AI system engineering key challenges and lessons learned

U-Net: Convolutional Networks for Biomedical Image Segmentation

Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404 · see citing works

Deep learning

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Mask R-CNN

He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.322 · Cited by 29,137 · see citing works

The Elements of Statistical Learning

Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970 · see citing works

A survey on transfer learning

Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387 · see citing works

Explaining and Harnessing Adversarial Examples

Goodfellow, I. J., Shlens, J., & Szegedy, C. (2014) · arXiv · doi:10.48550/arxiv.1412.6572 · Cited by 10,381 · see citing works

Statistical Analysis with Missing Data

Little, R. J. A., & Rubin, D. B. (2002) · Wiley Series in Probability and Statistics · doi:10.1002/9781119013563 · Cited by 8,473 · see citing works

Show, Attend and Tell: Neural Image Caption Generation with Visual Attention

Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhutdinov, R., Zemel, R., & Bengio, Y. (2015) · arXiv · doi:10.48550/arxiv.1502.03044 · Cited by 8,248 · see citing works

Intriguing properties of neural networks

Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., & Fergus, R. (2013) · arXiv · doi:10.48550/arxiv.1312.6199 · Cited by 8,143 · see citing works

Dissecting racial bias in an algorithm used to manage the health of populations

Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019) · Science · doi:10.1126/science.aax2342 · Cited by 7,137 · cited 1× · see citing works

Dropout as a bayesian approximation: Representing model uncertainty in deep learning

Gal, Y., & Ghahramani, Z. (2015) · arXiv · doi:10.48550/arxiv.1506.02142 · Cited by 6,471 · see citing works

mixup: Beyond Empirical Risk Minimization

Zhang, H., Cisse, M., Dauphin, Y. N., & Lopez-Paz, D. (2017) · arXiv · doi:10.48550/arxiv.1710.09412 · Cited by 5,831 · see citing works

The Algorithmic Foundations of Differential Privacy

Dwork, C., & Roth, A. (2014) · Foundations and Trends® in Theoretical Computer Science · doi:10.1561/0400000042 · Cited by 5,557 · see citing works

CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features

Yun, S., Han, D., Oh, S. J., Chun, S., Choe, J., & Yoo, Y. (2019) · arXiv · doi:10.48550/arxiv.1905.04899 · Cited by 5,439 · see citing works

The mythos of model interpretability

Lipton, Z. C. (2018) · Communications of the ACM · doi:10.1145/3233231 · Cited by 5,427 · see citing works

CutMix: Regularization Strategy to Train Strong Classifiers With Localizable Features

Yun, S., Han, D., Chun, S., Oh, S. J., Yoo, Y., & Choe, J. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00612 · Cited by 5,400 · see citing works

Adversarial examples in the physical world

Kurakin, A., Goodfellow, I. J., & Bengio, S. (2018) · Artificial Intelligence Safety and Security · doi:10.1201/9781351251389-8 · Cited by 5,375 · see citing works

Membership Inference Attacks Against Machine Learning Models

Shokri, R., Stronati, M., Song, C., & Shmatikov, V. (2017) · 2017 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2017.41 · Cited by 4,754 · see citing works

Beyond Accuracy: What Data Quality Means to Data Consumers

Wang, R. Y., & Strong, D. M. (1996) · Journal of Management Information Systems · doi:10.1080/07421222.1996.11518099 · Cited by 4,672 · see citing works

Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference

Jacob, B., Kligys, S., Chen, B., Zhu, M., Tang, M., Howard, A., Adam, H., & Kalenichenko, D. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00286 · Cited by 4,114 · see citing works

The Algorithmic Foundations of Differential Privacy

Dwork, C., & Roth, A. (2013) · doi:10.1561/9781601988195 · Cited by 3,856 · see citing works

Deep CORAL: Correlation Alignment for Deep Domain Adaptation

Sun, B., & Saenko, K. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-49409-8_35 · Cited by 3,757 · see citing works

