A theoretical framework for overfitting in energy-based modeling | Paper Citation Record

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This paper cites H., Hinton, G.

A theoretical framework for overfitting in energy-based modeling H., Hinton, G

Reference 1

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This paper cites S., Saxe, A.

A theoretical framework for overfitting in energy-based modeling S., Saxe, A

Reference 2

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This paper cites Explaining the effects of non-convergent MCMC in the training of energy-based models.

A theoretical framework for overfitting in energy-based modeling Explaining the effects of non-convergent MCMC in the training of energy-based models

Reference 3

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This paper cites From high-dimensional & mean-field dynamics to dimensionless odes: A unifying approach to sgd in two-layers networks.

A theoretical framework for overfitting in energy-based modeling From high-dimensional & mean-field dynamics to dimensionless odes: A unifying approach to sgd in two-layers networks

Reference 4

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This paper cites Scaling and renormalization in high-dimensional regression.

A theoretical framework for overfitting in energy-based modeling Scaling and renormalization in high-dimensional regression

Reference 5

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A theoretical framework for overfitting in energy-based modeling and Silverstein, J

Reference 6

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This paper cites Fit without fear: remarkable mathematical phenomena of deep learning through the prism of interpolation.

A theoretical framework for overfitting in energy-based modeling Fit without fear: remarkable mathematical phenomena of deep learning through the prism of interpolation

Reference 7

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This paper cites To understand deep learning we need to understand kernel learning.

A theoretical framework for overfitting in energy-based modeling To understand deep learning we need to understand kernel learning

Reference 8

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This paper cites K., Houkpati, Y., Irungu, J., and Oladunni, T.

A theoretical framework for overfitting in energy-based modeling K., Houkpati, Y., Irungu, J., and Oladunni, T

Reference 9

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This paper cites Learning a Restricted Boltzmann Machine using biased Monte Carlo sampling.

A theoretical framework for overfitting in energy-based modeling Learning a Restricted Boltzmann Machine using biased Monte Carlo sampling

Reference 10

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A theoretical framework for overfitting in energy-based modeling Fast, accurate training and sampling of restricted B oltzmann machines

Reference 11

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This paper cites Cleaning large correlation matrices: Tools from random matrix theory.

A theoretical framework for overfitting in energy-based modeling Cleaning large correlation matrices: Tools from random matrix theory

Reference 12

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A theoretical framework for overfitting in energy-based modeling Overlaps between eigenvectors of correlated random matrices

Reference 13

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This paper cites On lazy training in differentiable programming.

A theoretical framework for overfitting in energy-based modeling On lazy training in differentiable programming

Reference 14

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This paper cites Inverse statistical physics of protein sequences: a key issues review.

A theoretical framework for overfitting in energy-based modeling Inverse statistical physics of protein sequences: a key issues review

Reference 15

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A theoretical framework for overfitting in energy-based modeling Unresolved cited work

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This paper cites Thermodynamics of restricted B oltzmann machines and related learning dynamics.

A theoretical framework for overfitting in energy-based modeling Thermodynamics of restricted B oltzmann machines and related learning dynamics

Reference 17

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This paper cites Unsupervised hierarchical clustering using the learning dynamics of restricted B oltzmann machines.

A theoretical framework for overfitting in energy-based modeling Unsupervised hierarchical clustering using the learning dynamics of restricted B oltzmann machines

Reference 18

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A theoretical framework for overfitting in energy-based modeling Unresolved cited work

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This paper cites Inferring high-order couplings with neural networks.

A theoretical framework for overfitting in energy-based modeling Inferring high-order couplings with neural networks

Reference 20

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This paper cites Gromov–wasserstein distances between gaussian distributions.

A theoretical framework for overfitting in energy-based modeling Gromov–wasserstein distances between gaussian distributions

Reference 21

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A theoretical framework for overfitting in energy-based modeling The mnist database of handwritten digit images for machine learning research

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A theoretical framework for overfitting in energy-based modeling and Mordatch, I

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A theoretical framework for overfitting in energy-based modeling Optimal regularizations for data generation with probabilistic graphical models

Reference 24

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A theoretical framework for overfitting in energy-based modeling and Lucibello, C

Reference 25

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This paper cites Interpretable pairwise distillations for generative protein sequence models.

