Perceiver IO is a general neural network architecture that performs well for structured input modalities and output tasks. Perceiver IO is built to easily integrate and transform arbitrary information for arbitrary tasks.

The **Quasi-Hyperbolic Momentum Algorithm (QHM)** is a simple alteration of [momentum SGD](https://paperswithcode.com/method/sgd-with-momentum), averaging a plain [SGD](https://paperswithcode.com/method/sgd) step with a momentum step. **QHAdam** is a QH augmented version of [Adam](https://paperswithcode.com/method/adam), where we replace both of Adam's moment estimators with quasi-hyperbolic terms. QHAdam decouples the momentum term from the current gradient when updating the weights, and decouples the mean squared gradients term from the current squared gradient when updating the weights. 

In essence, it is a weighted average of the momentum and plain SGD, weighting the current gradient with an immediate discount factor $v\_{1}$ divided by a weighted average of the mean squared gradients and the current squared gradient, weighting the current squared gradient with an immediate discount factor $v\_{2}$. 

$$ \theta\_{t+1, i} = \theta\_{t, i} - \eta\left[\frac{\left(1-v\_{1}\right)\cdot{g\_{t}} + v\_{1}\cdot\hat{m}\_{t}}{\sqrt{\left(1-v\_{2}\right)g^{2}\_{t} + v\_{2}\cdot{\hat{v}\_{t}}} + \epsilon}\right], \forall{t} $$

It is recommended to set $v\_{2} = 1$ and $\beta\_{2}$ same as in Adam.

QHAdam

Quasi-hyperbolic momentum and Adam for deep learning

Perceiver IO

Perceiver IO: A General Architecture for Structured Inputs & Outputs

Bilateral grid is a new data structure that enables fast edge-aware image processing. It enables edge-aware image manipulations such as local tone mapping on high resolution images in real time.

Source: [Chen et al.](https://people.csail.mit.edu/sparis/publi/2007/siggraph/Chen_07_Bilateral_Grid.pdf)

Image source: [Chen et al.](https://people.csail.mit.edu/sparis/publi/2007/siggraph/Chen_07_Bilateral_Grid.pdf)

Source	Perceiver IO: A General Architecture for Structured Inputs & Outputs
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com