Predicting User Roles from Computer Logs Using Recurrent Neural Networks. - Aaron Tuor; Samuel Kaplan; Brian Hutchinson; Nicole Nichols 001; Sean Robinson (AAAI, 2017)

Deep Learning for Unsupervised Insider Threat Detection in Structured Cybersecurity Data Streams. - Aaron Tuor; Samuel Kaplan; Brian Hutchinson; Nicole Nichols 001; Sean Robinson (AAAI Workshops, 2017)

Recurrent Neural Network Language Models for Open Vocabulary Event-Level Cyber Anomaly Detection. - Aaron Randall Tuor; Ryan Baerwolf; Nicolas Knowles; Brian Hutchinson; Nicole Nichols 001; Robert Jasper (AAAI Workshops, 2018)

Recurrent Neural Network Attention Mechanisms for Interpretable System Log Anomaly Detection. - Andy Brown; Aaron Tuor; Brian Hutchinson; Nicole Nichols 001 (CoRR, 2018)

A systematic exploration of ΔΔG cutoff ranges in machine learning models for protein mutation stability prediction. - Richard Olney; Aaron Tuor; Filip Jagodzinski; Brian Hutchinson (J. Bioinform. Comput. Biol., 2018)

Multiple Document Representations from News Alerts for Automated Bio-surveillance Event Detection. - Aaron Tuor; Fnu Anubhav; Lauren E. Charles (CoRR, 2019)

Systematic Evaluation of Backdoor Data Poisoning Attacks on Image Classifiers. - Loc Truong; Chace Jones; Brian Hutchinson; Andrew August; Brenda Praggastis; Robert Jasper; Nicole Nichols 001; Aaron Tuor (CVPR Workshops, 2020)

Constrained Neural Ordinary Differential Equations with Stability Guarantees. - Aaron Tuor; Ján Drgona; Draguna L. Vrabie (CoRR, 2020)

Constrained Physics-Informed Deep Learning for Stable System Identification and Control of Unknown Linear Systems. - Ján Drgona; Aaron Tuor; Draguna L. Vrabie (CoRR, 2020)

Differentiable Predictive Control: An MPC Alternative for Unknown Nonlinear Systems using Constrained Deep Learning. - Ján Drgona; Karol Kis; Aaron Tuor; Draguna L. Vrabie; Martin Klauco (CoRR, 2020)

Physics-constrained Deep Learning of Multi-zone Building Thermal Dynamics. - Ján Drgona; Aaron Randall Tuor; Vikas Chandan; Draguna L. Vrabie (CoRR, 2020)

Spectral Analysis and Stability of Deep Neural Dynamics. - Ján Drgona; Elliott Skomski; Soumya Vasisht; Aaron Tuor; Draguna L. Vrabie (CoRR, 2020)

Physics-Informed Neural State Space Models via Learning and Evolution. - Elliott Skomski; Ján Drgona; Aaron Tuor (CoRR, 2020)

Fuzzy Simplicial Networks: A Topology-Inspired Model to Improve Task Generalization in Few-shot Learning. - Henry Kvinge; Zachary New; Nico Courts; Jung H. Lee; Lauren A. Phillips; Courtney D. Corley; Aaron Tuor; Andrew Avila; Nathan O. Hodas (MetaDL@AAAI, 2021)

Constrained Block Nonlinear Neural Dynamical Models. - Elliott Skomski; Soumya Vasisht; Colby Wight; Aaron Tuor; Ján Drgona; Draguna L. Vrabie (ACC, 2021)

Automating Discovery of Physics-Informed Neural State Space Models via Learning and Evolution. - Elliott Skomski; Ján Drgona; Aaron Tuor (L4DC, 2021)

Prototypical Region Proposal Networks for Few-Shot Localization and Classification. - Elliott Skomski; Aaron Tuor; Andrew Avila; Lauren A. Phillips; Zachary New; Henry Kvinge; Courtney D. Corley; Nathan O. Hodas (CoRR, 2021)

Deep Learning Explicit Differentiable Predictive Control Laws for Buildings. - Ján Drgona; Aaron Tuor; Soumya Vasisht; Elliott Skomski; Draguna L. Vrabie (CoRR, 2021)

Deep Learning Classification of Cheatgrass Invasion in the Western United States Using Biophysical and Remote Sensing Data. - Kyle B. Larson; Aaron Randall Tuor (Remote. Sens., 2021)

Koopman-based Differentiable Predictive Control for the Dynamics-Aware Economic Dispatch Problem. - Ethan King; Ján Drgona; Aaron Tuor; Shrirang Abhyankar; Craig Bakker; Arnab Bhattacharya 008; Draguna L. Vrabie (ACC, 2022)

Neural Ordinary Differential Equations for Nonlinear System Identification. - Aowabin Rahman; Ján Drgona; Aaron Tuor; Jan Strube 001 (ACC, 2022)

Neural Lyapunov Differentiable Predictive Control. - Sayak Mukherjee; Ján Drgona; Aaron Tuor; Mahantesh Halappanavar; Draguna L. Vrabie (CDC, 2022)

Differentiable Predictive Control with Safety Guarantees: A Control Barrier Function Approach. - Wenceslao Shaw-Cortez; Ján Drgona; Aaron Tuor; Mahantesh Halappanavar; Draguna L. Vrabie (CDC, 2022)

Learning Stochastic Parametric Differentiable Predictive Control Policies. - Ján Drgona; Sayak Mukherjee; Aaron Tuor; Mahantesh Halappanavar; Draguna L. Vrabie (CoRR, 2022)

Neuro-physical dynamic load modeling using differentiable parametric optimization. - Shrirang Abhyankar; Ján Drgona; Andrew August; Elliott Skomski; Aaron Tuor (CoRR, 2022)

Structural Inference of Networked Dynamical Systems with Universal Differential Equations. - James Koch; Zhao Chen; Aaron Tuor; Ján Drgona; Draguna L. Vrabie (CoRR, 2022)

Domain-aware Control-oriented Neural Models for Autonomous Underwater Vehicles. - Wenceslao Shaw-Cortez; Soumya Vasisht; Aaron Tuor; Ján Drgona; Draguna L. Vrabie (CoRR, 2022)

Learning Constrained Parametric Differentiable Predictive Control Policies With Guarantees. - Ján Drgona; Aaron Tuor; Draguna L. Vrabie (IEEE Trans. Syst. Man Cybern. Syst., 2024)