Deep Learning: Foundations and Concepts

AI advocates and cautious critics: How AI attitudes, AI interest, use of AI, and AI literacy build university students’ AI self-efficacy.

Bewersdorff, A., Hornberger, M., Nerdel, C., & Schiff, D. S. (2025) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2024.100340 · Cited by 148

Transformers and genome language models

Consens, M. E., Dufault, C., Wainberg, M., Forster, D., Karimzadeh, M., Goodarzi, H., Theis, F. J., Moses, A. M., & Wang, B. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-01007-9 · Cited by 170

Physics-driven learning for inverse problems in quantum chromodynamics

Gert Aarts, A., Fukushima, K., Hatsuda, T., Ipp, A., Shi, S., Wang, L., & Zhou, K. (2025) · Nature Reviews Physics · doi:10.1038/s42254-024-00798-x · Cited by 64

Improving Hardenability Modeling: A Bayesian Optimization Approach to Tuning Hyperparameters for Neural Network Regression

Gemechu, W. F., Sitek, W., & Batalha, G. F. (2024) · Applied Sciences · doi:10.3390/app14062554 · Cited by 30

Fractional Calculus Meets Neural Networks for Computer Vision: A Survey

Coelho, C., Costa, M. F. P., & Ferrás, L. L. (2024) · AI · doi:10.3390/ai5030067 · Cited by 31

Graph-Based Audio Classification Using Pre-Trained Models and Graph Neural Networks

Castro-Ospina, A. E., Solarte-Sanchez, M., Vega-Escobar, L. S., Isaza, C., & Martinez-Vargas, J. D. (2024) · Sensors · doi:10.3390/s24072106 · Cited by 26

World and Human Action Models towards gameplay ideation

Kanervisto, A., Bignell, D., Wen, L., Grayson, M., Georgescu, R., Macua, S. V., Tan, S. Z., Rashid, T., Pearce, T., Cao, Y., et al. (2025) · Nature · doi:10.1038/s41586-025-08600-3 · Cited by 36

Overcoming barriers in the use of artificial intelligence in point of care ultrasound

Vega, R., Dehghan, M., Nagdev, A. D., Buchanan, B. M., Kapur, J., Jaremko, J. L., & Zonoobi, D. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01633-y · Cited by 29

An adaptive rainfall-runoff model for daily runoff prediction under the changing environment: Stream-LSTM

Hu, F., Yang, Q., Yang, J., Shao, J., & Wang, G. (2025) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2025.106524 · Cited by 20

A Review of Passenger Counting in Public Transport Concepts with Solution Proposal Based on Image Processing and Machine Learning

Radovan, A., Mršić, L., Đambić, G., & Mihaljević, B. (2024) · Eng · doi:10.3390/eng5040172 · Cited by 24

Generative AI for predictive breeding: hopes and caveats

Miguel Pérez-enciso, A., Zingaretti, L. M., & Campos, G. D. L. (2025) · Theoretical and Applied Genetics · doi:10.1007/s00122-025-04942-8 · Cited by 17

stVCR: spatiotemporal dynamics of single cells

Peng, Q., Zhou, P., & Li, T. (2026) · Nature Methods · doi:10.1038/s41592-026-03010-3 · Cited by 19

Herbal ingredient-target interaction prediction via multi-modal learning

Liang, X., Lai, G., Yu, J., Lin, T., Wang, C., & Wang, W. (2025) · Information Sciences · doi:10.1016/j.ins.2025.122115 · Cited by 15

Multimodal foundation transformer models for multiscale genomics

Khan, S. A., Martinez‐de‐Morentin, X., Alsabbagh, A. R., Maillo, A., Lagani, V., Gómez-Cabrero, D., Lehmann, R., & Tegner, J. N. (2025) · Nature Methods · doi:10.1038/s41592-025-02918-6 · Cited by 17

Solving Viscous Burgers’ Equation: Hybrid Approach Combining Boundary Layer Theory and Physics-Informed Neural Networks

