| Faim–a convnet method for unsupervised 3d medical image registration D Kuang, T Schmah Machine Learning in Medical Imaging: 10th International Workshop, MLMI 2019 …, 2019 | 120 | 2019 |
| A 1d convolutional network for leaf and time series classification D Kuang https://arxiv.org/abs/1907.00069, 2018 | 110* | 2018 |
| Kinetics and mechanism of CO2 gasification of coal catalyzed by Na2CO3, FeCO3 and Na2CO3–FeCO3 B Xu, Q Cao, D Kuang, KAM Gasem, H Adidharma, D Ding, M Fan Journal of the Energy Institute 93 (3), 922-933, 2020 | 43 | 2020 |
| Predicting kinetic triplets using a 1d convolutional neural network D Kuang, B Xu Thermochimica acta 669, 8-15, 2018 | 33 | 2018 |
| Cycle-consistent training for reducing negative jacobian determinant in deep registration networks D Kuang Simulation and Synthesis in Medical Imaging: 4th International Workshop …, 2019 | 28* | 2019 |
| Reduced models for ETG transport in the tokamak pedestal DR Hatch, C Michoski, D Kuang, B Chapman-Oplopoiou, M Curie, ... Physics of Plasmas 29 (6), 2022 | 20 | 2022 |
| Characterization of Powder River Basin coal pyrolysis with cost-effective and environmentally-friendly composite NaFe catalysts in a thermogravimetric analyzer and a fixed-bed … B Xu, D Kuang, F Liu, W Lu, AK Goroncy, T He, K Gasem, M Fan international journal of hydrogen energy 43 (14), 6918-6935, 2018 | 17 | 2018 |
| A Geodesic Landmark Shooting Algorithm for Template Matching and Its Applications R Camassa, D Kuang, L Lee SIAM Journal on Imaging Sciences 10 (1), 303-334, 2017 | 10 | 2017 |
| SEER-net: Simple EEG-based Recognition network D Kuang, C Michoski Biomedical Signal Processing and Control 83, 104620, 2023 | 7 | 2023 |
| Landmark-based algorithms for group average and pattern recognition S Huzurbazar, D Kuang, L Lee Pattern Recognition 86, 172-187, 2019 | 6* | 2019 |
| Solitary Waves and N‐Particle Algorithms for a Class of Euler–Poincaré Equations R Camassa, D Kuang, L Lee Studies in Applied Mathematics 137 (4), 502-546, 2016 | 6 | 2016 |
| Dual stream neural networks for brain signal classification [J] K Dongyang, C MICHOSKI Journal of Neural Engineering 18 (1), 016006, 2021 | 4 | 2021 |
| KAM-A kernel attention module for emotion classification with EEG data D Kuang, C Michoski International Workshop on Interpretability of Machine Intelligence in …, 2022 | 2 | 2022 |
| Some optional methods of activation energy determination on pyrolysis B Xu, D Kuang Kinetics and Catalysis 60 (2), 137-146, 2019 | 2 | 2019 |
| A conservation formulation and a numerical algorithm for the double-gyre nonlinear shallow-water model D Kuang, L Lee Numerical Mathematics: Theory, Methods and Applications 8 (4), pp 634-650, 2015 | 2 | 2015 |
| Attention with kernels for EEG-based emotion classification D Kuang, C Michoski Neural Computing and Applications 36 (10), 5251-5266, 2024 | 1 | 2024 |
| A Hierarchical Diffusion-Convolutional Network with Node-wise Localization for EEG-NIRS-based Brain-Computer Interface W Huang, X Song, D Kuang 2024 12th International Winter Conference on Brain-Computer Interface (BCI), 1-6, 2024 | 1 | 2024 |
| A monotonicity constrained attention module for emotion classification with limited EEG data D Kuang, C Michoski, W Li, R Guo Workshop on Medical Image Learning with Limited and Noisy Data, 218-228, 2022 | 1 | 2022 |
| Emotion Classification from Multi-Channel EEG Signals Using HiSTN: A Hierarchical Graph-based Spatial-Temporal Approach D Kuang, X Song, C Michoski arXiv preprint arXiv:2408.15255, 2024 | | 2024 |
| From gram to attention matrices: a monotonicity constrained method for eeg-based emotion classification D Kuang, C Michoski, W Li, R Guo Applied Intelligence 53 (18), 20690-20709, 2023 | | 2023 |
Craig MichoskiODEN Institute for Computational Engineering and Sciences在 ices.utexas.edu 的电子邮件经过验证
