Variational Bounds for Perceptron Learning from Structured Data | Paper Citation Record

Observation 49b95ffb-6d3b-4639-94ec-0143ee11a2e9 · outbound

This paper cites The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain.

Variational Bounds for Perceptron Learning from Structured Data The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain

Reference 1

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This paper cites Geometrical and Statistical Properties of Systems of Linear Inequalities with Applications in Pattern Recognition.

Variational Bounds for Perceptron Learning from Structured Data Geometrical and Statistical Properties of Systems of Linear Inequalities with Applications in Pattern Recognition

Reference 2

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This paper cites The Space of Interactions in Neural Network Models.

Variational Bounds for Perceptron Learning from Structured Data The Space of Interactions in Neural Network Models

Reference 3

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This paper cites Optimal Storage Properties of Neural Network Models.

Variational Bounds for Perceptron Learning from Structured Data Optimal Storage Properties of Neural Network Models

Reference 4

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This paper cites Statistical Mechanics of Learning from Examples.

Variational Bounds for Perceptron Learning from Structured Data Statistical Mechanics of Learning from Examples

Reference 5

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This paper cites The Statistical Mechanics of Learning a Rule.

Variational Bounds for Perceptron Learning from Structured Data The Statistical Mechanics of Learning a Rule

Reference 6

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Observation c25b819d-dea2-423d-bb1e-c3fc25b77410 · outbound

This paper cites Cambridge University Press, 2001.doi:10.1017/CBO9781139164542.

Variational Bounds for Perceptron Learning from Structured Data Cambridge University Press, 2001.doi:10.1017/CBO9781139164542

Reference 7

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This paper cites A Modern Maximum-Likelihood Theory for High-Dimensional Logistic Regression.

Variational Bounds for Perceptron Learning from Structured Data A Modern Maximum-Likelihood Theory for High-Dimensional Logistic Regression

Reference 8

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Observation 6e833662-d44d-4504-a631-32276d5d0c6b · outbound

This paper cites Precise Error Analy- sis of RegularizedM-Estimators in High Dimensions.

Variational Bounds for Perceptron Learning from Structured Data Precise Error Analy- sis of RegularizedM-Estimators in High Dimensions

Reference 9

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This paper cites Fundamental Lim- its of Ridge-Regularized Empirical Risk Minimization in High Dimensions.

Variational Bounds for Perceptron Learning from Structured Data Fundamental Lim- its of Ridge-Regularized Empirical Risk Minimization in High Dimensions

Reference 10

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Observation 33254b80-e6cb-4255-a52f-9849aaa4a9ed · outbound

This paper cites Generaliza- tion Error in High-Dimensional Perceptrons: Approaching Bayes Error with Convex Optimization.

Variational Bounds for Perceptron Learning from Structured Data Generaliza- tion Error in High-Dimensional Perceptrons: Approaching Bayes Error with Convex Optimization

Reference 11

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Observation 0fe84f12-7ee6-4eaf-8cb6-4e10c4ec0017 · outbound

This paper cites Optimal errors and phase transitions in high-dimensional generalized linear mod- els.

Variational Bounds for Perceptron Learning from Structured Data Optimal errors and phase transitions in high-dimensional generalized linear mod- els

Reference 12

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Observation acd0df52-ee03-4a52-831e-7c6626f7c7d3 · outbound

This paper cites Asymptotic Errors for Teacher– Student Convex Generalized Linear Models (or: How to Prove Kabashima’s Replica Formula).

Variational Bounds for Perceptron Learning from Structured Data Asymptotic Errors for Teacher– Student Convex Generalized Linear Models (or: How to Prove Kabashima’s Replica Formula)

Reference 13

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This paper cites High Dimensional Classification via Regularized and Unregularized Empirical Risk Minimization: Precise Error and Optimal Loss.

Variational Bounds for Perceptron Learning from Structured Data High Dimensional Classification via Regularized and Unregularized Empirical Risk Minimization: Precise Error and Optimal Loss

Reference 14

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Observation daddd62b-3e47-4972-9a7b-a7f0d9d12c0d · outbound

This paper cites Sharp Guarantees and Optimal Performance for Inference in Binary and Gaussian-Mixture Models.

