A **Variational Autoencoder** is a type of likelihood-based generative model. It consists of an encoder, that takes in data $x$ as input and transforms this into a latent representation $z$,  and a decoder, that takes a latent representation $z$ and returns a reconstruction $\hat{x}$. Inference is performed via variational inference to approximate the posterior of the model.

A **FuseFormer block** is used in the [FuseFormer](https://paperswithcode.com/method/fuseformer) model for video inpainting. It is the same to standard [Transformer](https://paperswithcode.com/method/transformer) block except that feed forward network is replaced with a Fusion Feed Forward Network (F3N). F3N brings no extra parameter into the standard feed forward net and the difference is that F3N inserts a soft-split and a soft composite operation between the two layer of MLPs.

FuseFormer Block

FuseFormer: Fusing Fine-Grained Information in Transformers for Video Inpainting

Auto-Encoding Variational Bayes

**Sarsa_INLINE_MATH_1** extends eligibility-traces to action-value methods. It has the same update rule as for **TD_INLINE_MATH_1** but we use the action-value form of the TD erorr:

$$ \delta\_{t} = R\_{t+1} + \gamma\hat{q}\left(S\_{t+1}, A\_{t+1}, \mathbb{w}\_{t}\right) - \hat{q}\left(S\_{t}, A\_{t}, \mathbb{w}\_{t}\right) $$

and the action-value form of the [eligibility trace](https://paperswithcode.com/method/eligibility-trace):

$$ \mathbb{z}\_{-1} = \mathbb{0} $$

$$ \mathbb{z}\_{t} = \gamma\lambda\mathbb{z}\_{t-1} + \nabla\hat{q}\left(S\_{t}, A\_{t}, \mathbb{w}\_{t} \right), 0 \leq t \leq T$$

Source: Sutton and Barto, Reinforcement Learning, 2nd Edition

Source	Auto-Encoding Variational Bayes
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com