using an Artificial Neural Network and Hydrological Components

    Chung, Jeehun | Lee, Yonggwan | Kim, Jinuk | Jung, Chunggil | Kim, Seongjoon

    Remote Sensing, Vol. 14 (2022), Iss. 3 P.465

    https://doi.org/10.3390/rs14030465 [Citations: 20]

  115. Regularised feed forward neural networks for streamed data classification problems

    Ellis, Mathys | Bosman, Anna S. | Engelbrecht, Andries P.

    Engineering Applications of Artificial Intelligence, Vol. 133 (2024), Iss. P.108555

    https://doi.org/10.1016/j.engappai.2024.108555 [Citations: 1]

  116. Lifelong deep learning‐based control of robot manipulators

    Ganie, Irfan | Sarangapani, Jagannathan

    International Journal of Adaptive Control and Signal Processing, Vol. 37 (2023), Iss. 12 P.3169

    https://doi.org/10.1002/acs.3678 [Citations: 0]

  117. Editorial

    Chen, Chi-Hua | Chao, Kuo-Ming | Hwang, Feng-Jang | Han, Chunjia | Pu, Lianrong

    International Journal of Distributed Sensor Networks, Vol. 17 (2021), Iss. 2 P.155014772199288

    https://doi.org/10.1177/1550147721992881 [Citations: 0]

  118. Age Prediction by DNA Methylation in Neural Networks

    Li, Lechuan | Zhang, Chonghao | Liu, Shiyu | Guan, Hannah | Zhang, Yu

    IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol. 19 (2022), Iss. 3 P.1393

    https://doi.org/10.1109/TCBB.2021.3084596 [Citations: 7]

  119. Functional loops: Monitoring functional organization of deep neural networks using algebraic topology

    Zhang, Ben | Lin, Hongwei

    Neural Networks, Vol. 174 (2024), Iss. P.106239

    https://doi.org/10.1016/j.neunet.2024.106239 [Citations: 3]

  120. Flex-SFU: Accelerating DNN Activation Functions by Non-Uniform Piecewise Approximation

    Reggiani, Enrico | Andri, Renzo | Cavigelli, Lukas

    2023 60th ACM/IEEE Design Automation Conference (DAC), (2023), P.1

    https://doi.org/10.1109/DAC56929.2023.10247855 [Citations: 2]

  121. Uncertainty quantification in Neural Networks by Approximate Bayesian Computation: Application to fatigue in composite materials

    Fernández, Juan | Chiachío, Manuel | Chiachío, Juan | Muñoz, Rafael | Herrera, Francisco

    Engineering Applications of Artificial Intelligence, Vol. 107 (2022), Iss. P.104511

    https://doi.org/10.1016/j.engappai.2021.104511 [Citations: 33]

  122. Diagnosis of COVID-19 and Pneumonia using Depthwise Separable Convolutional Neural Network

    Saha, Shrabana | Bhadra, Rajarshi | Kar, Subhajit

    2021 IEEE Bombay Section Signature Conference (IBSSC), (2021), P.1

    https://doi.org/10.1109/IBSSC53889.2021.9673215 [Citations: 1]

  123. Adaptive Morphing Activation Function for Neural Networks

    Herrera-Alcántara, Oscar | Arellano-Balderas, Salvador

    Fractal and Fractional, Vol. 8 (2024), Iss. 8 P.444

    https://doi.org/10.3390/fractalfract8080444 [Citations: 0]

  124. A Distributed Machine Learning-Based Approach for IRS-Enhanced Cell-Free MIMO Networks

    Chen, Chen | Xu, Sai | Zhang, Jiliang | Zhang, Jie

    IEEE Transactions on Wireless Communications, Vol. 23 (2024), Iss. 5 P.5287

    https://doi.org/10.1109/TWC.2023.3325772 [Citations: 11]

  125. Fuzzy-based collective pitch control for wind turbine via deep reinforcement learning

    Nabeel, Abdelhamid | Lasheen, Ahmed | Elshafei, Abdel Latif | Aboul Zahab, Essam

    ISA Transactions, Vol. 148 (2024), Iss. P.307

    https://doi.org/10.1016/j.isatra.2024.03.023 [Citations: 1]

  126. Federated Learning-Based Channel Estimation for RIS-Aided Communication Systems

    Qiu, Bin | Chang, Xiao | Li, Xian | Xiao, Hailin | Zhang, Zhongshan

    IEEE Wireless Communications Letters, Vol. 13 (2024), Iss. 8 P.2130

    https://doi.org/10.1109/LWC.2024.3402968 [Citations: 0]

  127. Forecasting Surface Velocity Fields Associated With Laboratory Seismic Cycles Using Deep Learning

    Mastella, G. | Corbi, F. | Bedford, J. | Funiciello, F. | Rosenau, M.

