2009 International Joint Conference on Artificial Intelligence

Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI

Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., García, S., Gil-López, S., Molina, D., Benjamins, R., et al. (2019) · arXiv · doi:10.48550/arxiv.1910.10045 · Cited by 11,321

Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., et al. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.12.012 · Cited by 10,423

An Overview of Overfitting and its Solutions

Ying, X. (2019) · Journal of Physics: Conference Series · doi:10.1088/1742-6596/1168/2/022022 · Cited by 2,352

Towards a Cognitive Theory of Emotions

Oatley, K., & Johnson-laird, P. N. (1987) · Cognition & Emotion · doi:10.1080/02699938708408362 · Cited by 1,751

Diffusion Models: A Comprehensive Survey of Methods and Applications

Yang, L., Zhang, Z., Song, Y., Hong, S., Xu, R., Zhao, Y., Zhang, W., Cui, B., & Yang, M.-H. (2024) · ACM Computing Surveys · doi:10.1145/3626235 · Cited by 1,704

Machine learning classifiers and fMRI: A tutorial overview

Pereira, F., Mitchell, T., & Botvinick, M. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2008.11.007 · Cited by 1,624

Multi-column deep neural network for traffic sign classification

Cireşan, D., Meier, U., Masci, J., & Schmidhuber, J. (2012) · Neural Networks · doi:10.1016/j.neunet.2012.02.023 · Cited by 1,057

Large scale deep learning for computer aided detection of mammographic lesions

Kooi, T., Litjens, G., van Ginneken, B., Gubern-Mérida, A., Sánchez, C. I., Mann, R., den Heeten, A., & Karssemeijer, N. (2017) · Medical Image Analysis · doi:10.1016/j.media.2016.07.007 · Cited by 965

Clustering-based undersampling in class-imbalanced data

Lin, W.-C., Tsai, C.-F., Hu, Y.-H., & Jhang, J.-S. (2017) · Information Sciences · doi:10.1016/j.ins.2017.05.008 · Cited by 832

Active inference and epistemic value

Friston, K., Rigoli, F., Ognibene, D., Mathys, C., Fitzgerald, T., & Pezzulo, G. (2015) · Cognitive Neuroscience · doi:10.1080/17588928.2015.1020053 · Cited by 731

Natural Actor-Critic

Peters, J., & Schaal, S. (2008) · Neurocomputing · doi:10.1016/j.neucom.2007.11.026 · Cited by 738

Using text mining for study identification in systematic reviews: a systematic review of current approaches

O’Mara-Eves, A., Thomas, J., McNaught, J., Miwa, M., & Ananiadou, S. (2015) · Systematic Reviews · doi:10.1186/2046-4053-4-5 · Cited by 681

Experiences with an architecture for intelligent, reactive agents

Peter Bonasso, R., James Firby, R., Gat, E., Kortenkamp, D., Miller, D. P., & Slack, M. G. (1997) · Journal of Experimental & Theoretical Artificial Intelligence · doi:10.1080/095281397147103 · Cited by 601

How to train your robot with deep reinforcement learning: Lessons we have learned

Ibarz, J., Tan, J., Finn, C., Kalakrishnan, M., Pastor, P., & Levine, S. (2021) · The International Journal of Robotics Research · doi:10.1177/0278364920987859 · Cited by 570

Least squares 3D surface and curve matching

Gruen, A., & Akca, D. (2005) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2005.02.006 · Cited by 536

Exploration in deep reinforcement learning: A survey

Ladosz, P., Weng, L., Kim, M., & Oh, H. (2022) · Information Fusion · doi:10.1016/j.inffus.2022.03.003 · Cited by 525

Financial ratios and corporate governance indicators in bankruptcy prediction: A comprehensive study

Liang, D., Lu, C.-C., Tsai, C.-F., & Shih, G.-A. (2016) · European Journal of Operational Research · doi:10.1016/j.ejor.2016.01.012 · Cited by 405

Recent Advances in Artificial Immune Systems: Models and Applications

Dasgupta, D., Yu, S., & Nino, F. (2011) · Applied Soft Computing · doi:10.1016/j.asoc.2010.08.024 · Cited by 396

When Are Tutorial Dialogues More Effective Than Reading?

VanLehn, K., Graesser, A. C., Jackson, G. T., Jordan, P., Olney, A., & Rosé, C. P. (2007) · Cognitive Science · doi:10.1080/03640210709336984 · Cited by 378

Wordcraft: Story Writing With Large Language Models

Yuan, A., Coenen, A., Reif, E., & Ippolito, D. (2022) · 27th International Conference on Intelligent User Interfaces · doi:10.1145/3490099.3511105 · Cited by 374

Feature selection with multi-view data: A survey

Zhang, R., Nie, F., Li, X., & Wei, X. (2019) · Information Fusion · doi:10.1016/j.inffus.2018.11.019 · Cited by 313

Empowering biomedical discovery with AI agents

Gao, S., Fang, A., Huang, Y., Giunchiglia, V., Noori, A., Schwarz, J. R., Ektefaie, Y., Kondic, J., & Zitnik, M. (2024) · Cell · doi:10.1016/j.cell.2024.09.022 · Cited by 315

A systematic literature review for network intrusion detection system (IDS)

Abdulganiyu, O. H., Ait Tchakoucht, T., & Saheed, Y. K. (2023) · International Journal of Information Security · doi:10.1007/s10207-023-00682-2 · Cited by 279

