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https://arxiv.org/pdf/1806.07366https://github.com/rtqichen/torchdiffeq(Dec 2019 NeurIPS 2018)AbstractWe introduce a new family of deep neural network models. Instead of specifying a discrete sequence of hidden layers, we parameterize the derivative of the hidden state using a neural network. The output of the network is computed using a blackbox differential equation solver. These continuous-de..

https://arxiv.org/pdf/2401.06281나중에 참고하려고 정리 여러 번 line by line으로 도출해봄..2.1. Forward Process: Gaussian Diffusion2.1.1. Linear Gaussian Transitions: q(zt | zs) 2.1.2. Top-down Posterior: q(zs | zt, x)2.1.3. Learning the Noise Schedule2.2. Reverse Process: Discrete-Time Generative Model2.2.1. Generative Transitions: p(zs | zt) Two other notable parameterizations not elaborated upon in this article ..

https://arxiv.org/pdf/2107.00630https://github.com/google-research/vdm(NeurIPS 2021) Jul 2021 ★ ★ ★ ★ 이걸 먼저 봐야 함!!!!! ★ ★ ★ ★ ↓ ↓ ↓ ↓ ↓ ↓ ↓Demystifying Variational Diffusion Models https://arxiv.org/pdf/2401.06281 ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ 「 Variational Diffusion Models 」 이 논문에서의 수식이 Classifier-free guidance, distillation, Imagen 등등..에서 계속 이어지는데,(score-based model 계열 논문에서도 수식, 개념이..