Ortiz-Ortiz, R.-D., Núñez, O. M., & Marin-Ramirez, A.-M. (2024) · Mathematics · doi:10.3390/math12213430 · Cited by 13

Outlier-detection for reactive machine learned potential energy surfaces

Vazquez-Salazar, L. I., Käser, S., & Meuwly, M. (2025) · npj Computational Materials · doi:10.1038/s41524-024-01473-6 · Cited by 13

Interferometric Synthetic Aperture Radar Multitemporal Deformation Monitoring: A review of machine learning techniques

Wassie, Y., & Milillo, P. (2025) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2025.3538667 · Cited by 16

Deep learning-based cell segmentation for rapid optical cytopathology of thyroid cancer

Jermain, P. R., Oswald, M., Langdun, T., Wright, S., Khan, A. A., Stadelmann, T., Abdulkadir, A., & Yaroslavsky, A. N. (2024) · Scientific Reports · doi:10.1038/s41598-024-64855-2 · Cited by 13

Machine learning data fusion for high spatio-temporal resolution PM 2.5

Porcheddu, A., Kolehmainen, V., Lähivaara, T., & Lipponen, A. (2025) · Atmospheric measurement techniques · doi:10.5194/amt-18-4771-2025 · Cited by 11

Machine Learning in Cheese-Making: Methods, Applications, and the Future

Pardo, D., Castillo, M., Mülâyim, M. O., & Cerquides, J. (2025) · Food Engineering Reviews · doi:10.1007/s12393-025-09420-x · Cited by 11

The ECB press conference statement: deriving a new sentiment indicator for the euro area

Kanelis, D., & Siklos, P. L. (2024) · International Journal of Finance & Economics · doi:10.1002/ijfe.2940 · Cited by 9

Fault Feature Assessment Method for High-Voltage Circuit Breakers Based on Explainable Image Recognition

Tan, Y., Gong, J., Li, S., Li, J., & Chen, W. (2024) · IEEE Transactions on Dielectrics and Electrical Insulation · doi:10.1109/tdei.2024.3487820 · Cited by 9

Sample Selection Bias in Machine Learning for Healthcare

Chauhan, V. K., Clifton, L., Salaün, A., Lu, H. Y., Branson, K., Schwab, P., Nigam, G. B., & Clifton, D. A. (2025) · ACM Transactions on Computing for Healthcare · doi:10.1145/3761822 · Cited by 11

Uncovering ion transport mechanisms in ionic liquids using data science

Umaña, J. E., Cashen, R. K., Zavala, V. M., & Gebbie, M. A. (2025) · Digital Discovery · doi:10.1039/d4dd00378k · Cited by 8

Early Detection of ITSC Faults in PMSMs Using Transformer Model and Transient Time-Frequency Features

Zsuga, Á., & Dineva, A. A. (2025) · Energies · doi:10.3390/en18154048 · Cited by 9

High-risk AI transparency? On qualified transparency mandates for oversight bodies under the EU AI Act

Söderlund, K. (2025) · Technology and Regulation · doi:10.71265/6bedar76 · Cited by 9

Application of deep learning methods for beam size control during user operation at the Advanced Light Source

Hellert, T., Ford, T., Leemann, S., Nishimura, H., Venturini, M., & Pollastro, A. (2024) · Physical Review Accelerators and Beams · doi:10.1103/physrevaccelbeams.27.074602 · Cited by 8

Learning from the Data to Predict the Process

Pfeiffer, P., Abb, L., Fettke, P., & Rehse, J.-R. (2025) · Business & Information Systems Engineering · doi:10.1007/s12599-025-00936-4 · Cited by 7

Assistive Artificial Intelligence in Epilepsy and Its Impact on Epilepsy Care in Low- and Middle-Income Countries

Koirala, N., Adhikari, S. R., Adhikari, M., Yadav, T., Anwar, A. R., Ciolac, D., Shrestha, B., Adhikari, I., Khanal, B., & Muthuraman, M. (2025) · Brain Sciences · doi:10.3390/brainsci15050481 · Cited by 7