MixMatch: A Holistic Approach to Semi-Supervised LearningPreprint

Berthelot, D., Carlini, N., Goodfellow, I., Papernot, N., Oliver, A., & Raffel, C. A. (2019) · Neural Information Processing Systems · doi:10.48550/arxiv.1905.02249 · Cited by 3,744 · see citing works

On calibration of modern neural networks

Guo, C., Pleiss, G., Sun, Y., & Weinberger, K. Q. (2017) · arXiv · doi:10.48550/arxiv.1706.04599 · Cited by 3,435 · see citing works

Towards A Rigorous Science of Interpretable Machine Learning

Doshi-Velez, F., & Kim, B. (2017) · arXiv · doi:10.48550/arxiv.1702.08608 · Cited by 3,121 · see citing works

Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures

Fredrikson, M., Jha, S., & Ristenpart, T. (2015) · Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2810103.2813677 · Cited by 2,961 · see citing works

Understanding deep learning requires rethinking generalization

Zhang, C., Bengio, S., Hardt, M., Recht, B., & Vinyals, O. (2016) · arXiv · doi:10.48550/arxiv.1611.03530 · Cited by 2,838 · see citing works

Knowledge Graph Embedding: A Survey of Approaches and Applications

Wang, Q., Mao, Z., Wang, B., & Guo, L. (2017) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2017.2754499 · Cited by 2,654 · see citing works

Explaining Explanations: An Overview of Interpretability of Machine Learning

Gilpin, L. H., Bau, D., Yuan, B. Z., Bajwa, A., Specter, M., & Kagal, L. (2018) · 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa.2018.00018 · Cited by 2,507 · see citing works

The Effectiveness of Data Augmentation in Image Classification using Deep Learning

Perez, L., & Wang, J. (2017) · arXiv · doi:10.48550/arxiv.1712.04621 · Cited by 2,499 · see citing works

Guidelines for Human-AI Interaction

Amershi, S., Weld, D., Vorvoreanu, M., Fourney, A., Nushi, B., Collisson, P., Suh, J., Iqbal, S., Bennett, P. N., Inkpen, K., et al. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300233 · Cited by 2,439 · see citing works

A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications

Cai, H., Zheng, V. W., & Chang, K. C.-C. (2018) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2018.2807452 · Cited by 2,034 · see citing works

Causability and explainability of artificial intelligence in medicine

Holzinger, A., Langs, G., Denk, H., Zatloukal, K., & Müller, H. (2019) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1312 · Cited by 1,885 · see citing works

Implementing Machine Learning in Health Care — Addressing Ethical Challenges

Char, D. S., Shah, N. H., & Magnus, D. (2018) · New England Journal of Medicine · doi:10.1056/nejmp1714229 · Cited by 1,857 · see citing works

Machine Learning Interpretability: A Survey on Methods and Metrics

Carvalho, D. V., Pereira, E. M., & Cardoso, J. S. (2019) · Electronics · doi:10.3390/electronics8080832 · Cited by 1,842 · see citing works

Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning

Nasr, M., Shokri, R., & Houmansadr, A. (2019) · 2019 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2019.00065 · Cited by 1,760 · see citing works

A Review of Relational Machine Learning for Knowledge Graphs

Nickel, M., Murphy, K. P., Tresp, V., & Gabrilovich, E. (2015) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2483592 · Cited by 1,684 · see citing works

Wild patterns: Ten years after the rise of adversarial machine learning

Biggio, B., & Roli, F. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.07.023 · Cited by 1,563 · see citing works

Dataset Shift in Machine Learning

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Hyperband: A Novel Bandit-Based Approach to Hyperparameter OptimizationPreprint

Li, L., Jamieson, K., DeSalvo, G., Rostamizadeh, A., & Talwalkar, A. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1603.06560 · Cited by 1,368 · see citing works

Multimodal Data Fusion: An Overview of Methods, Challenges, and Prospects

Lahat, D., Adali, T., & Jutten, C. (2015) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2460697 · Cited by 1,296 · see citing works

Big data deep learning: Challenges and perspectives

Chen, X.-W., & Lin, X. (2014) · IEEE Access · doi:10.1109/access.2014.2325029 · Cited by 1,255 · see citing works

