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Bayesian Deep Learning
최성준
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Bayesian Deep Learning
Bayesian Deep Learning
http://www.boostcourse.org/bayesiandeeplearning/lecture/24812/
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CHAPTER 1. Elementary of mathematics
Introduction
Set theory
Measure theory
Probability
Random variable
Random process
Functional analysis
CHAPTER 2. Gaussian process
Introduction
Gaussian process
Weight space view
Function space view
Gaussian process latent variable model (GPLVM)
Gaussian process Application : 최성준님 연구 소개
CHAPTER 3. Bayesian deep neural network (1)
Introduction
Minimizing the Description Length
Ensemble Learning in Bayesian Neural Network
Practical variational inference
Bayes by backprop (BBB)
CHAPTER 4. Bayesian deep neural network (2)
Summary of Variational Inference
Dropout as a Bayesian Approximation
Stein Variational Gradient Descent
CHAPTER 5. Summary
Summary
CHAPTER 6. Uncertainties in Deep Learning
Introduction
Uncertainty in Deep Learning
Representing Inferential Uncertainty through Sampling
Bayesian Uncertainty Estimation
Predictive Uncertainty Estimation using Deep Ensembles
Uncertainties in Bayesian Deep Learning for Computer Vision
Uncertainty -Aware Reinforcement Learning
Safe Visual Navigation via Deep Learning
Uncertainty-Aware Learning using Mixture Density Networks
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Discussion Forum
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#conjugate
#conjugateprior
#gaussianprocess
#gpr
#likelihood
#linearregression
#posterior
#prior
#weightspaceview
#최성준
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Weight space view - 김민정
Weight space view - 김민정
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Gaussian process
Function space view
최성준
