Improving classification accuracy by identifying and removing instances that should be misclassified. - Michael R. Smith 002; Tony R. Martinez (IJCNN, 2011)

An Extensive Evaluation of Filtering Misclassified Instances in Supervised Classification Tasks. - Michael R. Smith 002; Tony R. Martinez (CoRR, 2013)

A Comparative Evaluation of Curriculum Learning with Filtering and Boosting. - Michael R. Smith 002; Tony R. Martinez (CoRR, 2013)

Recommending Learning Algorithms and Their Associated Hyperparameters. - Michael R. Smith 002; Logan Mitchell; Christophe G. Giraud-Carrier; Tony R. Martinez (MetaSel@ECAI, 2014)

An Easy to Use Repository for Comparing and Improving Machine Learning Algorithm Usage. - Michael R. Smith 002; Andrew White; Christophe G. Giraud-Carrier; Tony R. Martinez (MetaSel@ECAI, 2014)

Nehovah: A Neologism Creator Nomen Ipsum. - Michael R. Smith 002; Ryan S. Hintze; Dan Ventura (ICCC, 2014)

Reducing the Effects of Detrimental Instances. - Michael R. Smith 002; Tony R. Martinez (ICMLA, 2014)

A Hierarchical Multi-Output Nearest Neighbor Model for Multi-Output Dependence Learning. - Richard G. Morris; Tony R. Martinez; Michael R. Smith 002 (CoRR, 2014)

Becoming More Robust to Label Noise with Classifier Diversity. - Michael R. Smith 002; Tony R. Martinez (CoRR, 2014)

The Potential Benefits of Filtering Versus Hyper-Parameter Optimization. - Michael R. Smith 002; Tony R. Martinez; Christophe G. Giraud-Carrier (CoRR, 2014)

An instance level analysis of data complexity. - Michael R. Smith 002; Tony R. Martinez; Christophe G. Giraud-Carrier (Mach. Learn., 2014)

A minimal architecture for general cognition. - Michael S. Gashler; Zachariah Kindle; Michael R. Smith 002 (IJCNN, 2015)

A hybrid latent variable neural network model for item recommendation. - Michael R. Smith 002; Michael S. Gashler; Tony R. Martinez (IJCNN, 2015)

Using Classifier diversity to handle label noise. - Michael R. Smith 002; Tony R. Martinez (IJCNN, 2015)

The Potential Benefits of Data Set Filtering and Learning Algorithm Hyperparameter Optimization. - Michael R. Smith 002; Tony R. Martinez; Christophe G. Giraud-Carrier (MetaSel@PKDD/ECML, 2015)

Missing Value Imputation with Unsupervised Backpropagation. - Michael S. Gashler; Michael R. Smith 002; Richard G. Morris; Tony R. Martinez (Comput. Intell., 2016)

A Comparative Evaluation of Curriculum Learning with Filtering and Boosting in Supervised Classification Problems. - Michael R. Smith 002; Tony R. Martinez (Comput. Intell., 2016)

A Spike-Timing Neuromorphic Architecture. - Aaron J. Hill; Jonathon W. Donaldson; Fredrick H. Rothganger; Craig M. Vineyard; David R. Follett; Pamela L. Follett; Michael R. Smith 002; Stephen J. Verzi; William Severa; Felix Wang; James B. Aimone; John H. Naegle; et al. (ICRC, 2017)

A novel digital neuromorphic architecture efficiently facilitating complex synaptic response functions applied to liquid state machines. - Michael R. Smith 002; 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 (IJCNN, 2017)

A Digital Neuromorphic Architecture Efficiently Facilitating Complex Synaptic Response Functions Applied to Liquid State Machines. - Michael R. Smith 002; 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 (CoRR, 2017)

Dynamic Analysis of Executables to Detect and Characterize Malware. - Michael R. Smith 002; Joe Ingram; Christopher C. Lamb; Timothy Draelos; Justin E. Doak; James B. Aimone; Conrad D. James (ICMLA, 2018)

The robustness of majority voting compared to filtering misclassified instances in supervised classification tasks. - Michael R. Smith 002; Tony R. Martinez (Artif. Intell. Rev., 2018)

Mind the Gap: On Bridging the Semantic Gap between Machine Learning and Malware Analysis. - Michael R. Smith 002; Nicholas T. Johnson; Joe B. Ingram; Armida J. Carbajal; Bridget I. Haus; Eva Domschot; Ramyaa; Christopher C. Lamb; Stephen J. Verzi; W. Philip Kegelmeyer (AISec@CCS, 2020)

Mind the Gap: On Bridging the Semantic Gap between Machine Learning and Information Security. - Michael R. Smith 002; Nicholas T. Johnson; Joe B. Ingram; Armida J. Carbajal; Ramyaa Ramyaa; Evelyn Domschot; Christopher C. Lamb; Stephen J. Verzi; W. Philip Kegelmeyer (CoRR, 2020)

Self-Updating Models with Error Remediation. - Justin E. Doak; Michael R. Smith 002; Joey B. Ingram (CoRR, 2020)

Crossing the Cleft: Communication Challenges Between Neuroscience and Artificial Intelligence. - Frances S. Chance; James B. Aimone; Srideep Musuvathy; Michael R. Smith 002; Craig M. Vineyard; Felix Wang (Frontiers Comput. Neurosci., 2020)

Malware Generation with Specific Behaviors to Improve Machine Learning-based Detection. - Michael R. Smith 002; Stephen J. Verzi; Nicholas T. Johnson; Xin Zhou; Kanad Khanna; Sophie Quynn; Raga Krishnakumar (IEEE BigData, 2021)

Sage Advice? The Impacts of Explanations for Machine Learning Models on Human Decision-Making in Spam Detection. - Mallory C. Stites; Megan Nyre-Yu; Blake Moss; Charles Smutz; Michael R. Smith 002 (HCI (36), 2021)

Assessing the Fidelity of Explanations with Global Sensitivity Analysis. - Michael R. Smith 002; Erin Acquesta; Charles Smutz; Ahmad Rushdi; Blake Moss (HICSS, 2023)

Anomaly Detection in Video Using Compression. - Michael R. Smith 002; Renee Gooding; Jonathan Bisila; Christina L. Ting (MIPR, 2024)

Improving Automated Labeling for ATT&CK Tactics in Malware Threat Reports. - Eva Domschot; Ramyaa Ramyaa; Michael R. Smith 002 (DTRAP, 2024)

Topology of Out-of-Distribution Examples in Deep Neural Networks. - Esha Datta; Johanna Hennig; Eva Domschot; Connor Mattes; Michael R. Smith 002 (CoRR, 2025)