These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

363 related articles for article (PubMed ID: 33476947)

  • 1. A Dual-Dimer method for training physics-constrained neural networks with minimax architecture.
    Liu D; Wang Y
    Neural Netw; 2021 Apr; 136():112-125. PubMed ID: 33476947
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Physics-informed neural networks based on adaptive weighted loss functions for Hamilton-Jacobi equations.
    Liu Y; Cai L; Chen Y; Wang B
    Math Biosci Eng; 2022 Sep; 19(12):12866-12896. PubMed ID: 36654026
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Gradient-based training and pruning of radial basis function networks with an application in materials physics.
    Määttä J; Bazaliy V; Kimari J; Djurabekova F; Nordlund K; Roos T
    Neural Netw; 2021 Jan; 133():123-131. PubMed ID: 33212359
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Transformed ℓ
    Ma R; Miao J; Niu L; Zhang P
    Neural Netw; 2019 Nov; 119():286-298. PubMed ID: 31499353
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Performance of Fourier-based activation function in physics-informed neural networks for patient-specific cardiovascular flows.
    Aghaee A; Khan MO
    Comput Methods Programs Biomed; 2024 Apr; 247():108081. PubMed ID: 38428251
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Gradient Descent Ascent for Minimax Problems on Riemannian Manifolds.
    Huang F; Gao S
    IEEE Trans Pattern Anal Mach Intell; 2023 Jul; 45(7):8466-8476. PubMed ID: 37018266
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Fractional-order gradient descent learning of BP neural networks with Caputo derivative.
    Wang J; Wen Y; Gou Y; Ye Z; Chen H
    Neural Netw; 2017 May; 89():19-30. PubMed ID: 28278430
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Nonconvex Sparse Regularization for Deep Neural Networks and Its Optimality.
    Ohn I; Kim Y
    Neural Comput; 2022 Jan; 34(2):476-517. PubMed ID: 34758482
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Constructing Physics-Informed Neural Networks with Architecture Based on Analytical Modification of Numerical Methods by Solving the Problem of Modelling Processes in a Chemical Reactor.
    Tarkhov D; Lazovskaya T; Malykhina G
    Sensors (Basel); 2023 Jan; 23(2):. PubMed ID: 36679461
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Adaptive complex-valued stepsize based fast learning of complex-valued neural networks.
    Zhang Y; Huang H
    Neural Netw; 2020 Apr; 124():233-242. PubMed ID: 32018161
    [TBL] [Abstract][Full Text] [Related]  

  • 11. An improvement of extreme learning machine for compact single-hidden-layer feedforward neural networks.
    Huynh HT; Won Y; Kim JJ
    Int J Neural Syst; 2008 Oct; 18(5):433-41. PubMed ID: 18991365
    [TBL] [Abstract][Full Text] [Related]  

  • 12. A Combination of Deep Neural Networks and Physics to Solve the Inverse Problem of Burger's Equation.
    Alkhadhr S; Almekkawy M
    Annu Int Conf IEEE Eng Med Biol Soc; 2021 Nov; 2021():4465-4468. PubMed ID: 34892210
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Personalising left-ventricular biophysical models of the heart using parametric physics-informed neural networks.
    Buoso S; Joyce T; Kozerke S
    Med Image Anal; 2021 Jul; 71():102066. PubMed ID: 33951597
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Can a computer "learn" nonlinear chromatography?: Physics-based deep neural networks for simulation and optimization of chromatographic processes.
    Subraveti SG; Li Z; Prasad V; Rajendran A
    J Chromatogr A; 2022 Jun; 1672():463037. PubMed ID: 35462309
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Data classification based on fractional order gradient descent with momentum for RBF neural network.
    Xue H; Shao Z; Sun H
    Network; 2020; 31(1-4):166-185. PubMed ID: 33283569
    [TBL] [Abstract][Full Text] [Related]  

  • 16. LordNet: An efficient neural network for learning to solve parametric partial differential equations without simulated data.
    Huang X; Shi W; Gao X; Wei X; Zhang J; Bian J; Yang M; Liu TY
    Neural Netw; 2024 Aug; 176():106354. PubMed ID: 38723308
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Fully complex conjugate gradient-based neural networks using Wirtinger calculus framework: Deterministic convergence and its application.
    Zhang B; Liu Y; Cao J; Wu S; Wang J
    Neural Netw; 2019 Jul; 115():50-64. PubMed ID: 30974301
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Multi-fidelity information fusion with concatenated neural networks.
    Pawar S; San O; Vedula P; Rasheed A; Kvamsdal T
    Sci Rep; 2022 Apr; 12(1):5900. PubMed ID: 35393511
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Enhancing neurodynamic approach with physics-informed neural networks for solving non-smooth convex optimization problems.
    Wu D; Lisser A
    Neural Netw; 2023 Nov; 168():419-430. PubMed ID: 37804745
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Neural Network Potentials: A Concise Overview of Methods.
    Kocer E; Ko TW; Behler J
    Annu Rev Phys Chem; 2022 Apr; 73():163-186. PubMed ID: 34982580
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 19.