Time-aware Graph Neural Networks for Entity Alignment between Temporal Knowledge Graphs. 2022. [PUMA: Alignment Entity Graph Graphs Knowledge Networks Neural Temporal Time-aware Yaff] URL
Learning algorithms for spiking neural networks: should one use learning algorithms from ANN/DL or neurological plausible learning? - A thought-provoking impulse. XLIII Jornadas de Automática: libro de actas: 7, 8 y 9 de septiembre de 2022, Logroño (La Rioja), 201--207, Servizo de Publicacións da UDC, September 2022. [PUMA: Learning Xack algorithms learning networks neural neurological plausible spiking]
Predicting years with extremely low gross primary production from daily weather data using Convolutional Neural Networks. Environmental Data Science, (1):e2, 2022. [PUMA: Convolutional Networks Neural Predicting data gross low primary production weather]
Inadequacy of common stochastic neural networks for reliable clinical decision support. 2024. [PUMA: Inadequacy clinical decision networks neural reliable stochastic support] URL
A methodology for direct parameter identification for experimental results using machine learning — Real world application to the highly non-linear deformation behavior of FRP. Computational Materials Science, (244 (2024))Elsevier Science B.V., September 2024. [PUMA: area_architectures topic_engineering Constitutive FIS_scads Fiber Machine Neural Parameter identification learning, modeling, networks, plastics, reinforced]
Direct parameter identification for highly nonlinear strain rate dependent constitutive models using machine learning. ECCM21 - Proceedings of the 21st European Conference on Composite Materials, (3):1252--1259, European Society for Composite Materials (ESCM), Jul 2, 2024. [PUMA: area_architectures topic_engineering Convolutional Direct FIS_scads Fiber Machine Strain dependency, identification, learning, networks, neural parameter plastics rate reinforced] URL
Inverse Dirichlet weighting enables reliable training of physics informed neural networks. Machine learning: science and technology, (3)1IOP Publishing Ltd., Feb 15, 2022. [PUMA: topic_lifescience ALGORITHM FIS_scads active catastrophic flow forgetting, gradient modeling, multi-objective multi-scale networks, neural physics-informed regularization, training, turbulence,]
Adaptive weighting of Bayesian physics informed neural networks for multitask and multiscale forward and inverse problems. Journal of computational physics, (491)Academic Press Inc., Oct 15, 2023. [PUMA: topic_lifescience Adaptive Artificial Bayesian Carlo, FIS_scads Hamiltonian Intelligence, Monte Multi-objective Quantification Uncertainty learning, networks, neural physics-informed training, weight]
SetQuence & SetOmic: Deep Set Transformer-based Representations of Cancer Multi-Omics. 2022 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2022, 139--147, IEEE, New York u. a., United States of America, 2022. [PUMA: topic_federatedlearn livinglab Deep FIS_scads Network, Neural Representations Set analysis, expression, gene genome, language molecular multi-omics, mutome, natural processing, sequence]
Artificial neural network small-sample-bias-corrections of the AR(1) parameter close to unit root. Statistica Neerlandica, Wiley-Blackwell, Oxford u. a., Jul 31, 2024. [PUMA: topic_engineering FIS_scads bias correction, network, neural sample small]
Spatial–temporal recurrent reinforcement learning for autonomous ships. Neural Networks, (2023)165:634--653, Elsevier Science B.V., Jun 15, 2023. [PUMA: topic_engineering Algorithms, Autonomous COLREG, Computer, Deep FIS_scads Networks, Neural Psychology, Recurrency, Reinforcement, Reward Ships, learning, reinforcement surface vehicle,]
Learning computable models from data. 1--6, 2021. [PUMA: Differential ENO-WENO, FIS_scads Neural Surrogate modeling networks, operators,]
OmniOpt – A tool for hyperparameter optimization on HPC. In Heike Jagode, Hartwig Anzt, Hatem Ltaief, and Piotr Luszczek (Eds.), High Performance Computing - ISC High Performance Digital 2021 International Workshops, 2021, Revised Selected Papers, 285--296, Springer, Berlin u. a., Germany, Nov 13, 2021. [PUMA: FIS_scads High Hyperparameter Neural computing, networks optimization, performance]
Robust Reconstruction of the Void Fraction from Noisy Magnetic Flux Density Using Invertible Neural Networks. Sensors, (24)42024. [PUMA: topic_visualcomputing Density Flux Fraction Invertible Magnetic Networks Neural Noisy Reconstruction Void] URL
TraceVis: Towards Visualization for Deep Statistical Model Checking. Leveraging Applications of Formal Methods, Verification and Validation: Tools and Trends: 9th International Symposium on Leveraging Applications of Formal Methods, ISoLA 2020, Rhodes, Greece, October 20–30, 2020, Proceedings, Part IV, 27–46, Springer-Verlag, Berlin, Heidelberg, 2020. [PUMA: Checking Model Networks Neural Statistical Visualization] URL
Integration of deep learning-based image analysis and genomic data in cancer pathology: A systematic review. Eur. J. Cancer, (160):80--91, Elsevier BV, January 2022. [PUMA: topic_lifescience Biomarker Cancer; Convolutional Multimodal Omics fusion; identification; networks; neural]
Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology. Med. Image Anal., (79)102474:102474, Elsevier BV, July 2022. [PUMA: topic_lifescience Artificial Computational Convolutional Learning; Multiple-Instance Vision Weakly-supervised deep intelligence; learning networks; neural pathology; transformers;]
Artificial intelligence in liver diseases: Improving diagnostics, prognostics and response prediction. JHEP Rep., (4)4:100443, Elsevier BV, April 2022. [PUMA: topic_lifescience AI, Artificial CNN, Communications DICOM, Diagnosis; Digital HCC, Imaging Individual ML, MVI, Medicine; NAFLD, NASH, Prognosis Reporting TACE, TRIPOD, Transparent WSIs, a and artificial carcinoma; chemoembolisation; convolutional data deep diagnostic disease; fatty for hepatocellular images; imaging; in integration intelligence; invasion; learning; liver machine microvascular model multimodal multivariable network; neural non-alcoholic of or prediction slide steatohepatitis; support system; transarterial whole]
Introduction to the BioChemical Library (BCL): An application-based open-source toolkit for integrated cheminformatics and machine learning in computer-aided drug discovery. Front. Pharmacol., (13):833099, Frontiers Media SA, February 2022. [PUMA: topic_lifescience BCL; QSAR; biochemical cheminformatics; deep design; discovery; drug library; network; neural open-source]
Structural damage identification of composite rotors based on fully connected neural networks and convolutional neural networks. Sensors (Basel), (21)6:2005, MDPI AG, March 2021. [PUMA: (SHM) composite composites; connected convolutional dense fully health learning; machine monitoring networks; neural rotors; structural]