Dense Hebbian neural networks: a replica symmetric picture of supervised learning
From MaRDI portal
Publication:6095677
DOI10.1016/j.physa.2023.129076arXiv2212.00606MaRDI QIDQ6095677
Elena Agliari, Andrea Alessandrelli, Francesco Alemanno, Dino Pedreschi, Daniele Lotito, Adriano Barra, Linda Albanese, Fosca Giannotti
Publication date: 8 September 2023
Published in: Physica A (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2212.00606
Cites Work
- Unnamed Item
- Unnamed Item
- Neural networks retrieving Boolean patterns in a sea of Gaussian ones
- The spin-glass phase-transition in the Hopfield model with \(p\)-spin interactions
- Broken replica symmetry bounds in the mean field spin glass model
- Generalized Guerra's interpolation schemes for dense associative neural networks
- The interpolation phase transition in neural networks: memorization and generalization under lazy training
- Generalization error of random feature and kernel methods: hypercontractivity and kernel matrix concentration
- Annealing and replica-symmetry in deep Boltzmann machines
- Existence of two-step replica symmetry breaking for the spherical mixed \(p\)-spin glass at zero temperature
- Thouless-Anderson-Palmer equations for generic \(p\)-spin glasses
- Universality in Sherrington-Kirkpatrick's spin glass model
- Replica symmetry breaking in dense Hebbian neural networks
- Statistical Mechanics of Learning
- Notes on the p-spin glass studied via Hamilton-Jacobi and smooth-cavity techniques
- Universality in bipartite mean field spin glasses
- Extended Plefka expansion for stochastic dynamics
- Statistical Mechanics of Neural Networks
- Modeling Brain Function
- Spin-glass theory for pedestrians
- Concentration of the complexity of spherical pure p-spin models at arbitrary energies
- A transport equation approach for deep neural networks with quenched random weights
- Geometry and Temperature Chaos in Mixed Spherical Spin Glasses at Low Temperature: The Perturbative Regime
- Machine learning and statistical physics: preface
- Nonlinear PDEs approach to statistical mechanics of dense associative memories
- The replica symmetric approximation of the analogical neural network
- On the effective initialisation for restricted Boltzmann machines via duality with Hopfield model
This page was built for publication: Dense Hebbian neural networks: a replica symmetric picture of supervised learning
