Generative counterfactual introspection for explainable deep learning S Liu, B Kailkhura, D Loveland, Y Han 2019 IEEE global conference on signal and information processing (GlobalSIP …, 2019 | 115 | 2019 |
The Lick AGN Monitoring Project 2016: velocity-resolved Hβ lags in luminous Seyfert galaxies U Vivian, AJ Barth, HA Vogler, H Guo, T Treu, VN Bennert, G Canalizo, ... The Astrophysical Journal 925 (1), 52, 2022 | 43 | 2022 |
Predicting compressive strength of consolidated molecular solids using computer vision and deep learning B Gallagher, M Rever, D Loveland, TN Mundhenk, B Beauchamp, ... Materials & Design 190, 108541, 2020 | 42 | 2020 |
Predicting energetics materials’ crystalline density from chemical structure by machine learning P Nguyen, D Loveland, JT Kim, P Karande, AM Hiszpanski, TYJ Han Journal of Chemical Information and Modeling 61 (5), 2147-2158, 2021 | 34 | 2021 |
How does heterophily impact the robustness of graph neural networks? theoretical connections and practical implications J Zhu, J Jin, D Loveland, MT Schaub, D Koutra Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and …, 2022 | 30* | 2022 |
Studying the [O iii]λ5007 Å emission-line width in a sample of ∼ 80 local active galaxies: a surrogate for σ⋆? VN Bennert, D Loveland, E Donohue, M Cosens, S Lewis, S Komossa, ... Monthly Notices of the Royal Astronomical Society 481 (1), 138-152, 2018 | 22 | 2018 |
Fairedit: Preserving fairness in graph neural networks through greedy graph editing D Loveland, J Pan, AF Bhathena, Y Lu arXiv preprint arXiv:2201.03681, 2022 | 19 | 2022 |
Attribution-driven explanation of the deep neural network model via conditional microstructure image synthesis S Liu, B Kailkhura, J Zhang, AM Hiszpanski, E Robertson, D Loveland, ... ACS omega 7 (3), 2624-2637, 2022 | 7* | 2022 |
Reliable graph neural network explanations through adversarial training D Loveland, S Liu, B Kailkhura, A Hiszpanski, Y Han ICML 2021 Workshop on Theoretic Foundation, Criticism, and Application Trend …, 2021 | 7 | 2021 |
On graph neural network fairness in the presence of heterophilous neighborhoods D Loveland, J Zhu, M Heimann, B Fish, MT Schaub, D Koutra SIGKDD 2023 Deep Learning on Graphs Workshop, 2022 | 6 | 2022 |
Automated identification of molecular crystals’ packing motifs D Loveland, B Kailkhura, P Karande, AM Hiszpanski, TYJ Han Journal of Chemical Information and Modeling 60 (12), 6147-6154, 2020 | 5 | 2020 |
Zeroth-order sciml: Non-intrusive integration of scientific software with deep learning I Tsaknakis, B Kailkhura, S Liu, D Loveland, J Diffenderfer, AM Hiszpanski, ... arXiv preprint arXiv:2206.02785, 2022 | 3 | 2022 |
On Performance Discrepancies Across Local Homophily Levels in Graph Neural Networks D Loveland, J Zhu, M Heimann, B Fish, MT Schaub, D Koutra Learning on Graphs Conference 2023 (Spotlight), 2023 | 1 | 2023 |
Network Design through Graph Neural Networks: Identifying Challenges and Improving Performance D Loveland, R Caceres The 12th International Conference on Complex Networks and their Applications, 2023 | | 2023 |
VizieR Online Data Catalog: LAMP 2016: velocity-resolved Hb lags in Seyfert gal.(U+, 2022) AJ Barth, HA Vogler, H Guo, T Treu, VN Bennert, G Canalizo, ... VizieR Online Data Catalog, J/ApJ/925/52, 2023 | | 2023 |
Generative attribute optimization S Liu, T Han, B Kailkhura, D Loveland US Patent 11,436,427, 2022 | | 2022 |