A spectral approach to Hebbian-like neural networks
From MaRDI portal
Publication:6585533
DOI10.1016/j.amc.2024.128689zbMATH Open1545.82017MaRDI QIDQ6585533
E. Agliari, Alberto Fachechi, Domenico Luongo
Publication date: 12 August 2024
Published in: Applied Mathematics and Computation (Search for Journal in Brave)
Artificial neural networks and deep learning (68T07) Neural nets applied to problems in time-dependent statistical mechanics (82C32)
Cites Work
- Title not available (Why is that?)
- Title not available (Why is that?)
- Title not available (Why is that?)
- The inverse eigenvalue problem of structured matrices from the design of Hopfield neural networks
- Lower bounds on the restitution error in the Hopfield model
- ``Neural computation of decisions in optimization problems
- On the storage capacity of Hopfield models with correlated patterns
- Large deviation principles for the Hopfield model and the Kac-Hopfield model
- The spin-glass phase-transition in the Hopfield model with \(p\)-spin interactions
- Generalized Guerra's interpolation schemes for dense associative neural networks
- Universality of covariance matrices
- Rigorous results on the thermodynamics of the dilute Hopfield model
- An almost sure large deviation principle for the Hopfield model
- The capacity of the Hopfield associative memory
- Statistical mechanics of Hopfield-like neural networks with modified interactions
- Replica symmetry breaking in neural networks with modified pseudo-inverse interactions
- Modeling Brain Function
- Hebbian learning, its correlation catastrophe, and unlearning
- High storage capacity in the Hopfield model with auto-interactions—stability analysis
- Random Matrices and Complexity of Spin Glasses
- Sharp upper bounds on perfect retrieval in the Hopfield model
- On the unlearning procedure yielding a high-performance associative memory neural network
- On the Marchenko–Pastur law in analog bipartite spin-glasses
- Random Matrix Methods for Machine Learning
- Dreaming neural networks: rigorous results
- Neural networks and physical systems with emergent collective computational abilities.
- Distributions of Matrix Variates and Latent Roots Derived from Normal Samples
- Replica symmetry breaking in neural networks: a few steps toward rigorous results
- On the critical capacity of the Hopfield model.
- Interacting dreaming neural networks
- The emergence of a concept in shallow neural networks
- On the effective initialisation for restricted Boltzmann machines via duality with Hopfield model
- Hebbian dreaming for small datasets
- Eigenvector dreaming
- Hebbian learning from first principles
This page was built for publication: A spectral approach to Hebbian-like neural networks
Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6585533)