Variational Bounds for Perceptron Learning from Structured Data Sharp Guarantees and Optimal Performance for Inference in Binary and Gaussian-Mixture Models

Reference 15

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Observation 8abc4902-e8e1-4f08-8ecf-b51d329f3e29 · outbound

This paper cites The Role of Regularization in Classification of High-dimensional Noisy Gaussian Mixture.

Variational Bounds for Perceptron Learning from Structured Data The Role of Regularization in Classification of High-dimensional Noisy Gaussian Mixture

Reference 16

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Observation f49759a4-8b76-4f74-803e-ca93793f0f2c · outbound

This paper cites Learning Gaussian Mixtures with Generalized Linear Models: Precise Asymptotics in High-Dimensions.

Variational Bounds for Perceptron Learning from Structured Data Learning Gaussian Mixtures with Generalized Linear Models: Precise Asymptotics in High-Dimensions

Reference 17

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Observation a7f4e5d0-afe2-42f1-ba6f-96976ffad7bf · outbound

This paper cites Gaussian Universality of Perceptrons with Random Labels.

Variational Bounds for Perceptron Learning from Structured Data Gaussian Universality of Perceptrons with Random Labels

Reference 18

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Observation 49ac75de-feb7-4e33-bab9-11b2dab17c06 · outbound

This paper cites Are Gaussian Data All You Need? The Extents and Limits of Universality in High-Dimensional Generalized Linear Estimation.

Variational Bounds for Perceptron Learning from Structured Data Are Gaussian Data All You Need? The Extents and Limits of Universality in High-Dimensional Generalized Linear Estimation

Reference 19

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Observation a7f080d8-cac2-4b08-8864-cc2584da94b7 · outbound

This paper cites Universality laws for Gaussian mixtures in generalized linear models.

Variational Bounds for Perceptron Learning from Structured Data Universality laws for Gaussian mixtures in generalized linear models

Reference 20

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Observation 0ca0ff27-b2ba-4b46-b7a7-298d0323b106 · outbound

This paper cites The Space of Interactions in Neural Networks: Gardner’s Computa- tion with the Cavity Method.

Variational Bounds for Perceptron Learning from Structured Data The Space of Interactions in Neural Networks: Gardner’s Computa- tion with the Cavity Method

Reference 21

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Observation 6e05acad-2ecc-4910-8431-ab13c491f879 · outbound

This paper cites Intersecting Random Half-Spaces: Toward the Gardner–Derrida Formula.

Variational Bounds for Perceptron Learning from Structured Data Intersecting Random Half-Spaces: Toward the Gardner–Derrida Formula

Reference 22

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This paper cites On the Gaussian Perceptron at High Temperature.

Variational Bounds for Perceptron Learning from Structured Data On the Gaussian Perceptron at High Temperature

Reference 23

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Observation 58967a6f-1c23-4e53-88ea-c5a2b0a06133 · outbound

This paper cites Rigorous Solution of the Gardner Prob- lem.

Variational Bounds for Perceptron Learning from Structured Data Rigorous Solution of the Gardner Prob- lem

Reference 24

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Observation 416d77c7-428b-4a9f-b53b-186bfb2a2823 · outbound

This paper cites Gardner Formula for Ising Perceptron Models at Small Densities.

Variational Bounds for Perceptron Learning from Structured Data Gardner Formula for Ising Perceptron Models at Small Densities

Reference 25

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This paper cites Characterizing Finite-Dimensional Posterior Marginals in High-Dimensional GLMs via Leave-One-Out.

Variational Bounds for Perceptron Learning from Structured Data Characterizing Finite-Dimensional Posterior Marginals in High-Dimensional GLMs via Leave-One-Out

Reference 26

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This paper cites Springer, 2010.

Variational Bounds for Perceptron Learning from Structured Data Springer, 2010

Reference 27

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Observation b6c29d4f-8f03-42d1-9a42-1b602a30291d · outbound

This paper cites The Thermodynamic Limit in Mean Field Spin Glass Models.

Variational Bounds for Perceptron Learning from Structured Data The Thermodynamic Limit in Mean Field Spin Glass Models

Reference 28

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Observation 9dc56a2a-e67e-4110-b3ff-d7c863092f5a · outbound

This paper cites Broken Replica Symmetry Bounds in the Mean Field Spin Glass Model.