Deep metric learning for few-shot image classification: A Review of recent developments

Li, X., Yang, X., Ma, Z., & Xue, J.-H. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.109381 · Cited by 243

A review of the F-measure: its history, properties, criticism, and alternatives

Christen, P., Hand, D. J., & Kirielle, N. (2024) · ACM Computing Surveys · doi:10.1145/3606367 · Cited by 236

Common Sense and Sociological Explanations

Watts, D. J. (2014) · American Journal of Sociology · doi:10.1086/678271 · Cited by 176

ILP turns 20: Biography and future challenges

Muggleton, S., De Raedt, L., Poole, D., Bratko, I., Flach, P., Inoue, K., & Srinivasan, A. (2012) · Machine Learning · doi:10.1007/s10994-011-5259-2 · Cited by 126

Mixing up contrastive learning: Self-supervised representation learning for time series

Wickstrøm, K., Kampffmeyer, M., Mikalsen, K. Ø., & Jenssen, R. (2022) · Pattern Recognition Letters · doi:10.1016/j.patrec.2022.02.007 · Cited by 113

A survey of multimodal deep generative models

Suzuki, M., & Matsuo, Y. (2022) · Advanced Robotics · doi:10.1080/01691864.2022.2035253 · Cited by 109

Incomplete multi-modal representation learning for Alzheimer’s disease diagnosis

Liu, Y., Fan, L., Zhang, C., Zhou, T., Xiao, Z., Geng, L., & Shen, D. (2021) · Medical Image Analysis · doi:10.1016/j.media.2020.101953 · Cited by 102

Investigating Remote Driving over the LTE Network

Liu, R., Kwak, D., Devarakonda, S., Bekris, K., & Iftode, L. (2017) · Proceedings of the 9th International Conference on Automotive User Interfaces and Interactive Vehicular Applications · doi:10.1145/3122986.3123008 · Cited by 73

A regional estimate of postfire streamflow change in California

Bart, R. R. (2016) · Water Resources Research · doi:10.1002/2014wr016553 · Cited by 67

Feature selection and classification of imbalanced datasets

Duchesnay, E., Cachia, A., Boddaert, N., Chabane, N., Mangin, J.-F., Martinot, J.-L., Brunelle, F., & Zilbovicius, M. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.05.011 · Cited by 60

Predicting employee absenteeism for cost effective interventions

Lawrance, N., Petrides, G., & Guerry, M.-A. (2021) · Decision Support Systems · doi:10.1016/j.dss.2021.113539 · Cited by 40

DTR-HAR: deep temporal residual representation for human activity recognition

Basly, H., Ouarda, W., Sayadi, F. E., Ouni, B., & Alimi, A. M. (2022) · The Visual Computer · doi:10.1007/s00371-021-02064-y · Cited by 36

Past, Present, and Future of Sensor-based Human Activity Recognition Using Wearables: A Surveying Tutorial on a Still Challenging Task

Haresamudram, H., Tang, C. I., Suh, S., Lukowicz, P., & Plötz, T. (2025) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3729467 · Cited by 33

Sensor Data Required for Automatic Recognition of Athletic Tasks Using Deep Neural Networks

Clouthier, A. L., Ross, G. B., & Graham, R. B. (2020) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2019.00473 · Cited by 29

Player-Driven Emergence in LLM-Driven Game Narrative

Peng, X., Quaye, J., Rao, S., Xu, W., Botchway, P., Brockett, C., Jojic, N., DesGarennes, G., Lobb, K., Xu, M., et al. (2024) · 2024 IEEE Conference on Games (CoG) · doi:10.1109/cog60054.2024.10645607 · Cited by 27

Anonymous Edge Representation for Inductive Anomaly Detection in Dynamic Bipartite Graph

Fang, L., Feng, K., Gui, J., Feng, S., & Hu, A. (2023) · Proceedings of the VLDB Endowment · doi:10.14778/3579075.3579088 · Cited by 21

Building Appropriate Mental Models: What Users Know and Want to Know about an Agentic AI Chatbot

Brachman, M., Kunde, S., Miller, S., Fucs, A., Dempsey, S., Jabbour, J., & Geyer, W. (2025) · Proceedings of the 30th International Conference on Intelligent User Interfaces · doi:10.1145/3708359.3712071 · Cited by 18

Untitled work

(2024) · doi:10.1109/icra57147.2024.10611448 · Cited by 12

Quantum computing and supervised machine learning

Oneto, L., Ridella, S., & Anguita, D. (2017) · Quantum Inspired Computational Intelligence · doi:10.1016/b978-0-12-804409-4.00002-4 · Cited by 11

Computing Education Research Today

Fincher, S. A., Tenenberg, J., Dorn, B., Hundhausen, C., McCartney, R., & Murphy, L. (2019) · The Cambridge Handbook of Computing Education Research · doi:10.1017/9781108654555.003 · Cited by 7

Large language model-based planning agent with generative memory strengthens performance in textualized world

Liu, J., Hao, W., Cheng, K., & Jin, D. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110319 · Cited by 5

Computer Vision for Anomaly Detection in Optical Networks with State of Polarization Image Data: Opportunities and Challenges

Abdelli, K., Lonardi, M., Gripp, J., Olsson, S., Boitier, F., & Layec, P. (2024) · 2024 24th International Conference on Transparent Optical Networks (ICTON) · doi:10.1109/icton62926.2024.10647504 · Cited by 1