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How to interpret the latent variables ?
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Classroom Contents
Neural Network Renormalization Group
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- 1 Intro
- 2 RG and Deep Learning
- 3 Motivation
- 4 Multi-Scale Entanglement Renormalization Ansatz
- 5 MERA as a quantum circuit
- 6 Neural Network Renormalization Group
- 7 Probability transformation in picture
- 8 Toy problem: Harmonic oscillator
- 9 Neural Bijectors
- 10 Bijectors form a group
- 11 Training: Probability Density Distillation
- 12 Interlude: The WaveNet Story
- 13 Variational Loss
- 14 What is the network learning?
- 15 How to interpret the latent variables ?
- 16 How is this useful?
- 17 Wander in the latent space
- 18 Latent space Hybrid MC
- 19 MI and holographic RG
- 20 Timeline on Generative Models
- 21 DL as a fluid control problem
