random(and/or)noiseempiricalNTKrandom(and/or)noisewidthdeepnetHowmuch timedo I/wehave?scalingthermo-dynamiclimitgeneralizationdepthinversetemperatureeigen-{value,vector,function}depthKRRlosslandscapeadversarialMNISTGaussiandataempiricalinversetemperaturegradientdescentCOVIDGaussiandatanumericsHowmuch timedo I/wehave?sparsitygeneralizationgeneralizationgenerativenumericsthermo-dynamiclimitrandom(and/or)noiselosslandscape{under, over}-parameterized.meanfieldNTKmeanfieldmeanfieldtwo-layertwo-layer{under, over}-parameterized.generativenumericsHessianreplicaspikelosslandscapespikeKRRreplicadepthHowmuch timedo I/wehave?replicaeigen{value,vector,function}eigen{value,vector,function}NTKtwo-layerthermo-dynamiclimitdeepnetspikekernelgradientdescentsparsityMNISTgenerativeMNISTscalingsparsityCOVID{under, over}-parameterizedGaussiandatawidthadversarialKRRHessiangradientdescentkernelwidthCOVIDdeepnetadversarialkernelscalingempiricalinversetemperatureHessianrandom(and/or)noiseempiricalNTKrandom(and/or)noisewidthdeepnetHowmuch timedo I/wehave?scalingthermo-dynamiclimitgeneralizationdepthinversetemperatureeigen-{value,vector,function}depthKRRlosslandscapeadversarialMNISTGaussiandataempiricalinversetemperaturegradientdescentCOVIDGaussiandatanumericsHowmuch timedo I/wehave?sparsitygeneralizationgeneralizationgenerativenumericsthermo-dynamiclimitrandom(and/or)noiselosslandscape{under, over}-parameterized.meanfieldNTKmeanfieldmeanfieldtwo-layertwo-layer{under, over}-parameterized.generativenumericsHessianreplicaspikelosslandscapespikeKRRreplicadepthHowmuch timedo I/wehave?replicaeigen{value,vector,function}eigen{value,vector,function}NTKtwo-layerthermo-dynamiclimitdeepnetspikekernelgradientdescentsparsityMNISTgenerativeMNISTscalingsparsityCOVID{under, over}-parameterizedGaussiandatawidthadversarialKRRHessiangradientdescentkernelwidthCOVIDdeepnetadversarialkernelscalingempiricalinversetemperatureHessian

Les Houches 2022 Summer school on Statistical Physics & Machine Learning - Call List

(Print) Use this randomly generated list as your call list when playing the game. There is no need to say the BINGO column name. Place some kind of mark (like an X, a checkmark, a dot, tally mark, etc) on each cell as you announce it, to keep track. You can also cut out each item, place them in a bag and pull words from the bag.


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  1. random (and/or) noise
  2. empirical
  3. NTK
  4. random (and/or) noise
  5. width
  6. deep net
  7. How much time do I/we have?
  8. scaling
  9. thermo-dynamic limit
  10. generalization
  11. depth
  12. inverse temperature
  13. eigen-{value, vector, function}
  14. depth
  15. KRR
  16. loss landscape
  17. adversarial
  18. MNIST
  19. Gaussian data
  20. empirical
  21. inverse temperature
  22. gradient descent
  23. COVID
  24. Gaussian data
  25. numerics
  26. How much time do I/we have?
  27. sparsity
  28. generalization
  29. generalization
  30. generative
  31. numerics
  32. thermo-dynamic limit
  33. random (and/or) noise
  34. loss landscape
  35. {under, over}-parameterized.
  36. mean field
  37. NTK
  38. mean field
  39. mean field
  40. two-layer
  41. two-layer
  42. {under, over}-parameterized.
  43. generative
  44. numerics
  45. Hessian
  46. replica
  47. spike
  48. loss landscape
  49. spike
  50. KRR
  51. replica
  52. depth
  53. How much time do I/we have?
  54. replica
  55. eigen{value, vector, function}
  56. eigen{value, vector, function}
  57. NTK
  58. two-layer
  59. thermo-dynamic limit
  60. deep net
  61. spike
  62. kernel
  63. gradient descent
  64. sparsity
  65. MNIST
  66. generative
  67. MNIST
  68. scaling
  69. sparsity
  70. COVID
  71. {under, over}-parameterized
  72. Gaussian data
  73. width
  74. adversarial
  75. KRR
  76. Hessian
  77. gradient descent
  78. kernel
  79. width
  80. COVID
  81. deep net
  82. adversarial
  83. kernel
  84. scaling
  85. empirical
  86. inverse temperature
  87. Hessian