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ORCIDDiane Oyen
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2020 – today
- 2024
[c24]Shamminuj Aktar, Andreas Bärtschi
, Diane Oyen, Stephan J. Eidenbenz, Abdel-Hameed A. Badawy:
Graph Neural Networks for Parameterized Quantum Circuits Expressibility Estimation. QCE 2024: 1547-1557- [i20]Shamminuj Aktar, Andreas Bärtschi, Diane Oyen, Stephan J. Eidenbenz, Abdel-Hameed A. Badawy:
Graph Neural Networks for Parameterized Quantum Circuits Expressibility Estimation. CoRR abs/2405.08100 (2024) - 2023
- [c23]Shawn M. Jones, Diane Oyen:
Discovering Image Usage Online: A Case Study with "Flatten the Curve". JCDL 2023: 293-294 - [c22]Shamminuj Aktar, Andreas Bärtschi, Abdel-Hameed A. Badawy, Diane Oyen, Stephan J. Eidenbenz:
Predicting Expressibility of Parameterized Quantum Circuits Using Graph Neural Network. QCE 2023: 401-402 - [i19]Nishant Panda, Natalie Klein, Dominic Yang, Patrick Gasda, Diane Oyen:
Semi-supervised Learning of Pushforwards For Domain Translation & Adaptation. CoRR abs/2304.08673 (2023) - [i18]Shawn M. Jones, Diane Oyen:
Discovering Image Usage Online: A Case Study With "Flatten the Curve". CoRR abs/2307.06458 (2023) - [i17]Kehinde Ajayi, Xin Wei, Martin Gryder, Winston Shields, Jian Wu, Shawn M. Jones, Michal Kucer, Diane Oyen:
DeepPatent2: A Large-Scale Benchmarking Corpus for Technical Drawing Understanding. CoRR abs/2311.04098 (2023) - 2022
- [c21]Md Reshad Ul Hoque, Xin Wei, Muntabir Hasan Choudhury, Kehinde Ajayi, Martin Gryder, Jian Wu, Diane Oyen:
Segmenting Technical Drawing Figures in US Patents. SDU@AAAI 2022 - [c20]Juan Castorena, Diane Oyen:
Spectroscopynet: Learning to Pre-Process Spectroscopy Signals Without Clean Data. IEEECONF 2022: 960-964 - [c19]Shawn M. Jones, Diane Oyen:
Abstract Images Have Different Levels of Retrievability Per Reverse Image Search Engine. ECCV Workshops (8) 2022: 203-222 - [c18]Xin Wei, Jian Wu, Kehinde Ajayi, Diane Oyen:
Visual descriptor extraction from patent figure captions: a case study of data efficiency between BiLSTM and transformer. JCDL 2022: 20 - [c17]Diane Oyen, Michal Kucer, Nicolas W. Hengartner, Har Simrat Singh:
Robustness to Label Noise Depends on the Shape of the Noise Distribution. NeurIPS 2022 - [c16]Michal Kucer, Diane Oyen, Juan Castorena, Jian Wu:
DeepPatent: Large scale patent drawing recognition and retrieval. WACV 2022: 557-566 - [i16]Diane Oyen, Michal Kucer, Nick Hengartner, Har Simrat Singh:
Robustness to Label Noise Depends on the Shape of the Noise Distribution in Feature Space. CoRR abs/2206.01106 (2022) - [i15]Shawn M. Jones, Diane Oyen:
Abstract Images Have Different Levels of Retrievability Per Reverse Image Search Engine. CoRR abs/2211.02115 (2022) - [i14]Natalie Klein, Nishant Panda, Patrick Gasda, Diane Oyen:
Generative structured normalizing flow Gaussian processes applied to spectroscopic data. CoRR abs/2212.07554 (2022) - 2021
- [c15]Ming Gong, Xin Wei, Diane Oyen, Jian Wu, Martin Gryder, Liping Yang:
Recognizing Figure Labels in Patents. SDU@AAAI 2021 - [c14]Divya Banesh, Nishant Panda, Ayan Biswas, Luke Van Roekel, Diane Oyen, Nathan M. Urban, Michael Grosskopf, Jonathan David Wolfe, Earl Lawrence:
Fast Gaussian Process Estimation for Large-Scale In Situ Inference using Convolutional Neural Networks. IEEE BigData 2021: 3731-3739 - [i13]Cory Braker Scott, Eric Mjolsness, Diane Oyen, Chie Kodera, David Bouchez, Magalie Uyttewaal:
Diff2Dist: Learning Spectrally Distinct Edge Functions, with Applications to Cell Morphology Analysis. CoRR abs/2106.15716 (2021) - [i12]Katiana Kontolati, Natalie Klein, Nishant Panda, Diane Oyen:
Neural density estimation and uncertainty quantification for laser induced breakdown spectroscopy spectra. CoRR abs/2108.08709 (2021) - [i11]Juan Castorena, Diane Oyen:
Learning to Pre-process Laser Induced Breakdown Spectroscopy Signals Without Clean Data. CoRR abs/2110.13748 (2021) - [i10]Michal Kucer, Diane Oyen, Garrett T. Kenyon:
On visual self-supervision and its effect on model robustness. CoRR abs/2112.04367 (2021) - 2020
- [j4]Nishant Panda, Dave Osthus, Gowri Srinivasan, Daniel O'Malley, Viet T. Chau, Diane Oyen, Humberto Godinez:
Mesoscale informed parameter estimation through machine learning: A case-study in fracture modeling. J. Comput. Phys. 420: 109719 (2020) - [c13]Anishi Mehta, Cory Braker Scott, Diane Oyen, Nishant Panda, Gowri Srinivasan:
Physics-Informed Spatiotemporal Deep Learning for Emulating Coupled Dynamical Systems. AAAI Spring Symposium: MLPS 2020 - [c12]Juan Castorena, Manish Bhattarai, Diane Oyen:
Learning Spatial Relationships between Samples of Patent Image Shapes. CVPR Workshops 2020: 655-662 - [c11]Manish Bhattarai, Diane Oyen, Juan Castorena, Liping Yang, Brendt Wohlberg:
Diagram Image Retrieval using Sketch-Based Deep Learning and Transfer Learning. CVPR Workshops 2020: 663-672 - [c10]Juan Castorena, Diane Oyen:
Learning Shapes on Image Sampled Points with Dynamic Graph CNNs. SSIAI 2020: 1-4 - [i9]Ming Gong, Liping Yang, Catherine Potts, Vijayan K. Asari, Diane Oyen, Brendt Wohlberg:
TGGLines: A Robust Topological Graph Guided Line Segment Detector for Low Quality Binary Images. CoRR abs/2002.12428 (2020) - [i8]Juan Castorena, Manish Bhattarai, Diane Oyen:
Learning Spatial Relationships between Samples of Patent Image Shapes. CoRR abs/2004.05713 (2020) - [i7]Manish Bhattarai, Diane Oyen, Juan Castorena, Liping Yang, Brendt Wohlberg:
Diagram Image Retrieval using Sketch-Based Deep Learning and Transfer Learning. CoRR abs/2004.10780 (2020) - [i6]Yinan Wang, Diane Oyen, Weihong Guo, Anishi Mehta, Cory Braker Scott, Nishant Panda, M. Giselle Fernández-Godino, Gowri Srinivasan, Xiaowei Yue:
StressNet: Deep Learning to Predict Stress With Fracture Propagation in Brittle Materials. CoRR abs/2011.10227 (2020) - [i5]Juan Castorena, Diane Oyen, Ann Ollila, Carey Legget, Nina Lanza:
Deep Spectral CNN for Laser Induced Breakdown Spectroscopy. CoRR abs/2012.01653 (2020)
2010 – 2019
- 2019
- [c9]Liping Yang, Diane Oyen, Brendt Wohlberg:
A Novel Algorithm for Skeleton Extraction From Images Using Topological Graph Analysis. CVPR Workshops 2019: 1162-1166 - [c8]Catherine Potts, Liping Yang, Diane Oyen, Brendt Wohlberg:
A Topological Graph-Based Representation for Denoising Low Quality Binary Images. ICCV Workshops 2019: 1788-1798 - [c7]Roozbeh Yousefzadeh, Diane Oyen, Nina Lanza:
Learning Diverse Gaussian Graphical Models and Interpreting Edges. SDM 2019: 405-413 - [i4]Carleton Coffrin, James Arnold, Stephan J. Eidenbenz, Derek Aberle, John Ambrosiano, Zachary K. Baker, Sara Brambilla, Michael J. Brown, K. Nolan Carter, Pinghan Chu, Patrick Conry, Keeley Costigan, Ariane Eberhardt, David M. Fobes, Adam Gausmann, Sean N. Harris, Donovan Heimer, Marlin Holmes, Bill Junor, Csaba Kiss, Steve Linger, Rodman R. Linn, Li-Ta Lo, Jonathan MacCarthy, Omar Marcillo, Clay McGinnis, Alexander McQuarters, Eric Michalak, Arvind T. Mohan, Matt Nelson, Diane Oyen, Nidhi Parikh, Donatella Pasqualini, Aaron S. Pope, Reid B. Porter, Chris Rawlings, Hannah Reinbolt, Reid D. Rivenburgh, Philip Romero, Kevin Schoonover, Alexei N. Skurikhin, Daniel R. Tauritz, Dima Tretiak, Zhehui Wang, James Wernicke, Brad Wolfe, Phillip Wolfram, Jonathan Woodring:
The ISTI Rapid Response on Exploring Cloud Computing 2018. CoRR abs/1901.01331 (2019) - 2018
- [i3]Patrick J. Coles, Stephan J. Eidenbenz, Scott Pakin, Adetokunbo Adedoyin, John Ambrosiano, Petr M. Anisimov, William Casper, Gopinath Chennupati, Carleton Coffrin, Hristo N. Djidjev, David Gunter, Satish Karra, Nathan Lemons, Shizeng Lin, Andrey Y. Lokhov, Alexander Malyzhenkov, David Dennis Lee Mascarenas, Susan M. Mniszewski, Balu Nadiga, Dan O'Malley, Diane Oyen, Lakshman Prasad, Randy Roberts, Philip Romero, Nandakishore Santhi, Nikolai Sinitsyn, Pieter Swart, Marc Vuffray, Jim Wendelberger, Boram Yoon, Richard J. Zamora, Wei Zhu:
Quantum Algorithm Implementations for Beginners. CoRR abs/1804.03719 (2018) - 2017
- [j3]Diane Oyen, Blake Anderson, Kari Sentz, Christine M. Anderson-Cook:
Order priors for Bayesian network discovery with an application to malware phylogeny. Stat. Anal. Data Min. 10(5): 343-358 (2017) - 2016
- [j2]Jason K. Johnson, Diane Oyen, Michael Chertkov, Praneeth Netrapalli:
Learning Planar Ising Models. J. Mach. Learn. Res. 17: 215:1-215:26 (2016) - [c6]Diane Oyen, Blake Anderson, Christine M. Anderson-Cook:
Bayesian Networks with Prior Knowledge for Malware Phylogenetics. AAAI Workshop: Artificial Intelligence for Cyber Security 2016 - 2015
- [j1]Diane Oyen, Terran Lane:
Transfer learning for Bayesian discovery of multiple Bayesian networks. Knowl. Inf. Syst. 43(1): 1-28 (2015) - [c5]Diane Oyen, Nina Lanza, Reid B. Porter:
Discovering compositional trends in Mars rock targets from ChemCam spectroscopy and remote imaging. AIPR 2015: 1-8 - [c4]Reid B. Porter, Diane Oyen, Beate G. Zimmer:
Learning Watershed Cuts Energy Functions. ISMM 2015: 497-508 - 2013
- [c3]Diane Oyen, Terran Lane:
Bayesian Discovery of Multiple Bayesian Networks via Transfer Learning. ICDM 2013: 577-586 - [i2]Diane Oyen, Terran Lane:
Bayesian Discovery of Multiple Bayesian Networks via Transfer Learning. CoRR abs/1307.2312 (2013) - [i1]Diane Oyen, Alexandru Niculescu-Mizil, Rachel Ostroff, Alex Stewart, Vincent P. Clark:
Controlling the Precision-Recall Tradeoff in Differential Dependency Network Analysis. CoRR abs/1307.2611 (2013) - 2012
- [c2]Diane Oyen, Terran Lane:
Leveraging Domain Knowledge in Multitask Bayesian Network Structure Learning. AAAI 2012: 1091-1097 - 2011
- [c1]Diane Oyen:
Active Learning of Transfer Relationships for Multiple Related Bayesian Network Structures. ICDM Workshops 2011: 1203-1206
Coauthor Index
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last updated on 2025-03-04 21:13 CET by the dblp team
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