Les Houches 2022 Summer school on Statistical Physics & Machine Learning

This bingo card has a free space and 87 words: mean field, NTK, kernel, KRR, spike, deep net, two-layer, Hessian, replica, loss landscape, generalization, Gaussian data, MNIST, generative, COVID, How much time do I/we have?, thermo-dynamic limit, adversarial, width, depth, scaling, eigen-{value, vector, function}, gradient descent, random (and/or) noise, inverse temperature, empirical, numerics, sparsity, {under, over}-parameterized, thermo-dynamic limit, generalization, loss landscape, KRR, COVID, gradient descent, generative, scaling, Hessian, replica, depth, eigen{value, vector, function}, adversarial, MNIST, mean field, numerics, inverse temperature, Gaussian data, random (and/or) noise, spike, empirical, deep net, sparsity, two-layer, width, kernel, {under, over}-parameterized., How much time do I/we have?, NTK, COVID, Hessian, replica, kernel, loss landscape, MNIST, width, mean field, adversarial, two-layer, generalization, spike, deep net, sparsity, generative, gradient descent, numerics, depth, {under, over}-parameterized., How much time do I/we have?, Gaussian data, scaling, eigen{value, vector, function}, thermo-dynamic limit, NTK, random (and/or) noise, empirical, KRR and inverse temperature.

⚠ This card has duplicate items: mean field (3), NTK (3), kernel (3), KRR (3), spike (3), deep net (3), two-layer (3), Hessian (3), replica (3), loss landscape (3), generalization (3), Gaussian data (3), MNIST (3), generative (3), COVID (3), How much time do I/we have? (3), thermo-dynamic limit (3), width (3), depth (3), scaling (3), gradient descent (3), random (and/or) noise (3), inverse temperature (3), empirical (3), numerics (3), sparsity (3), eigen{value, vector, function} (2), adversarial (2), {under, over}-parameterized. (2)

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