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Jamie 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
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
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
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
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
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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