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ORCIDMichael R. Smith 0002
Person information
- affiliation: Sandia National Laboratories, Albuquerque, NM, USA
- affiliation (former): Brigham Young University, Provo, UT, USA
Other persons with the same name
- Michael R. Smith 0001
![[0000-0003-2363-632X]](https://dblp.uni-trier.de./img/orcid-mark.12x12.png "0000-0003-2363-632X")
— University of Calgary, Department of Electrical and Computer Engineering / Department of Radiology, Canada
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2020 – today
- 2024
[j6]Eva Domschot, Ramyaa Ramyaa, Michael R. Smith:
Improving Automated Labeling for ATT&CK Tactics in Malware Threat Reports. DTRAP 5(1): 2:1-2:16 (2024)- [c17]Michael R. Smith, Renee Gooding, Jonathan Bisila, Christina L. Ting:
Anomaly Detection in Video Using Compression. MIPR 2024: 127-133 - 2023
- [c16]Michael R. Smith, Erin Acquesta, Charles Smutz, Ahmad Rushdi, Blake Moss:
Assessing the Fidelity of Explanations with Global Sensitivity Analysis. HICSS 2023: 1085-1094 - 2021
- [c15]Michael R. Smith, Stephen J. Verzi, Nicholas T. Johnson, Xin Zhou, Kanad Khanna, Sophie Quynn, Raga Krishnakumar:
Malware Generation with Specific Behaviors to Improve Machine Learning-based Detection. IEEE BigData 2021: 2160-2169 - [c14]Mallory C. Stites, Megan Nyre-Yu, Blake Moss, Charles Smutz, Michael R. Smith:
Sage Advice? The Impacts of Explanations for Machine Learning Models on Human Decision-Making in Spam Detection. HCI (36) 2021: 269-284 - 2020
- [j5]Frances S. Chance, James B. Aimone, Srideep Musuvathy, Michael R. Smith, Craig M. Vineyard, Felix Wang:
Crossing the Cleft: Communication Challenges Between Neuroscience and Artificial Intelligence. Frontiers Comput. Neurosci. 14: 39 (2020) - [c13]Michael R. Smith, Nicholas T. Johnson, Joe B. Ingram, Armida J. Carbajal, Bridget I. Haus, Eva Domschot, Ramyaa, Christopher C. Lamb, Stephen J. Verzi, W. Philip Kegelmeyer:
Mind the Gap: On Bridging the Semantic Gap between Machine Learning and Malware Analysis. AISec@CCS 2020: 49-60 - [i15]Michael R. Smith, Nicholas T. Johnson, Joe B. Ingram, Armida J. Carbajal, Ramyaa Ramyaa, Evelyn Domschot, Christopher C. Lamb, Stephen J. Verzi, W. Philip Kegelmeyer:
Mind the Gap: On Bridging the Semantic Gap between Machine Learning and Information Security. CoRR abs/2005.01800 (2020) - [i14]Justin E. Doak, Michael R. Smith, Joey B. Ingram:
Self-Updating Models with Error Remediation. CoRR abs/2005.09787 (2020)
2010 – 2019
- 2018
- [j4]Michael R. Smith, Tony R. Martinez:
The robustness of majority voting compared to filtering misclassified instances in supervised classification tasks. Artif. Intell. Rev. 49(1): 105-130 (2018) - [c12]Michael R. Smith, Joe Ingram, Christopher C. Lamb, Timothy Draelos, Justin E. Doak, James B. Aimone, Conrad D. James:
Dynamic Analysis of Executables to Detect and Characterize Malware. ICMLA 2018: 16-22 - 2017
- [c11]Aaron J. Hill, Jonathon W. Donaldson, Fredrick H. Rothganger, Craig M. Vineyard, David R. Follett, Pamela L. Follett, Michael R. Smith, Stephen J. Verzi, William Severa, Felix Wang, James B. Aimone, John H. Naegle, Conrad D. James:
A Spike-Timing Neuromorphic Architecture. ICRC 2017: 1-8 - [c10]Michael R. Smith, Aaron J. Hill, Kristofor D. Carlson, Craig M. Vineyard, Jonathon Donaldson, David R. Follett, Pamela L. Follett, John H. Naegle, Conrad D. James, James B. Aimone:
A novel digital neuromorphic architecture efficiently facilitating complex synaptic response functions applied to liquid state machines. IJCNN 2017: 2421-2428 - [i13]Michael R. Smith, Aaron J. Hill, Kristofor D. Carlson, Craig M. Vineyard, Jonathon Donaldson, David R. Follett, Pamela L. Follett, John H. Naegle, Conrad D. James, James B. Aimone:
A Digital Neuromorphic Architecture Efficiently Facilitating Complex Synaptic Response Functions Applied to Liquid State Machines. CoRR abs/1704.08306 (2017) - [i12]Michael R. Smith, Joe B. Ingram, Christopher C. Lamb, Timothy J. Draelos, Justin E. Doak, James B. Aimone, Conrad D. James:
Dynamic Analysis of Executables to Detect and Characterize Malware. CoRR abs/1711.03947 (2017) - 2016
- [j3]Michael R. Smith, Tony R. Martinez:
A Comparative Evaluation of Curriculum Learning with Filtering and Boosting in Supervised Classification Problems. Comput. Intell. 32(2): 167-195 (2016) - [j2]Michael S. Gashler, Michael R. Smith, Richard G. Morris, Tony R. Martinez:
Missing Value Imputation with Unsupervised Backpropagation. Comput. Intell. 32(2): 196-215 (2016) - 2015
- [c9]Michael S. Gashler, Zachariah Kindle, Michael R. Smith:
A minimal architecture for general cognition. IJCNN 2015: 1-8 - [c8]Michael R. Smith, Michael S. Gashler, Tony R. Martinez:
A hybrid latent variable neural network model for item recommendation. IJCNN 2015: 1-7 - [c7]Michael R. Smith, Tony R. Martinez:
Using Classifier diversity to handle label noise. IJCNN 2015: 1-8 - [c6]Michael R. Smith, Tony R. Martinez, Christophe G. Giraud-Carrier:
The Potential Benefits of Data Set Filtering and Learning Algorithm Hyperparameter Optimization. MetaSel@PKDD/ECML 2015: 3-14 - [i11]Michael S. Gashler, Zachariah Kindle, Michael R. Smith:
A Minimal Architecture for General Cognition. CoRR abs/1508.00019 (2015) - 2014
- [j1]Michael R. Smith, Tony R. Martinez, Christophe G. Giraud-Carrier:
An instance level analysis of data complexity. Mach. Learn. 95(2): 225-256 (2014) - [c5]Michael R. Smith, Logan Mitchell, Christophe G. Giraud-Carrier, Tony R. Martinez:
Recommending Learning Algorithms and Their Associated Hyperparameters. MetaSel@ECAI 2014: 39-40 - [c4]Michael R. Smith, Andrew White, Christophe G. Giraud-Carrier, Tony R. Martinez:
An Easy to Use Repository for Comparing and Improving Machine Learning Algorithm Usage. MetaSel@ECAI 2014: 41-48 - [c3]Michael R. Smith, Ryan S. Hintze, Dan Ventura:
Nehovah: A Neologism Creator Nomen Ipsum. ICCC 2014: 173-181 - [c2]Michael R. Smith, Tony R. Martinez:
Reducing the Effects of Detrimental Instances. ICMLA 2014: 183-188 - [i10]Michael R. Smith, Tony R. Martinez:
Becoming More Robust to Label Noise with Classifier Diversity. CoRR abs/1403.1893 (2014) - [i9]Michael R. Smith, Tony R. Martinez, Christophe G. Giraud-Carrier:
The Potential Benefits of Filtering Versus Hyper-Parameter Optimization. CoRR abs/1403.3342 (2014) - [i8]Michael R. Smith, Andrew White, Christophe G. Giraud-Carrier, Tony R. Martinez:
An Easy to Use Repository for Comparing and Improving Machine Learning Algorithm Usage. CoRR abs/1405.7292 (2014) - [i7]Michael R. Smith, Tony R. Martinez, Michael Gashler:
A Hybrid Latent Variable Neural Network Model for Item Recommendation. CoRR abs/1406.2235 (2014) - [i6]Michael R. Smith, Tony R. Martinez:
Reducing the Effects of Detrimental Instances. CoRR abs/1406.2237 (2014) - [i5]Michael R. Smith, Logan Mitchell, Christophe G. Giraud-Carrier, Tony R. Martinez:
Recommending Learning Algorithms and Their Associated Hyperparameters. CoRR abs/1407.1890 (2014) - [i4]Richard G. Morris, Tony R. Martinez, Michael R. Smith:
A Hierarchical Multi-Output Nearest Neighbor Model for Multi-Output Dependence Learning. CoRR abs/1410.4777 (2014) - 2013
- [i3]Michael R. Smith, Tony R. Martinez:
An Extensive Evaluation of Filtering Misclassified Instances in Supervised Classification Tasks. CoRR abs/1312.3970 (2013) - [i2]Michael R. Smith, Tony R. Martinez:
A Comparative Evaluation of Curriculum Learning with Filtering and Boosting. CoRR abs/1312.4986 (2013) - [i1]Michael Gashler, Michael R. Smith, Richard G. Morris, Tony R. Martinez:
Missing Value Imputation With Unsupervised Backpropagation. CoRR abs/1312.5394 (2013) - 2011
- [c1]Michael R. Smith, Tony R. Martinez:
Improving classification accuracy by identifying and removing instances that should be misclassified. IJCNN 2011: 2690-2697
Coauthor Index
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