Knowledge graph refinement: A survey of approaches and evaluation methods

Paulheim, H. (2016) · Semantic Web · doi:10.3233/sw-160218 · Cited by 1,253 · see citing works

Simultaneous Deep Transfer Across Domains and Tasks

Tzeng, E., Hoffman, J., Darrell, T., & Saenko, K. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.463 · Cited by 1,203 · see citing works

Explainable artificial intelligence: A survey

Dosilovic, F. K., Brcic, M., & Hlupic, N. (2018) · 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO) · doi:10.23919/mipro.2018.8400040 · Cited by 1,147 · see citing works

Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models

Samek, W., Wiegand, T., & Müller, K.-R. (2017) · arXiv · doi:10.48550/arxiv.1708.08296 · Cited by 1,080 · see citing works

Generating Adversarial Examples with Adversarial Networks

Xiao, C., Li, B., Zhu, J.-Y., He, W., Liu, M., & Song, D. X. (2018) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/543 · Cited by 988 · see citing works

Synthesizing Robust Adversarial ExamplesPreprint

Athalye, A., Engstrom, L., Ilyas, A., & Kwok, K. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.07397 · Cited by 973 · see citing works

Hyperband: a novel bandit-based approach to hyperparameter optimization

Li, L., Jamieson, K., DeSalvo, G., Rostamizadeh, A., & Talwalkar, A. (2017) · Journal of Machine Learning Research · Cited by 930 · see citing works

Manifold Mixup: Better Representations by Interpolating Hidden StatesPreprint

Verma, V., Lamb, A., Beckham, C., Najafi, A., Mitliagkas, I., Courville, A. C., López-Paz, D., & Bengio, Y. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.05236 · Cited by 919 · see citing works

Untitled work

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Interactive machine learning for health informatics: when do we need the human-in-the-loop?

Holzinger, A. (2016) · Brain Informatics · doi:10.1007/s40708-016-0042-6 · Cited by 906 · see citing works

Artificial Intelligence and Black‐Box Medical Decisions: <i>Accuracy versus Explainability</i>

London, A. J. (2019) · Hastings Center Report · doi:10.1002/hast.973 · Cited by 801 · see citing works

Data Augmentation Generative Adversarial NetworksPreprint

Antoniou, A., Storkey, A., & Edwards, H. S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.04340 · Cited by 797 · see citing works

Instance Weighting for Domain Adaptation in NLP

Jiang, J., & Zhai, C. (2007) · Cited by 780 · see citing works

AI can be sexist and racist — it’s time to make it fair

Zou, J., & Schiebinger, L. (2018) · Nature · doi:10.1038/d41586-018-05707-8 · Cited by 765 · see citing works

Hyper-Parameter Optimization: A Review of Algorithms and ApplicationsPreprint

Yu, T., & Zhu, H. C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.05689 · Cited by 729 · see citing works

A survey of symbolic execution techniques

Baldoni, R., Coppa, E., D’elia, D. C., Demetrescu, C., & Finocchi, I. (2019) · ACM Computing Surveys · doi:10.1145/3182657 · Cited by 708 · see citing works

An orchestrated survey of methodologies for automated software test case generation

Anand, S., Burke, E. K., Chen, T. Y., Clark, J., Cohen, M. B., Grieskamp, W., Harman, M., Harrold, M. J., McMinn, P., Bertolino, A., et al. (2013) · Journal of Systems and Software · doi:10.1016/j.jss.2013.02.061 · Cited by 669 · see citing works

Challenges in deploying machine learning: A survey of case studies

Paleyes, A., Urma, R.-G., & Lawrence, N. D. (2023) · ACM Computing Surveys · doi:10.1145/3533378 · Cited by 638 · see citing works

The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks

Carlini, N., Liu, C., Erlingsson, Ú., Kos, J., & Song, D. (2018) · arXiv · doi:10.48550/arxiv.1802.08232 · Cited by 629 · see citing works

A Survey of Methods for Explaining Black Box Models

Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., & Pedreschi, D. (2019) · ACM Computing Surveys · doi:10.1145/3236009 · Cited by 629 · see citing works