Variational Bounds for Perceptron Learning from Structured Data Broken Replica Symmetry Bounds in the Mean Field Spin Glass Model

Reference 29

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This paper cites The adaptive interpolation method: a simple scheme to prove replica formulas in Bayesian inference.

Variational Bounds for Perceptron Learning from Structured Data The adaptive interpolation method: a simple scheme to prove replica formulas in Bayesian inference

Reference 30

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This paper cites The adaptive interpolation method for proving replica formulas. Applications to the Curie–Weiss and Wigner spike models.

Variational Bounds for Perceptron Learning from Structured Data The adaptive interpolation method for proving replica formulas. Applications to the Curie–Weiss and Wigner spike models

Reference 31

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This paper cites Oxford; New York: Oxford University Press, 2001.

Variational Bounds for Perceptron Learning from Structured Data Oxford; New York: Oxford University Press, 2001

Reference 32

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Observation 14bc735b-d75c-4e19-996a-c8439dbc3e58 · outbound

This paper cites Griffiths Inequalities in the Nishimori Line.

Variational Bounds for Perceptron Learning from Structured Data Griffiths Inequalities in the Nishimori Line

Reference 33

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Observation 19c769c6-0ca3-4870-a62e-f13eba3575e3 · outbound

This paper cites Surface Terms on the Nishimori Line of the Gaussian Edwards–Anderson Model.

Variational Bounds for Perceptron Learning from Structured Data Surface Terms on the Nishimori Line of the Gaussian Edwards–Anderson Model

Reference 34

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Observation b1e078ca-d68f-4f16-9aa8-308a6300d68b · outbound

This paper cites Strong Replica Symmetry in High-Dimensional Optimal Bayesian Inference.

Variational Bounds for Perceptron Learning from Structured Data Strong Replica Symmetry in High-Dimensional Optimal Bayesian Inference

Reference 35

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This paper cites On Extensions of the Brunn–Minkowski and Pr´ ekopa–Leindler Theorems, Including Inequalities for Log Concave Functions, and with an Application to the Diffusion Equation.

Variational Bounds for Perceptron Learning from Structured Data On Extensions of the Brunn–Minkowski and Pr´ ekopa–Leindler Theorems, Including Inequalities for Log Concave Functions, and with an Application to the Diffusion Equation

Reference 36

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Observation 612214c1-d7fa-44f5-93cd-5a0aae9cb97c · outbound

This paper cites Strong replica symmetry for high-dimensional disordered log-concave Gibbs measures.

Variational Bounds for Perceptron Learning from Structured Data Strong replica symmetry for high-dimensional disordered log-concave Gibbs measures

Reference 37

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This paper cites An inference prob- lem in a mismatched setting: a spin-glass model with Mattis interaction.

Variational Bounds for Perceptron Learning from Structured Data An inference prob- lem in a mismatched setting: a spin-glass model with Mattis interaction

Reference 38

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Observation c8a295c8-9c3d-42a4-becc-2cbf3bbd22f1 · outbound

This paper cites The Onset of Parisi’s Complexity in a Mismatched Inference Problem.

Variational Bounds for Perceptron Learning from Structured Data The Onset of Parisi’s Complexity in a Mismatched Inference Problem

Reference 39

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Observation 49c8569e-0f05-44aa-a5a8-30a0380aa540 · outbound

This paper cites The multi-species mean-field spin-glass on the Nishimori line.

Variational Bounds for Perceptron Learning from Structured Data The multi-species mean-field spin-glass on the Nishimori line

Reference 40

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This paper cites Information- Theoretic Reduction of Deep Neural Networks to Linear Models in the Overparametrized Proportional Regime.

Variational Bounds for Perceptron Learning from Structured Data Information- Theoretic Reduction of Deep Neural Networks to Linear Models in the Overparametrized Proportional Regime

Reference 41

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Observation d47dc182-b35e-4663-9fd4-9707f4aa7cb8 · outbound

This paper cites Statistical Physics of Deep Learning: Optimal Learning of a Multilayer Perceptron near Interpolation.

Variational Bounds for Perceptron Learning from Structured Data Statistical Physics of Deep Learning: Optimal Learning of a Multilayer Perceptron near Interpolation

Reference 42

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Observation 8b57234b-d13c-44fe-bbed-b77c672e32f7 · outbound

This paper cites The solution of the deep Boltzmann machine on the Nishimori line.

