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 zno]
Inadequacy of common stochastic neural networks for reliable clinical decision support. 2024. [PUMA: Inadequacy clinical decision networks neural reliable stochastic support xack] URL
OmniOpt – A tool for hyperparameter optimization on HPC. In Heike Jagode, Hartwig Anzt, Hatem Ltaief, und Piotr Luszczek (Hrsg.), High Performance Computing - ISC High Performance Digital 2021 International Workshops, 2021, Revised Selected Papers, 285--296, Springer, Berlin u. a., Germany, 13.11.2021. [PUMA: FIS_scads High Hyperparameter Neural computing, networks optimization, performance xack]
Learning computable models from data. 1--6, 2021. [PUMA: Differential ENO-WENO, FIS_scads Neural Surrogate modeling networks, operators, yaff]
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), 02.07.2024. [PUMA: Convolutional Direct FIS_scads Fiber Machine Strain area_architectures dependency, identification, learning, networks, neural parameter plastics rate reinforced topic_engineering yaff] URL
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, Januar 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, Juli 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, Februar 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, März 2021. [PUMA: (SHM) composite composites; connected convolutional dense fully health learning; machine monitoring networks; neural rotors; structural]
Stroke-GFCN: ischemic stroke lesion prediction with a fully convolutional graph network. J. Med. Imaging (Bellingham), (10)4:044502, SPIE-Intl Soc Optical Eng, Juli 2023. [PUMA: topic_visualcomputing graph imaging; learning; machine medical multi-modal networks; neural prediction stroke]
Individual modelling of haematotoxicity with NARX neural networks: A knowledge transfer approach. Heliyon, (9)7:e17890, Elsevier BV, Juli 2023. [PUMA: topic_neuroinspired topic_mathfoundation Haematopoiesis; Precision Recurrent System Transfer identification; learning medicine; networks; neural unit_transfer]