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Found 11 papers, 0 papers with code
Oscillations-Aware Frequency Security Assessment via Efficient Worst-Case Frequency Nadir Computation
Frequency security assessment following major disturbances has long been one of the central tasks in power system operations.
Learning safety critics via a non-contractive binary bellman operator
To that end, we study the properties of the binary safety critic associated with a deterministic dynamical system that seeks to avoid reaching an unsafe region.
Early Neuron Alignment in Two-layer ReLU Networks with Small Initialization
Our analysis shows that, during the early phase of training, neurons in the first layer try to align with either the positive data or the negative data, depending on its corresponding weight on the second layer.
A Frequency Domain Analysis of Slow Coherency in Networked Systems
Network coherence generally refers to the emergence of simple aggregated dynamical behaviours, despite heterogeneity in the dynamics of the subsystems that constitute the network.
Learning Coherent Clusters in Weakly-Connected Network Systems
We propose a structure-preserving model-reduction methodology for large-scale dynamic networks with tightly-connected components.
Spectral clustering and model reduction for weakly-connected coherent network systems
We propose a novel model-reduction methodology for large-scale dynamic networks with tightly-connected components.
Reinforcement Learning with Almost Sure Constraints
We argue that stationary policies are not sufficient for solving this problem, and that a rich class of policies can be found by endowing the controller with a scalar quantity, so called budget, that tracks how close the agent is to violating the constraint.
Learning to Act Safely with Limited Exposure and Almost Sure Certainty
Our analysis further highlights a trade-off between the time lag for the underlying MDP necessary to detect unsafe actions, and the level of exposure to unsafe events.
Convergence and Implicit Bias of Gradient Flow on Overparametrized Linear Networks
Firstly, we show that the squared loss converges exponentially to its optimum at a rate that depends on the level of imbalance and the margin of the initialization.
Coherence and Concentration in Tightly-Connected Networks
More precisely, for a networked system with linear dynamics and coupling, we show that, as the network connectivity grows, the system transfer matrix converges to a rank-one transfer matrix representing the coherent behavior.
On the Explicit Role of Initialization on the Convergence and Generalization Properties of Overparametrized Linear Networks
In this paper, we present a novel analysis of overparametrized single-hidden layer linear networks, which formally connects initialization, optimization, and overparametrization with generalization performance.
