TY  - JOUR
PB  - SciPost Foundation
DO  - 10.21468/SciPostPhys.19.6.141
TI  - On the role of non-linear latent features in bipartite generative neural networks
PY  - 2025/12/01
UR  - https://www.scipost.org/SciPostPhys.19.6.141
JF  - SciPost Physics
JA  - SciPost Phys.
VL  - 19
IS  - 6
SP  - 141
A1  - Bonnaire, Tony
AU  - Catania, Giovanni
AU  - Decelle, Aurélien
AU  - Seoane, Beatriz
AB  - We investigate the phase diagram and memory retrieval capabilities of Restricted Boltzmann Machines (RBMs), an archetypal model of bipartite energy-based neural networks, as a function of the prior distribution imposed on their hidden units—including binary, multi-state, and ReLU-like activations. Drawing connections to the Hopfield model and employing analytical tools from statistical physics of disordered systems, we explore how the architectural choices and activation functions shape the thermodynamic properties of these models. Our analysis reveals that standard RBMs with binary hidden nodes and extensive connectivity suffer from reduced critical capacity, limiting their effectiveness as associative memories. To address this, we examine several modifications, such as introducing local biases and adopting richer hidden unit priors. These adjustments restore ordered retrieval phases and markedly improve recall performance, even at finite temperatures. Our theoretical findings, supported by finite-size Monte Carlo simulations, highlight the importance of hidden unit design in enhancing the expressive power of RBMs.
ER  -

@Article{10.21468/SciPostPhys.19.6.141,
	title={{On the role of non-linear latent features in bipartite generative neural networks}},
	author={Tony Bonnaire and Giovanni Catania and Aurélien Decelle and Beatriz Seoane},
	journal={SciPost Phys.},
	volume={19},
	pages={141},
	year={2025},
	publisher={SciPost},
	doi={10.21468/SciPostPhys.19.6.141},
	url={https://scipost.org/10.21468/SciPostPhys.19.6.141},
}