Aaron Tuor

According to our database, Aaron Tuor authored at least 27 papers between 2017 and 2022.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

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

Structural Inference of Networked Dynamical Systems with Universal Differential Equations.

James Koch, Zhao Chen, Aaron Tuor, Ján Drgona, 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

Learning Stochastic Parametric Differentiable Predictive Control Policies.

Ján Drgona, Sayak Mukherjee, Aaron Tuor, Mahantesh Halappanavar, Draguna L. Vrabie

CoRR, 2022

Differentiable Predictive Control with Safety Guarantees: A Control Barrier Function Approach.

Wenceslao Shaw-Cortez, Ján Drgona, Aaron Tuor, Mahantesh Halappanavar, Draguna L. Vrabie

Proceedings of the 61st IEEE Conference on Decision and Control, 2022

Neural Lyapunov Differentiable Predictive Control.

Sayak Mukherjee, Ján Drgona, Aaron Tuor, Mahantesh Halappanavar, Draguna L. Vrabie

Proceedings of the 61st IEEE Conference on Decision and Control, 2022

Neural Ordinary Differential Equations for Nonlinear System Identification.

Aowabin Rahman, Ján Drgona, Aaron Tuor, Jan Strube

Proceedings of the American Control Conference, 2022

Koopman-based Differentiable Predictive Control for the Dynamics-Aware Economic Dispatch Problem.

Ethan King, Ján Drgona, Aaron Tuor, Shrirang Abhyankar, Craig Bakker, Arnab Bhattacharya, Draguna L. Vrabie

Proceedings of the American Control Conference, 2022

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

Deep Learning Explicit Differentiable Predictive Control Laws for Buildings.

Ján Drgona, Aaron Tuor, Soumya Vasisht, Elliott Skomski, Draguna L. Vrabie

CoRR, 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

Automating Discovery of Physics-Informed Neural State Space Models via Learning and Evolution.

Elliott Skomski, Ján Drgona, Aaron Tuor

Proceedings of the 3rd Annual Conference on Learning for Dynamics and Control, 2021

Constrained Block Nonlinear Neural Dynamical Models.

Elliott Skomski, Soumya Vasisht, Colby Wight, Aaron Tuor, Ján Drgona, Draguna L. Vrabie

Proceedings of the 2021 American Control Conference, 2021

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

Proceedings of the AAAI Workshop on Meta-Learning and MetaDL Challenge, 2021

2020

Physics-Informed Neural State Space Models via Learning and Evolution.

Elliott Skomski, Ján Drgona, Aaron Tuor

CoRR, 2020

Spectral Analysis and Stability of Deep Neural Dynamics.

Ján Drgona, Elliott Skomski, Soumya Vasisht, Aaron Tuor, Draguna L. Vrabie

CoRR, 2020

Physics-constrained Deep Learning of Multi-zone Building Thermal Dynamics.

Ján Drgona, Aaron Randall Tuor, Vikas Chandan, 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

Constrained Physics-Informed Deep Learning for Stable System Identification and Control of Unknown Linear Systems.

Ján Drgona, Aaron Tuor, Draguna L. Vrabie

CoRR, 2020

Constrained Neural Ordinary Differential Equations with Stability Guarantees.

Aaron Tuor, Ján Drgona, Draguna L. Vrabie

CoRR, 2020

Systematic Evaluation of Backdoor Data Poisoning Attacks on Image Classifiers.

Loc Truong, Chace Jones, Brian Hutchinson, Andrew August, Brenda Praggastis, Robert Jasper, Nicole Nichols, Aaron Tuor

Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

2019

Multiple Document Representations from News Alerts for Automated Bio-surveillance Event Detection.

Aaron Tuor, Fnu Anubhav, Lauren Charles

CoRR, 2019

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

Recurrent Neural Network Attention Mechanisms for Interpretable System Log Anomaly Detection.

Andy Brown, Aaron Tuor, Brian Hutchinson, Nicole Nichols

CoRR, 2018

Recurrent Neural Network Language Models for Open Vocabulary Event-Level Cyber Anomaly Detection.

Aaron Randall Tuor, Ryan Baerwolf, Nicolas Knowles, Brian Hutchinson, Nicole Nichols, Robert Jasper

Proceedings of the Workshops of the The Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017

Deep Learning for Unsupervised Insider Threat Detection in Structured Cybersecurity Data Streams.

Aaron Tuor, Samuel Kaplan, Brian Hutchinson, Nicole Nichols, Sean Robinson

Proceedings of the Workshops of the The Thirty-First AAAI Conference on Artificial Intelligence, 2017

Predicting User Roles from Computer Logs Using Recurrent Neural Networks.

Aaron Tuor, Samuel Kaplan, Brian Hutchinson, Nicole Nichols, Sean Robinson

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017