deepnetlosslandscapekernelspikerandom(and/or)noisekernelthermo-dynamiclimitinversetemperatureCOVID{under, over}-parameterizedinversetemperaturemeanfieldkernelmeanfieldrandom(and/or)noisedepthgeneralizationNTKHessianmeanfieldempiricalKRRnumericsreplicasparsitygenerativescalingdeepnetspikereplicaspikeempiricaltwo-layerGaussiandatadepthHowmuch timedo I/wehave?MNISTadversariallosslandscape{under, over}-parameterized.{under, over}-parameterized.Howmuch timedo I/wehave?generativewidthwidthgradientdescenteigen{value,vector,function}gradientdescenteigen-{value,vector,function}generalizationlosslandscapenumericsgenerativeKRRMNISTCOVIDempiricalwidthCOVIDdeepnettwo-layerscalingthermo-dynamiclimitinversetemperatureKRRMNISTNTKgradientdescentGaussiandataadversarialtwo-layersparsitythermo-dynamiclimitHowmuch timedo I/wehave?sparsityrandom(and/or)noisenumericsscalingGaussiandatageneralizationHessianeigen{value,vector,function}adversarialreplicaHessianNTKdepthdeepnetlosslandscapekernelspikerandom(and/or)noisekernelthermo-dynamiclimitinversetemperatureCOVID{under, over}-parameterizedinversetemperaturemeanfieldkernelmeanfieldrandom(and/or)noisedepthgeneralizationNTKHessianmeanfieldempiricalKRRnumericsreplicasparsitygenerativescalingdeepnetspikereplicaspikeempiricaltwo-layerGaussiandatadepthHowmuch timedo I/wehave?MNISTadversariallosslandscape{under, over}-parameterized.{under, over}-parameterized.Howmuch timedo I/wehave?generativewidthwidthgradientdescenteigen{value,vector,function}gradientdescenteigen-{value,vector,function}generalizationlosslandscapenumericsgenerativeKRRMNISTCOVIDempiricalwidthCOVIDdeepnettwo-layerscalingthermo-dynamiclimitinversetemperatureKRRMNISTNTKgradientdescentGaussiandataadversarialtwo-layersparsitythermo-dynamiclimitHowmuch timedo I/wehave?sparsityrandom(and/or)noisenumericsscalingGaussiandatageneralizationHessianeigen{value,vector,function}adversarialreplicaHessianNTKdepth

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