Outlier Analysis

Aggarwal, C. C. (2016) · Springer eBooks · doi:10.1007/978-3-319-47578-3 · Cited by 616 · see citing works

Central Moment Discrepancy (CMD) for Domain-Invariant Representation LearningPreprint

Zellinger, W., Grubinger, T., Lughofer, E. D., Natschläger, T., & Saminger‐Platz, S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1702.08811 · Cited by 563 · see citing works

Federated Optimization:Distributed Optimization Beyond the DatacenterPreprint

Konečný, J., McMahan, B., & Ramage, D. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.03575 · Cited by 547 · see citing works

DARPA's explainable artificial intelligence (XAI) program

Gunning, D. (2019) · Proceedings of the 24th International Conference on Intelligent User Interfaces · doi:10.1145/3301275.3308446 · Cited by 546 · see citing works

Deep Multimodal Representation Learning: A Survey

Guo, W., Wang, J., & Wang, S. (2019) · IEEE Access · doi:10.1109/access.2019.2916887 · Cited by 535 · see citing works

The limits and potentials of deep learning for robotics

Sünderhauf, N., Brock, O., Scheirer, W. J., Hadsell, R. T., Fox, D., Leitner, J., Upcroft, B., Abbeel, P., Burgard, W., Milford, M. J., et al. (2018) · QUT ePrints (Queensland University of Technology) · Cited by 519 · see citing works

Metrics for explainable AI: challenges and prospects

Hoffman, R. R., Mueller, S. T., Klein, G., & Litman, J. (2018) · arXiv · doi:10.48550/arxiv.1812.04608 · Cited by 518 · see citing works

Property Inference Attacks on Fully Connected Neural Networks using Permutation Invariant Representations

Ganju, K., Wang, Q., Yang, W., Gunter, C. A., & Borisov, N. (2018) · Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3243734.3243834 · Cited by 475 · cited 1× · see citing works

Defect prediction from static code features: current results, limitations, new approaches

Menzies, T., Milton, Z., Turhan, B., Čukić, B., Jiang, Y., & Bener, A. (2010) · Automated Software Engineering · doi:10.1007/s10515-010-0069-5 · Cited by 462 · see citing works

Why ReLU Networks Yield High-Confidence Predictions Far Away From the Training Data and How to Mitigate the Problem

Hein, M., Andriushchenko, M., & Bitterwolf, J. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00013 · Cited by 444 · see citing works

Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015

Navab, N., Joachim Hornegger, A., Wells, W. M., & Frangi, A. F. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24571-3 · Cited by 425 · see citing works

Towards a Definition of Knowledge Graphs.

Ehrlinger, L., & Wöß, W. (2016) · Cited by 410 · see citing works

Measuring the Quality of Explanations: The System Causability Scale (SCS): Comparing Human and Machine Explanations

Holzinger, A., Carrington, A., & Müller, H. (2020) · KI - Künstliche Intelligenz · doi:10.1007/s13218-020-00636-z · Cited by 406 · see citing works

Demystifying Membership Inference Attacks in Machine Learning as a Service

Truex, S., Liu, L., Gürsoy, M. E., Yu, L., & Wei, W. (2019) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2019.2897554 · Cited by 389 · see citing works

A systematic approach to exploring embedded system architectures at multiple abstraction levels

Pimentel, A. D., Erbas, C., & Polstra, S. (2006) · IEEE Transactions on Computers · doi:10.1109/tc.2006.16 · Cited by 380 · see citing works

Next-Generation Big Data Analytics: State of the Art, Challenges, and Future Research Topics

Lv, Z., Song, H. H., Val, P. B., Steed, A. J., & Jo, M. (2017) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2017.2650204 · Cited by 366 · see citing works

Industry-scale knowledge graphs

Noy, N. F., Gao, Y., Jain, A. N., Narayanan, A., Patterson, A., & Taylor, J. (2019) · Communications of the ACM · doi:10.1145/3331166 · Cited by 363 · see citing works

Deep CORAL: Correlation Alignment for Deep Domain AdaptationPreprint

Sun, B., & Saenko, K. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1607.01719 · Cited by 361 · see citing works