A theoretical framework for overfitting in energy-based modeling Interpretable pairwise distillations for generative protein sequence models

Reference 26

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A theoretical framework for overfitting in energy-based modeling Free dynamics of feature learning processes

Reference 27

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This paper cites Generalized cross-validation as a method for choosing a good ridge parameter.

A theoretical framework for overfitting in energy-based modeling Generalized cross-validation as a method for choosing a good ridge parameter

Reference 28

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This paper cites Deterministic equivalents for certain functionals of large random matrices.

A theoretical framework for overfitting in energy-based modeling Deterministic equivalents for certain functionals of large random matrices

Reference 29

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This paper cites Surprises in high-dimensional ridgeless least squares interpolation.

A theoretical framework for overfitting in energy-based modeling Surprises in high-dimensional ridgeless least squares interpolation

Reference 30

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A theoretical framework for overfitting in energy-based modeling and Dayan, P

Reference 31

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A theoretical framework for overfitting in energy-based modeling Neural tangent kernel: Convergence and generalization in neural networks

Reference 32

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A theoretical framework for overfitting in energy-based modeling Unresolved cited work

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This paper cites Estimation of quenched random fields in the inverse ising problem using a diagonal matching method.

A theoretical framework for overfitting in energy-based modeling Estimation of quenched random fields in the inverse ising problem using a diagonal matching method

Reference 34

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A theoretical framework for overfitting in energy-based modeling Learning multiple layers of features from tiny images

Reference 35

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A theoretical framework for overfitting in energy-based modeling and P \'e ch \'e , S

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A theoretical framework for overfitting in energy-based modeling and Wolf, M

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A theoretical framework for overfitting in energy-based modeling and Wolf, M

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A theoretical framework for overfitting in energy-based modeling Restoring balance: principled under/oversampling of data for optimal classification

Reference 39

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A theoretical framework for overfitting in energy-based modeling Unresolved cited work

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A theoretical framework for overfitting in energy-based modeling Unresolved cited work

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A theoretical framework for overfitting in energy-based modeling A large scale analysis of logistic regression: Asymptotic performance and new insights

Reference 42

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A theoretical framework for overfitting in energy-based modeling and Pastur, L

Reference 43

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A theoretical framework for overfitting in energy-based modeling A mean field view of the landscape of two-layer neural networks

Reference 44

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A theoretical framework for overfitting in energy-based modeling S., Sander, C., Zecchina, R., Onuchic, J

Reference 45

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A theoretical framework for overfitting in energy-based modeling C., Zecchina, R., and Berg, J

Reference 46

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A theoretical framework for overfitting in energy-based modeling Failures and successes of cross-validation for early-stopped gradient descent

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A theoretical framework for overfitting in energy-based modeling and Bouchaud, J.-P

Reference 48

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A theoretical framework for overfitting in energy-based modeling On the spectral bias of neural networks

Reference 49

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A theoretical framework for overfitting in energy-based modeling The bethe approximation for solving the inverse I sing problem: a comparison with other inference methods

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A theoretical framework for overfitting in energy-based modeling Unresolved cited work

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A theoretical framework for overfitting in energy-based modeling and Solla, S

Reference 52

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A theoretical framework for overfitting in energy-based modeling M., McClelland, J

Reference 53

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A theoretical framework for overfitting in energy-based modeling and Kubo, R

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A theoretical framework for overfitting in energy-based modeling M., Sclocchi, A., and Wyart, M

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This paper cites Learning protein constitutive motifs from sequence data.

A theoretical framework for overfitting in energy-based modeling Learning protein constitutive motifs from sequence data

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A theoretical framework for overfitting in energy-based modeling More than a toy: Random matrix models predict how real-world neural representations generalize

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This paper cites E., Arnold, F.

A theoretical framework for overfitting in energy-based modeling E., Arnold, F

Reference 58

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This paper cites and Tanaka, K.

A theoretical framework for overfitting in energy-based modeling and Tanaka, K

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This paper cites Creating artificial human genomes using generative neural networks.

A theoretical framework for overfitting in energy-based modeling Creating artificial human genomes using generative neural networks

Reference 60

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A theoretical framework for overfitting in energy-based modeling L., Szatkownik, A., Furtlehner, C., Charpiat, G., and Jay, F

Reference 61

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