random(and/or)noisedeepnetlosslandscapekernelspikeHowmuch timedo I/wehave?Hessianwidththermo-dynamiclimitmeanfieldKRR{under, over}-parameterizedwidththermo-dynamiclimitgeneralizationrandom(and/or)noiseNTKgradientdescentgradientdescentHowmuch timedo I/wehave?COVIDempiricaltwo-layersparsityeigen{value,vector,function}two-layeradversariallosslandscaperandom(and/or)noiseMNISTgeneralizationreplicaNTKnumericsscalinggenerativedeepnetKRRscalingwidthspikeadversariallosslandscapekernelsparsityNTKKRRnumericsreplicagradientdescentHessiankernelempiricalCOVIDHowmuch timedo I/wehave?eigen-{value,vector,function}numericsdepthGaussiandatainversetemperaturereplicaMNISTthermo-dynamiclimitmeanfieldempiricalgenerativegeneralizationgenerative{under, over}-parameterized.eigen{value,vector,function}two-layerdepthsparsityHessianGaussiandataCOVIDinversetemperature{under, over}-parameterized.deepnetadversarialMNISTinversetemperaturedepthscalingspikemeanfieldGaussiandatarandom(and/or)noisedeepnetlosslandscapekernelspikeHowmuch timedo I/wehave?Hessianwidththermo-dynamiclimitmeanfieldKRR{under, over}-parameterizedwidththermo-dynamiclimitgeneralizationrandom(and/or)noiseNTKgradientdescentgradientdescentHowmuch timedo I/wehave?COVIDempiricaltwo-layersparsityeigen{value,vector,function}two-layeradversariallosslandscaperandom(and/or)noiseMNISTgeneralizationreplicaNTKnumericsscalinggenerativedeepnetKRRscalingwidthspikeadversariallosslandscapekernelsparsityNTKKRRnumericsreplicagradientdescentHessiankernelempiricalCOVIDHowmuch timedo I/wehave?eigen-{value,vector,function}numericsdepthGaussiandatainversetemperaturereplicaMNISTthermo-dynamiclimitmeanfieldempiricalgenerativegeneralizationgenerative{under, over}-parameterized.eigen{value,vector,function}two-layerdepthsparsityHessianGaussiandataCOVIDinversetemperature{under, over}-parameterized.deepnetadversarialMNISTinversetemperaturedepthscalingspikemeanfieldGaussiandata

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