    Geophysical Research Letters, Vol. 49 (2022), Iss. 15

    https://doi.org/10.1029/2022GL099632 [Citations: 2]

  128. Principle of TEM alignment using convolutional neural networks: Case study on condenser aperture alignment

    Grossetête, Loïc | Marcelot, Cécile | Gatel, Christophe | Pauchet, Sylvain | Hytch, Martin

    Ultramicroscopy, Vol. 267 (2024), Iss. P.114047

    https://doi.org/10.1016/j.ultramic.2024.114047 [Citations: 0]

  129. Using personalized next session to improve session-based recommender systems

    Chen, Yen-Liang | Wu, Chia-Chi | Shih, Po-Cheng

    The Journal of Supercomputing, Vol. 80 (2024), Iss. 6 P.7369

    https://doi.org/10.1007/s11227-023-05743-w [Citations: 0]

  130. Adaptive Activation Functions for Deep Learning-based Power Flow Analysis

    Kaseb, Zeynab | Xiang, Yu | Palensky, Peter | Vergara, Pedro P.

    2023 IEEE PES Innovative Smart Grid Technologies Europe (ISGT EUROPE), (2023), P.1

    https://doi.org/10.1109/ISGTEUROPE56780.2023.10407913 [Citations: 0]

  131. Estimating permeability of 3D micro-CT images by physics-informed CNNs based on DNS

    Gärttner, Stephan | Alpak, Faruk O. | Meier, Andreas | Ray, Nadja | Frank, Florian

    Computational Geosciences, Vol. 27 (2023), Iss. 2 P.245

    https://doi.org/10.1007/s10596-022-10184-0 [Citations: 15]

  132. A Novel Visuo-Tactile Object Recognition Pipeline using Transformers with Feature Level Fusion

    Doherty, John | Gardiner, Bryan | Siddique, Nazmul | Kerr, Emmett

    2024 International Joint Conference on Neural Networks (IJCNN), (2024), P.1

    https://doi.org/10.1109/IJCNN60899.2024.10650147 [Citations: 0]
  133. Pattern Recognition Applications and Methods

    Reduced Precision Research of a GAN Image Generation Use-case

    Rehm, Florian | Saletore, Vikram | Vallecorsa, Sofia | Borras, Kerstin | Krücker, Dirk

    2023

    https://doi.org/10.1007/978-3-031-24538-1_1 [Citations: 0]

  134. Hybrid COVID-19 segmentation and recognition framework (HMB-HCF) using deep learning and genetic algorithms

    Balaha, Hossam Magdy | Balaha, Magdy Hassan | Ali, Hesham Arafat

    Artificial Intelligence in Medicine, Vol. 119 (2021), Iss. P.102156

    https://doi.org/10.1016/j.artmed.2021.102156 [Citations: 27]
  135. Numerical Analysis Meets Machine Learning

    Theoretical foundations of physics-informed neural networks and deep neural operators

    Shin, Yeonjong | Zhang, Zhongqiang | Karniadakis, George Em

    2024

    https://doi.org/10.1016/bs.hna.2024.05.008 [Citations: 0]

  136. Streamlining latent spaces in machine learning using moment pooling

    Gambhir, Rikab | Osathapan, Athis | Thaler, Jesse

    Physical Review D, Vol. 110 (2024), Iss. 7

    https://doi.org/10.1103/PhysRevD.110.074020 [Citations: 0]

  137. Applications of Neural Networks in Biomedical Data Analysis

    Weiss, Romano | Karimijafarbigloo, Sanaz | Roggenbuck, Dirk | Rödiger, Stefan

    Biomedicines, Vol. 10 (2022), Iss. 7 P.1469

    https://doi.org/10.3390/biomedicines10071469 [Citations: 13]

  138. CACTO: Continuous Actor-Critic With Trajectory Optimization—Towards Global Optimality

    Grandesso, Gianluigi | Alboni, Elisa | Papini, Gastone P. Rosati | Wensing, Patrick M. | Prete, Andrea Del

    IEEE Robotics and Automation Letters, Vol. 8 (2023), Iss. 6 P.3318

    https://doi.org/10.1109/LRA.2023.3266985 [Citations: 5]

  139. Intrusion Detection System Model Based on Gated Recurrent Unit to Detect Anomaly Traffic

    Alsyaibani, Omar Muhammad Altoumi | Utami, Ema | Hartanto, Anggit Dwi

    2021 4th International Conference on Information and Communications Technology (ICOIACT), (2021), P.5

    https://doi.org/10.1109/ICOIACT53268.2021.9564003 [Citations: 2]

  140. The limitation of neural nets for approximation and optimization

    Giovannelli, T. | Sohab, O. | Vicente, L. N.

    Journal of Global Optimization, Vol. (2024), Iss.

    https://doi.org/10.1007/s10898-024-01426-9 [Citations: 0]

  141. Identifying algorithm in program code based on structural features using CNN classification model

    Watanobe, Yutaka | Rahman, Md. Mostafizer | Amin, Md. Faizul Ibne | Kabir, Raihan

    Applied Intelligence, Vol. 53 (2023), Iss. 10 P.12210

    https://doi.org/10.1007/s10489-022-04078-y [Citations: 16]

  142. Mapping Atomic-Scale Metal–Molecule Interactions: Salient Feature Extraction through Autoencoding of Vibrational Spectroscopy Data

    Poppe, Alex | Griffiths, Jack | Hu, Shu | Baumberg, Jeremy J. | Osadchy, Margarita | Gibson, Stuart | de Nijs, Bart

    The Journal of Physical Chemistry Letters, Vol. 14 (2023), Iss. 34 P.7603

    https://doi.org/10.1021/acs.jpclett.3c01483 [Citations: 2]