High-resolution ensemble retrieval of cloud properties for all-day based on geostationary satellite

Xiao, H., Zhang, F., Wang, L., Pan, B., Zhu, Y., Wang, M., Li, W., Guo, B., & Li, J. (2025) · npj Climate and Atmospheric Science · doi:10.1038/s41612-025-01263-x · Cited by 7

Breaking the Barriers of Molecular Dynamics With Deep‐Learning: Opportunities, Pitfalls, and How to Navigate Them

Bonneau, K., Pasos-Trejo, A. S., Plainer, M., Sagresti, L., Venturin, J., Zaporozhets, I., Caruso, A., Rolando, E., Guljas, A., Klein, L., et al. (2026) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.70064 · Cited by 9

Prediction of Energy Consumption on Example of Heterogenic Commercial Buildings

Kawa, K., Mularczyk, R., Bauer, W., Grobler-Dębska, K., & Kucharska, E. (2024) · Energies · doi:10.3390/en17133220 · Cited by 7

Regularizing the pulsar timing array likelihood: A path toward Fourier space

Valtolina, S., & van Haasteren, R. (2025) · Physical review. D/Physical review. D. · doi:10.1103/s3gy-km61 · Cited by 7

Using a Symbolic Knowledge Graph to Address LLM Limitations in Analog Circuit Topology Generation

Shaik, H., & Doboli, A. (2025) · IEEE Annual Computing and Communication Workshop and Conference (CCWC) · doi:10.1109/ccwc62904.2025.10903951 · Cited by 7

Diving into deep learning

Wang, G. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00840-8 · Cited by 7

Improving the weld seam quality in laser welding processes by means of Bayesian optimization

Haas, M., Onuseit, V., Powell, J., Zaiß, F., Wahl, J., Menold, T., Hagenlocher, C., & Michalowski, A. (2024) · Procedia CIRP · doi:10.1016/j.procir.2024.08.222 · Cited by 6

Predicting sepsis treatment decisions in the paediatric emergency department using machine learning: the AiSEPTRON study

Gomes, S., Dhanoa, H., Assheton, P., Carr, E., Roland, D., & Deep, A. (2025) · BMJ Paediatrics Open · doi:10.1136/bmjpo-2024-003273 · Cited by 7

A Setup for Automatic Raman Measurements in High‐Throughput Experimentation

Lange, C., Seidel, S., Altmann, M., Stors, D., Kemmer, A., Cai, L., Born, S., Neubauer, P. B., & Bournazou, M. N. C. (2025) · Biotechnology and Bioengineering · doi:10.1002/bit.70006 · Cited by 6

Physics-conditioned diffusion models for lattice gauge theory

Zhu, Q., Gert Aarts, A., Wang, W., Zhou, K., & Wang, L. (2026) · Journal of High Energy Physics · doi:10.1007/jhep03(2026)111 · Cited by 38

Evaluation of Deformable Convolution: An Investigation in Image and Video Classification

Burgos-Madrigal, A., Romero-Bautista, V., Hernández, R. D., & Altamirano-Robles, L. (2024) · Mathematics · doi:10.3390/math12162448 · Cited by 6

AI-Driven Simulation of Stochastic Electrochemiluminescence

Mathwig, K. (2024) · ACS electrochemistry. · doi:10.1021/acselectrochem.4c00166 · Cited by 5

A Pipeline and NIR-Enhanced Dataset for Parking Lot Segmentation

Qiam, S., Devunuri, S., & Lehe, L. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00127 · Cited by 5

LYRICEL: Knowledge Graphs Combined With Large Language Models and Machine Learning for Cross-Cultural Analysis of Lyrics—The Case of Greek Songs

Panagoulias, D. P., Tsichrintzi, E.-A., Sotiropoulos, D. N., Chrysafiadi, K., Sakkopoulos, E., Tsihrintzis, G. A., & Virvou, M. K. (2025) · IEEE Access · doi:10.1109/access.2025.3597213 · Cited by 5

