| Representing formal languages: A comparison between finite automata and recurrent neural networks JJ Michalenko Rice University, 2019 | 56* | 2019 |
| Data-mining textual responses to uncover misconception patterns JJ Michalenko, AS Lan, RG Baraniuk Proceedings of the Fourth (2017) ACM Conference on Learning@ Scale, 245-248, 2017 | 18 | 2017 |
| Semisupervised learning for seismic monitoring applications L Linville, D Anderson, J Michalenko, J Galasso, T Draelos Seismological Research Letters 92 (1), 388-395, 2021 | 8 | 2021 |
| Machine Learning Predictions of Transition Probabilities in Atomic Spectra JJ Michalenko, CM Murzyn, JD Zollweg, L Wermer, AJ Van Omen, ... Atoms 9 (1), 2, 2021 | 4 | 2021 |
| Comparing the quality of neural network uncertainty estimates for classification problems D Ries, J Michalenko, T Ganter, RIF Baiyasi, J Adams 2022 21st IEEE International Conference on Machine Learning and Applications …, 2022 | 3 | 2022 |
| Finite Automata Can be Linearly Decoded from Language-Recognizing RNNs JJ Michalenko, A Shah International Conference on Learning Representations (ICLR), 2019 | 3 | 2019 |
| Multimodal Data Fusion via Entropy Minimization JJ Michalenko, LM Linville, DZ Anderson IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium …, 2020 | 2 | 2020 |
| Personalized Feedback for Open-Response Mathematical Questions using Long Short-Term Memory Networks. JJ Michalenko, AS Lan, RG Baraniuk EDM, 2017 | 2 | 2017 |
| Semi-supervised Bayesian Low-shot Learning J Adams, K Goode, J Michalenko, P Lewis, D Ries, J Zollweg Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2021 | 1 | 2021 |
| Quantifying Epistemic Uncertainty in Binary Classification via Accuracy Gain C Qian, T Ganter, J Michalenko, F Liang, J Adams Statistical Analysis and Data Mining: The ASA Data Science Journal 17 (5 …, 2024 | | 2024 |
| Non-conformity Scores for High-Quality Uncertainty Quantification from Conformal Prediction JR Adams, B Berman, R Deka, JJ Michalenko Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2024 | | 2024 |
| Improving and Assessing the Quality of Uncertainty Quantification in Deep Learning JR Adams, R Baiyasi, B Berman, MC Darling, T Ganter, F Liang, ... Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2023 | | 2023 |
| Evaluation and Calibration of Epistemic Uncertainty C Qian, T Ganter, JJ Michalenko, F Liang, JR Adams Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2023 | | 2023 |
| The Evaluation and Calibration of Epistemic Uncertainty Estimates. C Qian, T Ganter, J Michalenko, F Liang, J Adams Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2022 | | 2022 |
| GraphAlign: Graph-Enabled Machine Learning for Seismic Event Filtering J Michalenko, I Manickam, S Heck Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2022 | | 2022 |
| Data Fusion via Neural Network Entropy Minimization for Target Detection and Multi-Sensor Event Classification D Anderson, J Garcia, L Linville, J Michalenko Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2022 | | 2022 |
| Assessing the Quality of Uncertainty Estimates in Deep Learning. J Adams, R Baiyasi, T Ganter, J Michalenko, D Ries Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2022 | | 2022 |
| Evaluating the quality of uncertainty quantification enabled deep learning models. D Ries, J Adams, T Ganter, J Michalenko Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2022 | | 2022 |
| Machine Learning Predictions of Transition Probabilities in Atomic Spectra. Atoms 2021, 9, 2 JJ Michalenko, CM Murzyn, JD Zollweg, L Wermer, AJ Van Omen, ... s Note: MDPI stays neu-tral with regard to jurisdictional claims in …, 2021 | | 2021 |
| Multimodal Data Fusion via Entropy Minimization LM Linville, JJ Michalenko, DZ Anderson Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2020 | | 2020 |
