Search Results for author:
Found 4 papers, 0 papers with code
Neural Differential Algebraic Equations
In this work, we show that the proposed NDAEs abstraction is suitable for relevant system-theoretic data-driven modeling tasks.
Attributing Learned Concepts in Neural Networks to Training Data
By now there is substantial evidence that deep learning models learn certain human-interpretable features as part of their internal representations of data.
Experimental Observations of the Topology of Convolutional Neural Network Activations
Topological data analysis (TDA) is a branch of computational mathematics, bridging algebraic topology and data science, that provides compact, noise-robust representations of complex structures.
The SVD of Convolutional Weights: A CNN Interpretability Framework
Fully connected layers can be studied by decomposing their weight matrices using a singular value decomposition, in effect studying the correlations between the rows in each matrix to discover the dynamics of the map.