Learning to filter: snow data assimilation using a Long Short-Term Memory network

Blandini, G., Avanzi, F., Campo, L., Gabellani, S., Aalstad, K., Girotto, M., Yamaguchi, S., Hirashima, H., & Ferraris, L. (2025) · The Cryosphere · doi:10.5194/tc-19-4759-2025 · Cited by 7

Learning hadron emitting sources with deep neural networks

Wang, L., & Zhao, J. (2026) · Communications Physics · doi:10.1038/s42005-026-02530-w · Cited by 5

Designing the Chemical Composition of Steel with Required Hardenability Using Computational Methods

Tomašić, N., Sitek, W., Gemechu, W. F., & Iljkić, D. (2024) · Metals · doi:10.3390/met14091076 · Cited by 5

Neural network method to search for long transient gravitational waves

Attadio, F., Ricca, L., Serra, M., PALOMBA, C., Astone, P., Dall’Osso, S., Pra, S. D., D’Antonio, S., Giovanni, M. D., D’Onofrio, L., et al. (2024) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.110.103047 · Cited by 5

Data-driven discovery of self-similarity using neural networks

Watanabe, R., Ishii, T., Hirono, Y., & Maruoka, H. (2025) · Physical review. E · doi:10.1103/physreve.111.024301 · Cited by 5

A short introduction to neural networks and their application to Earth and Materials Science

Fanelli, D., Bindi, L., Chicchi, L., Pereti, C., Sessoli, R., & Tommasini, S. (2024) · RENDICONTI LINCEI · doi:10.1007/s12210-024-01271-8 · Cited by 4

Retrieval of Total Precipitable Water Under All‐Weather Conditions From Himawari‐8/AHI Observations Using the Generative Diffusion Model

Xiao, H., Zhang, F., Zhang, R., Lu, F., Cai, M., & Wang, L. (2025) · Geophysical Research Letters · doi:10.1029/2025gl117075 · Cited by 4

Emergent universal long-range structure in random-organizing systems

Anand, S., Zhang, G., & Martiniani, S. (2026) · Nature Communications · doi:10.1038/s41467-026-68601-2 · Cited by 4

A Dataset and a Comparison of Classification Methods for Valve Plate Fault Prediction of Piston Pump

Rojek, M., & Blachnik, M. (2024) · Applied Sciences · doi:10.3390/app14167183 · Cited by 4

Artificial Intelligence in Sport Scientific Creation and Writing Process

Latzel, R., & Glauner, P. (2024) · Springer eBooks · doi:10.1007/978-3-031-67256-9_2 · Cited by 4

On the Development of an Acoustic Image Dataset for Unexploded Ordnance Classification Using Front-Looking Sonar and Transfer Learning Methods

Ściegienka, P., & Blachnik, M. (2024) · Sensors · doi:10.3390/s24185946 · Cited by 4

A Data-Dependent Regularization Method Based on the Graph Laplacian

Bianchi, D., Evangelista, D., Aleotti, S., Donatelli, M., Piccolomini, E. L., & Li, W. (2025) · SIAM Journal on Scientific Computing · doi:10.1137/23m162750x · Cited by 4

Predicting dropout from higher education with multinomial finite mixture models: empirical evidence from Italy

Forti, M., Bilancia, M., Cafarelli, B., del Gobbo, E., & Nigri, A. (2025) · Quality & Quantity · doi:10.1007/s11135-025-02135-5 · Cited by 3

I’m not Welcome Here – Uma Revisão de Escopo sobre Toxicidade e Discurso de Ódio em Jogos Digitais

Gibrim, P. T. M., Ribeiro, M. V. G., Moreira, L. S., Melo, P. D. F., & Reis, J. C. S. (2025) · Brazilian Symposium on Multimedia and the Web · doi:10.5753/webmedia.2025.15998 · Cited by 6

JanusDDG: a physics-informed neural network for sequence-based protein stability via two-fronts attention