Variational Bounds for Perceptron Learning from Structured Data The solution of the deep Boltzmann machine on the Nishimori line

Reference 43

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Observation 4ae7744d-c7aa-4887-8e6a-e2e1f3a6eac9 · outbound

This paper cites Statistical inference of finite-rank tensors.

Variational Bounds for Perceptron Learning from Structured Data Statistical inference of finite-rank tensors

Reference 44

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Observation e7cc1e6f-527b-456b-b16f-7b43d7135732 · outbound

This paper cites On General Minimax Theorems.

Variational Bounds for Perceptron Learning from Structured Data On General Minimax Theorems

Reference 45

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This paper cites Performance of Bayesian linear regression in a model with mismatch.

Variational Bounds for Perceptron Learning from Structured Data Performance of Bayesian linear regression in a model with mismatch

Reference 46

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Observation 5fe29f93-a047-411c-8298-d495d982fd26 · outbound

This paper cites Some Inequalities for Gaussian Processes and Applications.

Variational Bounds for Perceptron Learning from Structured Data Some Inequalities for Gaussian Processes and Applications

Reference 47

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Observation fc9fcc43-b32e-4270-8d4b-95e84b0b6d2e · outbound

This paper cites The Gaussian min-max theorem in the Presence of Convexity.

Variational Bounds for Perceptron Learning from Structured Data The Gaussian min-max theorem in the Presence of Convexity

Reference 48

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Observation acf95938-2316-40a3-bdb8-5ae9b9310781 · outbound

This paper cites On the Stability of the Quenched State in Mean Field Spin Glass Models.

Variational Bounds for Perceptron Learning from Structured Data On the Stability of the Quenched State in Mean Field Spin Glass Models

Reference 49

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Observation a425619a-38a0-4614-a6e5-4b84e4c58ff5 · outbound

This paper cites Fundamental limits of overparametrized shallow neural networks for supervised learning.

Variational Bounds for Perceptron Learning from Structured Data Fundamental limits of overparametrized shallow neural networks for supervised learning

Reference 50

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Observation 4bdfdb0f-9086-4b16-8ee3-6608917aa419 · outbound

This paper cites A statistical mechanics framework for Bayesian deep neural networks beyond the infinite-width limit.

Variational Bounds for Perceptron Learning from Structured Data A statistical mechanics framework for Bayesian deep neural networks beyond the infinite-width limit

Reference 51

Resolution: verified exact; local_arxiv, observed 2026-08-15T14:53:59.626121Z

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Observation ded5b33e-6071-434a-8a29-7c4c959c039f · outbound

This paper cites Generalisation error in learning with random features and the hidden manifold model.

Variational Bounds for Perceptron Learning from Structured Data Generalisation error in learning with random features and the hidden manifold model

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:53:59.900748Z

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Observation 5c9140a3-28af-4b26-abd3-d1f99dae2fce · outbound

This paper cites Bayes-optimal learning of deep random networks of extensive-width.

Variational Bounds for Perceptron Learning from Structured Data Bayes-optimal learning of deep random networks of extensive-width

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:53:59.888510Z

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Observation 863b9624-76fc-4000-b0cb-1fb2a1e4d366 · outbound

This paper cites Spectral Dynamics of Learning in Restricted Boltzmann Machines.

Variational Bounds for Perceptron Learning from Structured Data Spectral Dynamics of Learning in Restricted Boltzmann Machines

Reference 54

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Observation ab528a01-eee4-4486-81cd-4c093364d62f · outbound

This paper cites Implicit Self-Regularization in Deep Neural Networks: Evidence from Random Matrix Theory and Implications for Learning.

Variational Bounds for Perceptron Learning from Structured Data Implicit Self-Regularization in Deep Neural Networks: Evidence from Random Matrix Theory and Implications for Learning

Reference 55

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Observation b0fe9a41-b3a7-45fd-bdf0-c8a3376c487d · outbound

This paper cites Cambridge University Press, 2018.doi:10.1017/9781108231596.

Variational Bounds for Perceptron Learning from Structured Data Cambridge University Press, 2018.doi:10.1017/9781108231596

Reference 56

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