spikescalingempiricalsparsityHowmuch timedo I/wehave?thermo-dynamiclimitspikewidthdeepnetCOVIDdepthadversarialtwo-layerscalingreplicaNTKMNISTkernelHessiandepth{under, over}-parameterized.kernelGaussiandatagradientdescentMNISTdeepnetNTKkerneltwo-layermeanfieldKRRempiricaltwo-layerthermo-dynamiclimitKRRlosslandscapeCOVIDwidthwidthHowmuch timedo I/wehave?deepnetGaussiandatasparsityinversetemperaturegeneralizationnumericslosslandscapemeanfieldgeneralizationlosslandscapeMNISTKRRinversetemperature{under, over}-parameterizedsparsitynumericseigen{value,vector,function}replicagenerativerandom(and/or)noise{under, over}-parameterized.eigen{value,vector,function}replicaNTKHowmuch timedo I/wehave?random(and/or)noisemeanfieldspikeeigen-{value,vector,function}Gaussiandatarandom(and/or)noisegradientdescentnumericsinversetemperatureadversarialadversarialthermo-dynamiclimitHessiandepthgenerativegenerativeCOVIDgeneralizationHessianempiricalgradientdescentscalingspikescalingempiricalsparsityHowmuch timedo I/wehave?thermo-dynamiclimitspikewidthdeepnetCOVIDdepthadversarialtwo-layerscalingreplicaNTKMNISTkernelHessiandepth{under, over}-parameterized.kernelGaussiandatagradientdescentMNISTdeepnetNTKkerneltwo-layermeanfieldKRRempiricaltwo-layerthermo-dynamiclimitKRRlosslandscapeCOVIDwidthwidthHowmuch timedo I/wehave?deepnetGaussiandatasparsityinversetemperaturegeneralizationnumericslosslandscapemeanfieldgeneralizationlosslandscapeMNISTKRRinversetemperature{under, over}-parameterizedsparsitynumericseigen{value,vector,function}replicagenerativerandom(and/or)noise{under, over}-parameterized.eigen{value,vector,function}replicaNTKHowmuch timedo I/wehave?random(and/or)noisemeanfieldspikeeigen-{value,vector,function}Gaussiandatarandom(and/or)noisegradientdescentnumericsinversetemperatureadversarialadversarialthermo-dynamiclimitHessiandepthgenerativegenerativeCOVIDgeneralizationHessianempiricalgradientdescentscaling

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