Barducci, G., Rossi, I., Codicè, F., Rollo, C., Repetto, V., Pancotti, C., Iannibelli, V., Sanavia, T., & Fariselli, P. (2026) · Communications Biology · doi:10.1038/s42003-026-09632-9 · Cited by 5

Learning data-efficient coarse-grained molecular dynamics from forces and noise

Durumeric, A. E. P., Chen, Y., Pasos-Trejo, A. S., Noé, F., & Clementi, C. (2026) · Nature Communications · doi:10.1038/s41467-026-70818-0 · Cited by 20

Embrace LLM-Based Cognitive Architecture to Boost Team Problem-Solving in Open-Ended Tasks

Shaik, H., Villuri, G., & Doboli, A. (2026) · Systems · doi:10.3390/systems14030313 · Cited by 3

Enhancing Deep Hashing With GCN-Based Models for Efficient Similarity Search

Kim, S. G., Min, S., Kim, W., Kim, D., & Hwang, D. (2024) · IEEE Access · doi:10.1109/access.2024.3514868 · Cited by 4

Fault Detection of Diesel Engines Using Artificial Neural Network

Pająk, M., Kluczyk, M., Muślewski, Ł., & Lisjak, D. (2025) · SAE International Journal of Engines · doi:10.4271/03-18-02-0010 · Cited by 3

Künstliche Intelligenz in den Naturwissenschaftsdidaktiken – gekommen, um zu bleiben: Potenziale, Desiderata, Herausforderungen

Nehring, A., Buschhüter, D., Kubsch, M., Ludwig, T., Wulff, P., & Neumann, K. (2025) · Zeitschrift für Didaktik der Naturwissenschaften · doi:10.1007/s40573-025-00177-8 · Cited by 3

Clinical insights to improve medical deep learning design: A comprehensive review of methods and benefits

Hellström, T. A. E., Ewals, L. J. S., Nederend, J., Luyer, M. D. P., de With, P. H. N., & van der Sommen, F. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110780 · Cited by 4

Deep learning reveals that multidimensional social status drives population variation in 11,875 US participant cohort

Marotta, J., Aggarwal, S., Osayande, N., Saltoun, K., Kopál, J., Holmes, A. J., Yip, S. W., & Bzdok, D. (2025) · PLoS ONE · doi:10.1371/journal.pone.0327729 · Cited by 4

Dynamical Analysis of Fractional-Order Quaternion-Valued Neural Networks with Leakage and Communication Delays

Wang, Q., Li, T., Wang, Y., & Tan, X. (2025) · Fractal and Fractional · doi:10.3390/fractalfract9090559 · Cited by 3

Semi-parametric bulk and tail regression using spline-based neural networks

Majumder, R., & Richards, J. (2026) · Extremes · doi:10.1007/s10687-026-00533-y · Cited by 3

Generative Models, Attention Mechanisms, and Adaptive Methods for Robot Navigation in Complex Environments—A Survey

Sridharan, H., Ramsai, N., & Vundurthy, B. (2025) · IEEE Access · doi:10.1109/access.2025.3590587 · Cited by 5

Climatic neural modeling for fault characterization and prediction in wind energy systems

Pérez, J. A., Deibe, Á., & Méndez, J. R. (2024) · International Journal of Green Energy · doi:10.1080/15435075.2024.2406846 · Cited by 3

What Comes After Transformers? A Selective Survey Connecting Ideas in Deep LearningGPT

Schneider, J. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-87330-0_4 · Cited by 14

Emerging Paradigms in Non-Profit Governance

Santos, J. (2024) · Advances in logistics, operations, and management science book series · doi:10.4018/979-8-3693-3723-3.ch001 · Cited by 4

Evaluating robustly standardized explainable anomaly detection of implausible variables in cancer data

Röchner, P., & Rothlauf, F. (2025) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaf011 · Cited by 2

A Framework for Optimal Parameter Selection in Electrocoagulation Wastewater Treatment Using Integrated Physics-Based and Machine Learning Models

