**MnasNet** is a type of convolutional neural network optimized for mobile devices that is discovered through mobile [neural architecture search](https://paperswithcode.com/method/neural-architecture-search), which explicitly incorporates model latency into the main objective so that the search can identify a model that achieves a good trade-off between accuracy and latency. The main building block is an [inverted residual block](https://paperswithcode.com/method/inverted-residual-block) (from [MobileNetV2](https://paperswithcode.com/method/mobilenetv2)).

**NICE**, or **Non-Linear Independent Components Estimation** is a framework for modeling complex high-dimensional densities. It is based on the idea that a good representation is one in which the data has a distribution that is easy to model. For this purpose, a non-linear deterministic transformation of the data is learned that maps it to a latent space so as to make the transformed data conform to a factorized distribution, i.e., resulting in independent latent variables.  The transformation is parameterised so that computing the determinant of the Jacobian and inverse Jacobian is trivial, yet it maintains the ability to learn complex non-linear transformations, via a composition of simple building blocks, each based on a deep neural network. The training criterion is simply the exact log-likelihood. The transformation used in NICE is the [affine coupling](https://paperswithcode.com/method/affine-coupling) layer without the scale term, known as additive coupling layer:

$$ y\_{I\_{2}} = x\_{I\_{2}} + m\left(x\_{I\_{1}}\right) $$

$$ x\_{I\_{2}} = y\_{I\_{2}} + m\left(y\_{I\_{1}}\right) $$

NICE

NICE: Non-linear Independent Components Estimation

MnasNet

MnasNet: Platform-Aware Neural Architecture Search for Mobile

**Factorized Random Synthesized Attention**, introduced with the [Synthesizer](https://paperswithcode.com/method/synthesizer) architecture, is similar to [factorized dense synthesized attention](https://paperswithcode.com/method/factorized-dense-synthesized-attention) but for random synthesizers. Letting $R$ being a randomly initialized matrix, we factorize $R$ into low rank matrices $R\_{1}, R\_{2} \in \mathbb{R}^{l\text{ x}k}$ in the attention function:

$$ Y = \text{Softmax}\left(R\_{1}R\_{2}^{T}\right)G\left(X\right) . $$

Here $G\left(.\right)$ is a parameterized function that is equivalent to $V$ in [Scaled Dot-Product Attention](https://paperswithcode.com/method/scaled).

For each head, the factorization reduces the parameter costs from $l^{2}$ to $2\left(lk\right)$ where
$k << l$ and hence helps prevent overfitting. In practice, we use a small value of $k = 8$.

The basic idea of a  Random Synthesizer is to not rely on pairwise token interactions or any information from individual token but rather to learn a task-specific alignment that works well globally across many samples.

Source	MnasNet: Platform-Aware Neural Architecture Search for Mobile
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com