The CAIAS disseminates its work in the form of research articles. A selection of recent relevant publications can be found below:
Papers
- L. E. Brito da Silva, N. Rayapati and D. C. Wunsch, “Incremental Cluster Validity Index-Guided Online Learning for Performance and Robustness to Presentation Order,” in IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 10, pp. 6686-6700, Oct. 2023, doi: 10.1109/TNNLS.2022.3212345.
- L. E. Brito da Silva, N. Rayapati and D. C. Wunsch, “iCVI-ARTMAP: Using Incremental Cluster Validity Indices and Adaptive Resonance Theory Reset Mechanism to Accelerate Validation and Achieve Multiprototype Unsupervised Representations,” in IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 12, pp. 9757-9770, Dec. 2023, doi: 10.1109/TNNLS.2022.3160381.
- L. E. Brito Da Silva, N. M. Melton and D. C. Wunsch, “Incremental Cluster Validity Indices for Online Learning of Hard Partitions: Extensions and Comparative Study,” in IEEE Access, vol. 8, pp. 22025-22047, 2020, doi: 10.1109/ACCESS.2020.2969849.
- Sasha Petrenko, Andrew Brna, Mario Aguilar-Simon, et al. Lifelong Context Recognition via Online Deep Feature Clustering. TechRxiv. July 14, 2023.
- N. M. Melton, S. A. Petrenko and D. C. Wunsch, “Meta-iCVIs: Ensemble Validity Metrics for Concise Labeling of Correct, Under-or Over-Partitioning in Streaming Clustering,” in IEEE Access, doi: 10.1109/ACCESS.2023.3346058.
- Sasha Petrenko, Daniel Hier, Tayo Obafemi-Ajayi, et al. Analyzing Biomedical Datasets with Symbolic Tree Adaptive Resonance Theory. TechRxiv. December 05, 2023.
- Wu, Tao, Tie Luo, and Donald C. Wunsch. “LRS: Enhancing Adversarial Transferability through Lipschitz Regularized Surrogate.” arXiv preprint arXiv:2312.13118 (2023).
- Wu, Tao, Tie Luo, and Donald C. Wunsch. “CR-SAM: Curvature Regularized Sharpness-Aware Minimization.” arXiv preprint arXiv:2312.13555 (2023).
- T. Wu, T. Luo and D. C. Wunsch, “GNP Attack: Transferable Adversarial Examples Via Gradient Norm Penalty,” 2023 IEEE International Conference on Image Processing (ICIP), Kuala Lumpur, Malaysia, 2023, pp. 3110-3114, doi: 10.1109/ICIP49359.2023.10223158.