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Phase Diagram of Restricted Boltzmann Machines and Generalised Hopfield Networks with Arbitrary Priors

arXiv 1702.05882 ↗First posted 20 Feb 2017cond-mat.dis-nn cs.LG physics.data-ancited by 25

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Abstract (arXiv)

Restricted Boltzmann Machines are described by the Gibbs measure of a bipartite spin glass, which in turn corresponds to the one of a generalised Hopfield network. This equivalence allows us to characterise the state of these systems in terms of retrieval capabilities, both at low and high load. We study the paramagnetic-spin glass and the spin glass-retrieval phase transitions, as the pattern (i.e. weight) distribution and spin (i.e. unit) priors vary smoothly from Gaussian real variables to Boolean discrete variables. Our analysis shows that the presence of a retrieval phase is robust and not peculiar to the standard Hopfield model with Boolean patterns. The retrieval region is larger when the pattern entries and retrieval units get more peaked and, conversely, when the hidden units acquire a broader prior and therefore have a stronger response to high fields. Moreover, at low load retrieval always exists below some critical temperature, for every pattern distribution ranging from the Boolean to the Gaussian case.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Feb 2017.

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Citing papers per year: 2021: 4; 2022: 3; 2023: 5; 2024: 5; 2025: 5; 2026: 3.

25 papers.

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  1. 2021The emergence of a concept in shallow neural networkscited by 20
  2. 2021Quantum field-theoretic machine learningcited by 16
  3. 2022Supervised Hebbian Learningcited by 16
  4. 2022Learning a Restricted Boltzmann Machine using biased Monte Carlo samplingcited by 11
  5. 2025Dense Associative Memory with Epanechnikov Energycited by 10
  6. 2021Restricted Boltzmann Machines as Models of Interacting Variablescited by 9
  7. 2023Inferring effective couplings with Restricted Boltzmann Machinescited by 8
  8. 2023Hopfield model with planted patterns: a teacher-student self-supervised learning modelcited by 7
  9. 2024Dense Hopfield Networks in the Teacher-Student Settingcited by 4
  10. 2024Fundamental operating regimes, hyper-parameter fine-tuning and glassiness: towards an interpretable replica-theory for trained restricted Boltzmann machinescited by 3
  11. 2024The effect of priors on Learning with Restricted Boltzmann Machinescited by 3
  12. 2024Modeling Structured Data Learning with Restricted Boltzmann Machines in the Teacher-Student Settingcited by 2
13 more
  1. 2021Equivalence between algorithmic instability and transition to replica symmetry breaking in perceptron learning systemscited by 1
  2. 2023Statistical Mechanics of Learning via Reverberation in Bidirectional Associative Memoriescited by 1
  3. 2025Neural Learning Rules from Associative Networks Theorycited by 1
  4. 2025On the role of non-linear latent features in bipartite generative neural networkscited by 1
  5. 2025Saddle Hierarchy in Dense Associative Memorycited by 1
  6. 2022Pattern reconstruction with restricted Boltzmann machinescited by 0
  7. 2023Local Max-Entropy and Free Energy Principles, Belief Diffusions and their Singularitiescited by 0
  8. 2023Unsupervised and Supervised learning by Dense Associative Memory under replica symmetry breakingcited by 0
  9. 2024Dataset-Free Weight-Initialization on Restricted Boltzmann Machinecited by 0
  10. 2025Learning with Restricted Boltzmann Machines: Asymptotics of AMP and GD in High Dimensionscited by 0
  11. 2026Activation Functions, Statistics and Learning of Higher-Order Interactions in Restricted Boltzmann Machinescited by 0
  12. 2026Equilibrium Training of Energy-Based Models with Parallel Trajectory Temperingcited by 0
  13. 2026Phases in a class of associative memories via hidden neuronscited by 0