1
Introduction
2
Cybenko (1989): sigmoidal density
3
Leshno–Lin–Pinkus–Schocken (1993): the polynomial dichotomy
4
Monotone networks — Mikulincer–Reichman
▶
4.1
The threshold-network model (Mikulincer–Reichman)
5
Monotone networks — Sartor et al.
▶
5.1
Beyond thresholds: saturating activations (Sartor et al. 2025)
6
Deep constrained monotonic networks — Runje et al.
▶
6.1
Constrained monotone dense layers
6.2
Skip connections and deep monotone networks
6.3
Deep monotone universal approximation
6.4
Partial monotonicity (secondary)
▶
6.4.1
Embedding non-monotone features
6.4.2
Soundness and universal approximation
6.4.3
Box-domain variant
6.4.4
Deep-core variant
7
Input-convex networks — Amos et al.
Dependency graph
Neural Network Universal Approximation — Blueprint
Davor Runje
1
Introduction
2
Cybenko (1989): sigmoidal density
3
Leshno–Lin–Pinkus–Schocken (1993): the polynomial dichotomy
4
Monotone networks — Mikulincer–Reichman
4.1
The threshold-network model (Mikulincer–Reichman)
5
Monotone networks — Sartor et al.
5.1
Beyond thresholds: saturating activations (Sartor et al. 2025)
6
Deep constrained monotonic networks — Runje et al.
6.1
Constrained monotone dense layers
6.2
Skip connections and deep monotone networks
6.3
Deep monotone universal approximation
6.4
Partial monotonicity (secondary)
6.4.1
Embedding non-monotone features
6.4.2
Soundness and universal approximation
6.4.3
Box-domain variant
6.4.4
Deep-core variant
7
Input-convex networks — Amos et al.