A comprehensive study on deep learning bug characteristics

Islam, J., Nguyen, G., Pan, R., & Rajan, H. (2019) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3338906.3338955 · Cited by 315 · see citing works

Transferring GANs: Generating Images from Limited Data

Wang, Y., Wu, C., Herranz, L., van de Weijer, J., González-García, A., & Raducanu, B. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01231-1_14 · Cited by 279 · see citing works

Simultaneous Deep Transfer Across Domains and Tasks

Hoffman, J., Tzeng, E., Darrell, T., & Saenko, K. (2017) · Advances in computer vision and pattern recognition · doi:10.1007/978-3-319-58347-1_9 · Cited by 277 · see citing works

Benchmarking TPU, GPU, and CPU Platforms for Deep LearningPreprint

Wang, Y. E., Wei, G.-Y., & Brooks, D. M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.10701 · Cited by 236 · see citing works

Generating Adversarial Examples with Adversarial NetworksPreprint

Xiao, C., Li, B., Zhu, J.-Y., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1801.02610 · Cited by 230 · see citing works

Explanatory Preferences Shape Learning and Inference

Lombrozo, T. (2016) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2016.08.001 · Cited by 229 · see citing works

Nonlinear Model Predictive Control

Grancharova, A., & Johansen, T. A. (2012) · Lecture notes in control and information sciences · doi:10.1007/978-3-642-28780-0_2 · Cited by 227 · see citing works

Current Advances, Trends and Challenges of Machine Learning and Knowledge Extraction: From Machine Learning to Explainable AI

Holzinger, A., Kieseberg, P., Weippl, E. R., & Tjoa, A. M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-99740-7_1 · Cited by 210 · see citing works

Quality analysis of source code comments

Steidl, D., Hummel, B., & Juergens, E. (2013) · International Conference on Program Comprehension · doi:10.1109/icpc.2013.6613836 · Cited by 200 · see citing works

Data Curation at Scale: The Data Tamer System

Stonebraker, M. R., Beskales, G., Pagan, A., Bruckner, D., Cherniack, M., Xu, S., Analytics, V., Ilyas, I. F., & Zdonik, S. (2013) · Cited by 190 · see citing works

Data Validation for Machine Learning

Breck, E. J., Zinkevich, M., Polyzotis, N., Whang, S. E., & Roy, S. (2019) · Cited by 182 · see citing works

Understanding Membership Inferences on Well-Generalized Learning Models

Long, Y., Bindschaedler, V., Wang, L., Bu, D., Wang, X., Tang, H., Gunter, C. A., & Chen, K. (2018) · arXiv · doi:10.48550/arxiv.1802.04889 · Cited by 166 · see citing works

Using and understanding cross-validation strategies. Perspectives on Saeb et al.

Little, M. A., Varoquaux, G. P., Saeb, S., Lonini, L., Jayaraman, A., Mohr, D. C., & Kording, K. P. (2017) · GigaScience · doi:10.1093/gigascience/gix020 · Cited by 159 · see citing works

No AI Is an Island: The Case for Teaming Intelligence

Johnson, M., & Vera, A. H. (2019) · AI Magazine · doi:10.1609/aimag.v40i1.2842 · Cited by 138 · see citing works

Blessing of dimensionality: mathematical foundations of the statistical physics of data

Gorban, A. N., & Tyukin, I. (2018) · Figshare · Cited by 137 · see citing works

Integrating artificial intelligence into health care through data access: can the GDPR act as a beacon for policymakers?

