Follow
D Dablain
Title
Cited by
Cited by
Year
DeepSMOTE: Fusing deep learning and SMOTE for imbalanced data
D Dablain, B Krawczyk, NV Chawla
IEEE Transactions on Neural Networks and Learning Systems 34 (9), 6390-6404, 2022
3012022
Understanding CNN fragility when learning with imbalanced data
D Dablain, KN Jacobson, C Bellinger, M Roberts, NV Chawla
Machine Learning 113 (7), 4785-4810, 2024
332024
Towards a holistic view of bias in machine learning: Bridging algorithmic fairness and imbalanced learning
D Dablain, B Krawczyk, N Chawla
arXiv preprint arXiv:2207.06084, 2022
152022
Efficient augmentation for imbalanced deep learning
DA Dablain, C Bellinger, B Krawczyk, NV Chawla
2023 IEEE 39th International Conference on Data Engineering (ICDE), 1433-1446, 2023
112023
Understanding imbalanced data: XAI & interpretable ML framework
D Dablain, C Bellinger, B Krawczyk, DW Aha, N Chawla
Machine Learning 113 (6), 3751-3769, 2024
52024
Towards understanding how data augmentation works with imbalanced data
DA Dablain, NV Chawla
arXiv preprint arXiv:2304.05895, 2023
52023
Towards a holistic view of bias in machine learning: bridging algorithmic fairness and imbalanced learning
D Dablain, B Krawczyk, N Chawla
Discover Data 2 (1), 4, 2024
42024
Generative AI Design and Exploration of Nucleoside Analogs
D Dablain, G Siwo, N Chawla
32021
Interpretable ML for Imbalanced Data
DA Dablain, C Bellinger, B Krawczyk, DW Aha, NV Chawla
arXiv preprint arXiv:2212.07743, 2022
22022
Data Augmentation's Effect on Machine Learning Models when Learning with Imbalanced Data
DA Dablain, NV Chawla
2024 IEEE 11th International Conference on Data Science and Advanced …, 2024
2024
The Hidden Influence of Latent Feature Magnitude When Learning with Imbalanced Data
DA Dablain, NV Chawla
arXiv preprint arXiv:2407.10165, 2024
2024
Linear Data Augmentation to Improve Generalization for Imbalanced Learning
DA Dablain
University of Notre Dame, 2024
2024
Discover Data
D Dablain, B Krawczyk, N Chawla
2024
Developing an NLP-based Recommender System for the Ethical, Legal, and Social Implications of Synthetic Biology
D Dablain, L Huang, B Sepulvado
arXiv preprint arXiv:2207.06360, 2022
2022
Developing an NLP-based Recommender System for the Ethical, Legal, and Social Implications of Synthetic Biology
The system can't perform the operation now. Try again later.
Articles 1–15