9 citations · 33 across the 10 of their papers we have counts for
12 papers
Self-Updating Models with Error Remediation
Justin E. Doak, Michael R. Smith, Joey B. Ingram
Many environments currently employ machine learning models for data processing and analytics that were built using a limited number of training data points. Once deployed, the mode…
A Minimal Architecture for General Cognition
Michael S. Gashler, Zachariah Kindle, Michael R. Smith
A minimalistic cognitive architecture called MANIC is presented. The MANIC architecture requires only three function approximating models, and one state machine. Even with so few m…
A Hierarchical Multi-Output Nearest Neighbor Model for Multi-Output Dependence Learning
Richard G. Morris, Tony Martinez, Michael R. Smith
Multi-Output Dependence (MOD) learning is a generalization of standard classification problems that allows for multiple outputs that are dependent on each other. A primary issue th…
Recommending Learning Algorithms and Their Associated Hyperparameters
Michael R. Smith, Logan Mitchell, Christophe Giraud-Carrier +1
The success of machine learning on a given task dependson, among other things, which learning algorithm is selected and its associated hyperparameters. Selecting an appropriate lea…
A Hybrid Latent Variable Neural Network Model for Item Recommendation
Michael R. Smith, Tony Martinez, Michael Gashler
Collaborative filtering is used to recommend items to a user without requiring a knowledge of the item itself and tends to outperform other techniques. However, collaborative filte…
An Easy to Use Repository for Comparing and Improving Machine Learning Algorithm Usage
Michael R. Smith, Andrew White, Christophe Giraud-Carrier +1
The results from most machine learning experiments are used for a specific purpose and then discarded. This results in a significant loss of information and requires rerunning expe…