TheoryofDeepLearning.2022

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810.2 Autoencoder defined using a density distribution p(h, x), where his the latent feature vector corresponding to visible vector x. The processof computing h given x is called “encoding” and the reverse iscalled “decoding.” In general applying the encoder on x followedby the decoder would not give x again, since the composed transformationis a sample from a distribution. 10213.1 A chasing sequence 115
List of Tables13.1 We ignore the O for simplicity. The l ∞ /l 2 is the strongest possibleguarantee, with l 2 /l 2 coming second, l 2 /l 1 third and exactly k-sparsebeing the weaker. We also note that all [? ? ? ? ] obtain improved analysesof the Restricted Isometry property; the algorithm is suggestedand analyzed (modulo the RIP property) in [? ]. The work in [? ] doesnot explicitly state the extension to the d-dimensional case, but caneasily be inferred from the arguments. [? ? ? ? ] work when the universesize in each dimension are powers of 2. 116
Page 1: C O N T R I B U T O R S : R A M A N
Page 4 and 5: 44 Basics of generalization theory
Page 6 and 7: 612 Representation Learning 11113 E
Page 11: IntroductionThis monograph discusse
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Page 17 and 18: 2Basics of OptimizationThis chapter
Page 19 and 20: basics of optimization 19where the
Page 21 and 22: basics of optimization 21Therefore,
Page 23 and 24: 3Backpropagation and its VariantsTh
Page 25 and 26: backpropagation and its variants 25
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Page 41 and 42: 6Algorithmic RegularizationLarge sc
Page 43 and 44: algorithmic regularization 43minimi
Page 45 and 46: algorithmic regularization 45update
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Page 49 and 50: algorithmic regularization 496.3.2
Page 51 and 52: algorithmic regularization 51Given
Page 53 and 54: algorithmic regularization 53to the
Page 55: algorithmic regularization 55Since
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58 theory of deep learning7.1 Preli
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60 theory of deep learningcreasing
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62 theory of deep learning“gradie
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64 theory of deep learning7.4 Case
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66 theory of deep learningWhen x =
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76 theory of deep learningFigure 8.
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78 theory of deep learningNote the
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9Inductive Biases due to Algorithmi
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inductive biases due to algorithmic
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10Unsupervised learning: OverviewMu
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11Generative Adversarial NetsChapte
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12Representation Learning
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116 theory of deep learning13.3 Exa
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118 theory of deep learning13.5 Exa
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