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Credit Assignment for Trained Neural Networks Based on Koopman Operator Theory
Based on Koopman operator theory, this paper presents an alternative perspective of linear dynamics on dealing with the credit assignment problem for trained neural networks.
Safety Verification for Neural Networks Based on Set-boundary Analysis
The homeomorphism property exists in some widely used NNs such as invertible NNs.
On the Properties of Kullback-Leibler Divergence Between Multivariate Gaussian Distributions
Importantly, all the bounds in the theorems presented in this paper are independent of the dimension $n$.
On Sufficient and Necessary Conditions in Bounded CTL: A Forgetting Approach
To address such a scenario in a principled way, we introduce a forgetting-based approach in CTL and show that it can be used to compute SNC and WSC of a property under a given model and over a given signature.
Kullback-Leibler Divergence-Based Out-of-Distribution Detection with Flow-Based Generative Models
For point-wise anomaly detection, our method achieves 90. 7\% AUROC on average and outperforms the baseline by 5. 2\% AUROC.