Forcier, M. B., Gallois, H., Mullan, S. M., & Joly, Y. (2019) · Journal of Law and the Biosciences · doi:10.1093/jlb/lsz013 · Cited by 135 · see citing works

Blessing of dimensionality: mathematical foundations of the statistical physics of data

Gorban, A. N., & Tyukin, I. Y. (2018) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2017.0237 · Cited by 128 · see citing works

Mathematics of Deep LearningPreprint

Vidal, R., Bruna, J., Giryes, R., & Soatto, S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1712.04741 · Cited by 126 · see citing works

Explicit Nonlinear Model Predictive Control

Grancharova, A., & Johansen, T. A. (2012) · Lecture notes in control and information sciences · doi:10.1007/978-3-642-28780-0 · Cited by 115 · see citing works

Domain-Invariant Partial-Least-Squares Regression

Nikzad‐Langerodi, R., Zellinger, W., Lughofer, E. D., & Saminger‐Platz, S. (2018) · Analytical Chemistry · doi:10.1021/acs.analchem.8b00498 · Cited by 110 · see citing works

Requirements for Data Quality Metrics

Heinrich, B., Hristova, D., Klier, M., Schiller, A., & Szubartowicz, M. (2017) · Journal of Data and Information Quality · doi:10.1145/3148238 · Cited by 108 · see citing works

Entity Resolution and Information Quality

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Robust unsupervised domain adaptation for neural networks via moment alignment

Zellinger, W., Moser, B., Grubinger, T., Lughofer, E. D., Natschläger, T., & Saminger‐Platz, S. (2019) · Information Sciences · doi:10.1016/j.ins.2019.01.025 · Cited by 98 · see citing works

Classifying Code Comments in Java Open-Source Software Systems

Pascarella, L., & Bacchelli, A. (2017) · IEEE/ACM International Conference on Mining Software Repositories (MSR) · doi:10.1109/msr.2017.63 · Cited by 93 · see citing works

Domain adaptation for regression under Beer–Lambert’s law

Nikzad‐Langerodi, R., Zellinger, W., Saminger‐Platz, S., & Moser, B. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.106447 · Cited by 81 · see citing works

An annotated fluorescence image dataset for training nuclear segmentation methods

Kromp, F., Bozsaky, E., Rifatbegovic, F., Fischer, L., Ambros, M., Berneder, M., Weiss, T., Lazic, D., Dörr, W., Hanbury, A., et al. (2020) · Scientific Data · doi:10.1038/s41597-020-00608-w · Cited by 77 · see citing works

The Judicial Demand for Explainable Artificial IntelligencePreprint

Deeks, A. S. (2019) · SSRN Electronic Journal · Cited by 74 · see citing works

NEURA ghe

Meloni, P., Capotondi, A., Deriu, G., Brian, M., Conti, F., Rossi, D., Raffo, L., & Luca Benini, C. (2018) · ACM Transactions on Reconfigurable Technology and Systems · doi:10.1145/3284357 · Cited by 73 · see citing works

GRT: Program-Analysis-Guided Random Testing (T)

Ma, L., Artho, C. V., Zhang, C., Satō, H., Gmeiner, J., & Ramler, R. (2015) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase.2015.49 · Cited by 72 · see citing works

Introduction to MAchine Learning & Knowledge Extraction (MAKE)

Holzinger, A. (2017) · Machine Learning and Knowledge Extraction · doi:10.3390/make1010001 · Cited by 65 · see citing works

Integrating risk-based testing in industrial test processes

Felderer, M., & Ramler, R. (2013) · Software Quality Journal · doi:10.1007/s11219-013-9226-y · Cited by 60 · see citing works

Introduction: Measuring Data Quality for Ongoing Improvement

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Mixture Density Generative Adversarial Networks

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Introduction to Artificial Intelligence

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Knowledge Guided Clustering Medieval Polychromy

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Contract-based Validation of Conceptual Design Bugs for Engineering Complex Machine Learning Software

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Salamzadeh, Y. (2025) · Contributions to management science · doi:10.1007/978-3-032-02526-5_4

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Uysal, M. P. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/403119

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Pourrahmani, H. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-45526-1.00006-4

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Generative AI in Systems Engineering: A Framework for Risk Assessment of Large Language Models

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Saleh, S. M., Gunda, V., Hardt, W., & Hasan, R. (2026) · International Symposium on Computer Science and Educational Technology (ISCSET) · doi:10.1109/iscset70652.2026.11722978

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Software engineering for artificial intelligence and machine learning software: A systematic literature review

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Engineering AI Systems: A Research AgendaPreprint

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