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Found 4 papers, 1 papers with code
Auditing and Debugging Deep Learning Models via Decision Boundaries: Individual-level and Group-level Analysis
Here, we use flip points to accomplish these goals for deep learning models with continuous output scores (e. g., computed by softmax), used in social applications.
Investigating Decision Boundaries of Trained Neural Networks
Through numerical results, we confirm that some of the speculations about the decision boundaries are accurate, some of the computational methods can be improved, and some of the simplifying assumptions may be unreliable, for models with nonlinear activation functions.
Refining the Structure of Neural Networks Using Matrix Conditioning
Here, we propose a practical method that employs matrix conditioning to automatically design the structure of layers of a feed-forward network, by first adjusting the proportion of neurons among the layers of a network and then scaling the size of network up or down.
Interpreting Neural Networks Using Flip Points
We show that distance between an input and the closest flip point identifies the most influential points in the training data.
