| Pi-net: A deep learning approach to extract topological persistence images A Som, H Choi, KN Ramamurthy, MP Buman, P Turaga Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020 | 37 | 2020 |
| AMC-loss: Angular margin contrastive loss for improved explainability in image classification H Choi, A Som, P Turaga Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020 | 29 | 2020 |
| Temporal alignment improves feature quality: an experiment on activity recognition with accelerometer data H Choi, Q Wang, M Toledo, P Turaga, M Buman, A Srivastava Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 15 | 2018 |
| Role of orthogonality constraints in improving properties of deep networks for image classification H Choi, A Som, P Turaga arXiv preprint arXiv:2009.10762, 2020 | 10 | 2020 |
| Understanding the Role of Mixup in Knowledge Distillation: An Empirical Study H Choi, ES Jeon, A Shukla, P Turaga Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2022 | 8 | 2022 |
| Leveraging angular distributions for improved knowledge distillation ES Jeon, H Choi, A Shukla, P Turaga Neurocomputing 518, 466-481, 2023 | 5 | 2023 |
| Topological knowledge distillation for wearable sensor data ES Jeon, H Choi, A Shukla, Y Wang, MP Buman, P Turaga 2022 56th Asilomar Conference on Signals, Systems, and Computers, 837-842, 2022 | 4 | 2022 |
| Interpretable COVID-19 chest x-ray classification via orthogonality constraint EY Wang, A Som, A Shukla, H Choi, P Turaga ACM Conference on Health, Inference, and Learning (CHIL) Workshops. 2021, 2021 | 3 | 2021 |
| Topological persistence guided knowledge distillation for wearable sensor data ES Jeon, H Choi, A Shukla, Y Wang, H Lee, MP Buman, P Turaga Engineering Applications of Artificial Intelligence 130, 107719, 2024 | 2 | 2024 |
| Constrained Adaptive Distillation Based on Topological Persistence for Wearable Sensor Data ES Jeon, H Choi, A Shukla, Y Wang, MP Buman, P Turaga IEEE Transactions on Instrumentation and Measurement, 2023 | 1 | 2023 |
| Enhancing Accuracy and Parameter-Efficiency of Neural Representations for Network Parameterization H Choi, JJ Thiagarajan, R Glatt, S Liu arXiv preprint arXiv:2407.00356, 2024 | | 2024 |
| Unmasking the Underlying Correlations in Nuclear Reaction Cross Sections K Wendt, N Schunck, S Liu, X Chen, R Glatt, H Choi, M Huang, S Mitra, ... Bulletin of the American Physical Society, 2024 | | 2024 |
| Unmasking Correlations in Nuclear Cross Sections with Graph Neural Networks S Mitra, H Choi, S Liu, R Glatt, K Wendt, N Schunck arXiv preprint arXiv:2404.02332, 2024 | | 2024 |
| Building Reliable and Robust Deep Neural Networks with Improved Representations using Model Distillation and Deep Constraints H Choi Arizona State University, 2023 | | 2023 |
