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ORCIDJamie Smith
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2020 – today
- 2024
[j6]Chris D. Godsil, Jamie Smith:
Strongly cospectral vertices. Australas. J Comb. 88: 1-21 (2024)- [c3]Vince Molnár
, Bence Graics, András Vörös, Stefano Tonetta, Luca Cristoforetti, Greg Kimberly, Pamela Dyer, Kristin Giammarco, Manfred Köthe, John Hester, Jamie Smith, Christoph Grimm:
Towards the Formal Verification of SysML v2 Models. MoDELS (Companion) 2024: 1086-1095 - 2023
- [c2]Berthy T. Feng, Jamie Smith, Michael Rubinstein, Huiwen Chang, Katherine L. Bouman, William T. Freeman:
Score-Based Diffusion Models as Principled Priors for Inverse Imaging. ICCV 2023: 10486-10497 - [i5]Berthy T. Feng, Jamie Smith, Michael Rubinstein, Huiwen Chang, Katherine L. Bouman, William T. Freeman:
Score-Based Diffusion Models as Principled Priors for Inverse Imaging. CoRR abs/2304.11751 (2023) - 2022
- [j5]Neha Gupta, Jamie Smith, Ben Adlam, Zelda E. Mariet:
Ensembles of Classifiers: a Bias-Variance Perspective. Trans. Mach. Learn. Res. 2022 (2022) - [j4]Samuel Kim, Peter Y. Lu, Charlotte Loh, Jamie Smith, Jasper Snoek, Marin Soljacic:
Deep Learning for Bayesian Optimization of Scientific Problems with High-Dimensional Structure. Trans. Mach. Learn. Res. 2022 (2022) - [i4]Ben Adlam, Neha Gupta, Zelda Mariet, Jamie Smith:
Understanding the bias-variance tradeoff of Bregman divergences. CoRR abs/2202.04167 (2022) - [i3]Neha Gupta, Jamie Smith, Ben Adlam, Zelda Mariet:
Ensembling over Classifiers: a Bias-Variance Perspective. CoRR abs/2206.10566 (2022) - 2021
- [i2]Samuel Kim, Peter Y. Lu, Charlotte Loh, Jamie Smith, Jasper Snoek, Marin Soljacic:
Scalable and Flexible Deep Bayesian Optimization with Auxiliary Information for Scientific Problems. CoRR abs/2104.11667 (2021) - 2020
- [j3]Amy Williams, Christy B. Turer, Jamie Smith, Isabelle Nievera, Laura McCulloch, Nuha Wareg, Megan Clary, Anuradha Rajagopalan, Ross C. Brownson, Richelle J. Koopman, Sarah Hampl:
Adoption of an Electronic Medical Record Tool for Childhood Obesity by Primary Care Providers. Appl. Clin. Inform. 11(02): 210-217 (2020) - [j2]Brandon Carter, Maxwell L. Bileschi, Jamie Smith, Theo Sanderson, Drew Bryant, David Belanger, Lucy J. Colwell:
Critiquing Protein Family Classification Models Using Sufficient Input Subsets. J. Comput. Biol. 27(8): 1219-1231 (2020)
2010 – 2019
- 2017
- [c1]Heng-Tze Cheng, Zakaria Haque, Lichan Hong, Mustafa Ispir, Clemens Mewald, Illia Polosukhin, Georgios Roumpos, D. Sculley, Jamie Smith, David Soergel, Yuan Tang, Philipp Tucker, Martin Wicke, Cassandra Xia, Jianwei Xie:
TensorFlow Estimators: Managing Simplicity vs. Flexibility in High-Level Machine Learning Frameworks. KDD 2017: 1763-1771 - [i1]Heng-Tze Cheng, Zakaria Haque, Lichan Hong, Mustafa Ispir, Clemens Mewald, Illia Polosukhin, Georgios Roumpos, D. Sculley, Jamie Smith, David Soergel, Yuan Tang, Philipp Tucker, Martin Wicke, Cassandra Xia, Jianwei Xie:
TensorFlow Estimators: Managing Simplicity vs. Flexibility in High-Level Machine Learning Frameworks. CoRR abs/1708.02637 (2017) - 2012
- [p1]Jamie Smith, Michele Mosca:
Algorithms for Quantum Computers. Handbook of Natural Computing 2012: 1451-1492 - 2011
- [j1]Jamie Smith:
On the Limitations of Graph Invariants Inspired by Quantum Walks. Electron. Notes Discret. Math. 38: 795-801 (2011)
Coauthor Index
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last updated on 2024-12-02 21:28 CET by the dblp team
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