Cho, K. T., Cotton, A., & Shibata, T. (2025) · Sustainability · doi:10.3390/su17104604 · Cited by 2

Sparse separable Gaussian neural network for interpretable learning of stationary solutions of Fokker–Planck–Kolmogorov equations

Yu, W., Xu, Y., Wang, X., Hong, L., Jiang, J., & Xing, S. (2025) · Nonlinear Dynamics · doi:10.1007/s11071-025-11602-5 · Cited by 2

Dimensionality Reduction for Enabling Visual Reinforcement Learning with a Classifier System

Schönberner, C., Mackensen, A., & Tomforde, S. (2025) · Genetic and Evolutionary Computation Conference Companion (GECCO Companion) · doi:10.1145/3712255.3734321 · Cited by 2

Enhancing computer-aided design with deep learning frameworks: a literature review

Steininger, S., Zhao, J., & Fottner, J. (2025) · Proceedings of the Design Society · doi:10.1017/pds.2025.10165 · Cited by 2

Query Processing Tradeoffs over an ML-Enhanced R-tree

Al-Mamun, A., Haider, C. M. R., & Aref, W. G. (2025) · ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery (GeoAI) · doi:10.1145/3764912.3770834 · Cited by 2

Multi-Scale ConvNeXt for Robust Brain Tumor Segmentation in Multimodal MRI

López-Ramírez, J. L., Hernandez-Gutierrez, F. D., Aviña-Ortiz, J. R., Bravo-Aguilar, P. D., Avina-Bravo, E. G., Ruiz-Pinales, J., & Avina‐Cervantes, J. G. (2026) · Technologies · doi:10.3390/technologies14010034 · Cited by 2

Impedance Eduction Based on Machine Learning for Acoustic Liners With Flow

Gaborit, M., Huang, X., Humbert, T., & Gabard, G. (2024) · AIAA/CEAS Aeroacoustics Conference · doi:10.2514/6.2024-3295 · Cited by 2

Fitting deep neural networks into the statistical regression modelling setting

Ha, I. D., & Burke, K. (2024) · Japanese Journal of Statistics and Data Science · doi:10.1007/s42081-024-00270-1 · Cited by 3

Using Machine Learning for Climate Modelling: Application of Neural Networks to a Slow-Fast Chaotic Dynamical System as a Case Study

Soldatenko, S. A., & Angudovich, Y. (2024) · Climate · doi:10.3390/cli12110189 · Cited by 5

On the use of feed-forward neural networks in the context of surrogate aeroelastic simulations

Roccia, B. A., Ruiz, M., & Gebhardt, C. G. (2024) · Acta Mechanica · doi:10.1007/s00707-024-04165-w · Cited by 2

Nachhaltigkeit durch KI

Sonnet, D., Moring, A., Bethge, J., & Müller, H. (2024) · SDG - Forschung, Konzepte, Lösungsansätze zur Nachhaltigkeit · doi:10.1007/978-3-658-46050-1_3 · Cited by 2

LMD: Light-Weight Prediction Quality Estimation for Object Detection in Lidar Point Clouds

Riedlinger, T., Schubert, M., Penquitt, S., Kezmann, J.-M., Colling, P., Kahl, K., Roese-Koerner, L., Arnold, M., Zimmermann, U., & Rottmann, M. (2025) · International Journal of Computer Vision · doi:10.1007/s11263-025-02377-8 · Cited by 2

Comparison of effectiveness between ChatGPT 3.5 and 4 in understanding different natural languages

Erös, B., Gritsch, C., Tick, A., & Rosenberger, P. (2025) · Journal of Intelligence Studies in Business · doi:10.37380/jisib.v14.i2.2547 · Cited by 3

Deep learning-based survival analysis with copula-based activation functions for multivariate response prediction

Kim, J., Ha, I. D., & Kim, S.-J. (2025) · Computational Statistics · doi:10.1007/s00180-025-01669-4 · Cited by 2

