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Statistical Field Theory for Neural Networks [[electronic resource] /] / by Moritz Helias, David Dahmen
| Statistical Field Theory for Neural Networks [[electronic resource] /] / by Moritz Helias, David Dahmen |
| Autore | Helias Moritz |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (XVII, 203 p. 127 illus., 5 illus. in color.) |
| Disciplina | 519.2 |
| Collana | Lecture Notes in Physics |
| Soggetto topico |
Statistical physics
Neurosciences Machine learning Neural networks (Computer science) Mathematical statistics Statistical Physics and Dynamical Systems Machine Learning Mathematical Models of Cognitive Processes and Neural Networks Probability and Statistics in Computer Science |
| ISBN | 3-030-46444-X |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- Probabilities, moments, cumulants -- Gaussian distribution and Wick’s theorem -- Perturbation expansion -- Linked cluster theorem -- Functional preliminaries -- Functional formulation of stochastic differential equations -- Ornstein-Uhlenbeck process: The free Gaussian theory -- Perturbation theory for stochastic differential equations -- Dynamic mean-field theory for random networks -- Vertex generating function -- Application: TAP approximation -- Expansion of cumulants into tree diagrams of vertex functions -- Loopwise expansion of the effective action - Tree level -- Loopwise expansion in the MSRDJ formalism -- Nomenclature. |
| Record Nr. | UNISA-996418173603316 |
Helias Moritz
|
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Statistical Field Theory for Neural Networks / / by Moritz Helias, David Dahmen
| Statistical Field Theory for Neural Networks / / by Moritz Helias, David Dahmen |
| Autore | Helias Moritz |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (XVII, 203 p. 127 illus., 5 illus. in color.) |
| Disciplina | 519.2 |
| Collana | Lecture Notes in Physics |
| Soggetto topico |
Statistical physics
Neurosciences Machine learning Neural networks (Computer science) Mathematical statistics Statistical Physics and Dynamical Systems Machine Learning Mathematical Models of Cognitive Processes and Neural Networks Probability and Statistics in Computer Science |
| ISBN | 3-030-46444-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- Probabilities, moments, cumulants -- Gaussian distribution and Wick’s theorem -- Perturbation expansion -- Linked cluster theorem -- Functional preliminaries -- Functional formulation of stochastic differential equations -- Ornstein-Uhlenbeck process: The free Gaussian theory -- Perturbation theory for stochastic differential equations -- Dynamic mean-field theory for random networks -- Vertex generating function -- Application: TAP approximation -- Expansion of cumulants into tree diagrams of vertex functions -- Loopwise expansion of the effective action - Tree level -- Loopwise expansion in the MSRDJ formalism -- Nomenclature. |
| Record Nr. | UNINA-9910427690903321 |
|
Helias Moritz
|
||
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||