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Found 9 papers, 1 papers with code
Capabilities of Large Language Models in Control Engineering: A Benchmark Study on GPT-4, Claude 3 Opus, and Gemini 1.0 Ultra
In this paper, we explore the capabilities of state-of-the-art large language models (LLMs) such as GPT-4, Claude 3 Opus, and Gemini 1. 0 Ultra in solving undergraduate-level control problems.
Novel Quadratic Constraints for Extending LipSDP beyond Slope-Restricted Activations
However, a direct application of LipSDP to the resultant residual ReLU networks is conservative and even fails in recovering the well-known fact that the MaxMin activation is 1-Lipschitz.
A Unified Algebraic Perspective on Lipschitz Neural Networks
Important research efforts have focused on the design and training of neural networks with a controlled Lipschitz constant.
Ranked #1 on
Provable Adversarial Defense
on CIFAR-100
Revisiting PGD Attacks for Stability Analysis of Large-Scale Nonlinear Systems and Perception-Based Control
We show that the ROA analysis can be approximated as a constrained maximization problem whose goal is to find the worst-case initial condition which shifts the terminal state the most.
Model-Free $μ$ Synthesis via Adversarial Reinforcement Learning
We build a connection between robust adversarial RL and $\mu$ synthesis, and develop a model-free version of the well-known $DK$-iteration for solving state-feedback $\mu$ synthesis with static $D$-scaling.
Forced Variational Integrator Networks for Prediction and Control of Mechanical Systems
As deep learning becomes more prevalent for prediction and control of real physical systems, it is important that these overparameterized models are consistent with physically plausible dynamics.
On Imitation Learning of Linear Control Policies: Enforcing Stability and Robustness Constraints via LMI Conditions
When applying imitation learning techniques to fit a policy from expert demonstrations, one can take advantage of prior stability/robustness assumptions on the expert's policy and incorporate such control-theoretic prior knowledge explicitly into the learning process.
Learning Latent State Spaces for Planning through Reward Prediction
The latent representation is learned exclusively from multi-step reward prediction which we show to be the only necessary information for successful planning.
Model-based Reinforcement Learning
reinforcement-learning
+1
Learning to Cope with Adversarial Attacks
Hence, it is imperative that RL agents deployed in real-life applications have the capability to detect and mitigate adversarial attacks in an online fashion.