Deep learning of thermodynamic laws from microscopic dynamics

Kuroyanagi, H., & Yuge, T. (2025) · Physical review. E · doi:10.1103/p2z8-j69p · Cited by 2

Illumination and Shadows in Head Rotation: Experiments with Denoising Diffusion Models

Asperti, A., Colasuonno, G., & Guerra, A. (2024) · Electronics · doi:10.3390/electronics13153091 · Cited by 5

Brain functional network connectivity interpolation characterizes the neuropsychiatric continuum and heterogeneity

Li, X., Geenjaar, E., Fu, Z., Pearlson, G. D., & Calhoun, V. D. (2026) · Imaging Neuroscience · doi:10.1162/imag.a.1220 · Cited by 1

Evaluating Students’ Literacy on Safe Pesticide Use and Sustainable Resource Management: A Case Study in Alentejo, Portugal

Coelho, C., Martins, M. R., & Vicente, H. (2024) · Environments · doi:10.3390/environments11120278 · Cited by 1

Mitigating Unfair Regression in Machine Learning Model Updates

Buselli, I., López, A. P., Jiménez, E. M., Anguita, D., Roli, F., & Oneto, L. (2024) · International Conference on Machine Learning and Applications · doi:10.1109/icmla61862.2024.00289 · Cited by 1

Dynamic Link and Flow Prediction in Bank Transfer Networks

Takahashi, S., Yamamoto, K., Kobayashi, S., Kondo, R., & Hisano, R. (2025) · Studies in computational intelligence · doi:10.1007/978-3-031-82431-9_11 · Cited by 1

[Paper] Appearance-and-Spectral-Based Identification System for Penguin Individuals

Noboru, Y., Ozasa, Y., & Tanaka, M. (2025) · ITE Transactions on Media Technology and Applications · doi:10.3169/mta.13.211 · Cited by 1

Copula-based deep neural networks for clustered survival data

Kwon, J., Emura, T., & Ha, I. D. (2025) · Communications for Statistical Applications and Methods · doi:10.29220/csam.2025.32.3.389 · Cited by 1

The Deep Latent Position Block Model for Block Clustering and Latent Representation of Nodes in Networks

Boutin, R., Latouche, P., & Bouveyron, C. (2025) · Statistics and Computing · doi:10.1007/s11222-025-10679-7 · Cited by 1

SECRET: Stochasticity Emulator for Cosmic Ray Electrons

Frediani, N., Krämer, M., Mertsch, P., & Nippel, K. (2025) · Journal of Cosmology and Astroparticle Physics · doi:10.1088/1475-7516/2025/08/007 · Cited by 1

Machine Learning Estimation of the Unit Weight of Organic Soils

Borowiec, A., Straż, G., & Sulewska, M. J. (2025) · Applied Sciences · doi:10.3390/app15169079 · Cited by 1

Biases and debiasing in human and artificial intelligence

Catellani, P., & Piastra, M. (2025) · Oñati Socio-legal Series · doi:10.35295/osls.iisl.2304 · Cited by 1

Neural classification of asymptotic (in)dependence

Wixson, T. P., & Cooley, D. (2025) · Extremes · doi:10.1007/s10687-025-00520-9 · Cited by 1

A memory-efficient adjoint method to enable billion parameter optimization on a single GPU in dynamic problems

Herrmann, L., Bürchner, T., Kudela, L., & Kollmannsberger, S. (2026) · Structural and Multidisciplinary Optimization · doi:10.1007/s00158-025-04237-y · Cited by 1

Deep Learning Foundations

Jo, T. (2023) · Springer eBooks · doi:10.1007/978-3-031-32879-4 · Cited by 4

Neural Networks and Deep Learning

Aggarwal, C. C. (2023) · Springer eBooks · doi:10.1007/978-3-031-29642-0 · Cited by 190

Deep Learning with Python

Ketkar, N. S. (2017) · Apress eBooks · doi:10.1007/978-1-4842-2766-4 · Cited by 477

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