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2025 ICML ICML 2025

Advancing Constrained Monotonic Neural Networks: Achieving Universal Approximation Beyond Bounded Activations

Abstract

No abstract available. No abstract was captured for this paper when its proceedings were scraped - most often an older edition published without one online. How the corpus is built.

Authors

Davide Sartor, Alberto Sinigaglia, Gian Antonio Susto

Topics

Artificial Intelligence > Core AI > Interpretability Machine Learning > Optimization & Theory > Optimization Deep Learning > Architectures > Neural Networks

Keywords

universal approximation monotonic neural network activation saturation bounded activations convex monotone activations non-negative weight constraints
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