Feature Regression and Wide Neural Networks
18 videos • 3,887 views • by Dr Mihai Nica
Goes over the notes found at the link below. Each video covers one subsection of the notes. https://sites.uoguelph.ca/nicam/files...
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0 Intro
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2
1 1 Definition of Feature Regression and Main results
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3
1 2 1 Random Features Regression Model
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1 2 2 Train only the last layer neural network
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5
1 2 3 Double DescentCurve
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6
1 3 1 The Kernel Trick
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7
1 3 2 Simple Extensions weights+biases and multi dimensional output
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8
1 4 1 Gradient Descent Dyanmics Proof of Thm 6 part1
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9
1 4 2 Solution to the Dynamics Proof of Theorem 6 Part 2
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10
1 4 3 Distribution at initialization Proof of Prop 8
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11
1 4 4 Distribution after training Proof of Theorem 9 Part 1
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12
1 4 5 Conditioning Gaussian Vectors Proof of Theorem 9 Part 2
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13
2 1 Main idea Linear Approximation of Neural Nets
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14
2 2 1 Definition of a Neural Network
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2 2 2 Neural Network Gaussian Processes and their Kernel
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16
2 2 3 Neural Tangent Kernel
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17
2 3 1 Proof of Neural Network Gaussian Process Kernel
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18
2 3 2 Proof of the Neural Tangent Kernel
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