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Cardinality-Minimal Explanations for Monotonic Neural Networks
In recent years, there has been increasing interest in explanation methods for neural model predictions that offer precise formal guarantees.
Confronting Machine Learning With Financial Research
We discuss some of the main challenges of machine learning in finance and examine how these could be accounted for.
Double-descent curves in neural networks: a new perspective using Gaussian processes
Double-descent curves in neural networks describe the phenomenon that the generalisation error initially descends with increasing parameters, then grows after reaching an optimal number of parameters which is less than the number of data points, but then descends again in the overparameterized regime.
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