Aaron Tuor

Orcid: 0000-0001-6951-1923

According to our database1, Aaron Tuor authored at least 28 papers between 2017 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Learning Constrained Parametric Differentiable Predictive Control Policies With Guarantees.
IEEE Trans. Syst. Man Cybern. Syst., June, 2024

2022
Domain-aware Control-oriented Neural Models for Autonomous Underwater Vehicles.
CoRR, 2022

Structural Inference of Networked Dynamical Systems with Universal Differential Equations.
CoRR, 2022

Neuro-physical dynamic load modeling using differentiable parametric optimization.
CoRR, 2022

Learning Stochastic Parametric Differentiable Predictive Control Policies.
CoRR, 2022

Differentiable Predictive Control with Safety Guarantees: A Control Barrier Function Approach.
Proceedings of the 61st IEEE Conference on Decision and Control, 2022

Neural Lyapunov Differentiable Predictive Control.
Proceedings of the 61st IEEE Conference on Decision and Control, 2022

Neural Ordinary Differential Equations for Nonlinear System Identification.
Proceedings of the American Control Conference, 2022

Koopman-based Differentiable Predictive Control for the Dynamics-Aware Economic Dispatch Problem.
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.
Remote. Sens., 2021

Deep Learning Explicit Differentiable Predictive Control Laws for Buildings.
CoRR, 2021

Prototypical Region Proposal Networks for Few-Shot Localization and Classification.
CoRR, 2021

Automating Discovery of Physics-Informed Neural State Space Models via Learning and Evolution.
Proceedings of the 3rd Annual Conference on Learning for Dynamics and Control, 2021

Constrained Block Nonlinear Neural Dynamical Models.
Proceedings of the 2021 American Control Conference, 2021

Fuzzy Simplicial Networks: A Topology-Inspired Model to Improve Task Generalization in Few-shot Learning.
Proceedings of the AAAI Workshop on Meta-Learning and MetaDL Challenge, 2021

2020
Physics-Informed Neural State Space Models via Learning and Evolution.
CoRR, 2020

Spectral Analysis and Stability of Deep Neural Dynamics.
CoRR, 2020

Physics-constrained Deep Learning of Multi-zone Building Thermal Dynamics.
CoRR, 2020

Differentiable Predictive Control: An MPC Alternative for Unknown Nonlinear Systems using Constrained Deep Learning.
CoRR, 2020

Constrained Physics-Informed Deep Learning for Stable System Identification and Control of Unknown Linear Systems.
CoRR, 2020

Constrained Neural Ordinary Differential Equations with Stability Guarantees.
CoRR, 2020

Systematic Evaluation of Backdoor Data Poisoning Attacks on Image Classifiers.
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.
CoRR, 2019

2018
A systematic exploration of ΔΔG cutoff ranges in machine learning models for protein mutation stability prediction.
J. Bioinform. Comput. Biol., 2018

Recurrent Neural Network Attention Mechanisms for Interpretable System Log Anomaly Detection.
CoRR, 2018

Recurrent Neural Network Language Models for Open Vocabulary Event-Level Cyber Anomaly Detection.
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.
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.
Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017


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