The **Exponential Linear Squashing Activation Function**, or **ELiSH**, is an activation function used for neural networks. It shares common properties with [Swish](https://paperswithcode.com/method/swish), being made up of an [ELU](https://paperswithcode.com/method/elu) and a [Sigmoid](https://paperswithcode.com/method/sigmoid-activation):

$$f\left(x\right) = \frac{x}{1+e^{-x}} \text{ if } x \geq 0 $$
$$f\left(x\right) = \frac{e^{x} - 1}{1+e^{-x}} \text{ if } x < 0 $$

The Sigmoid part of **ELiSH** improves information flow, while the linear parts solve issues of vanishing gradients.

**SortCut Sinkhorn Attention** is a variant of [Sparse Sinkhorn Attention](https://paperswithcode.com/method/sparse-sinkhorn-attention) where a post-sorting truncation of the input sequence is performed, essentially performing a hard top-k operation on the input sequence blocks within the computational graph. While most attention models mainly re-weight or assign near-zero weights during training, this allows for explicitly and dynamically truncate the input sequence. Specifically:

$$ Y = \text{Softmax}\left(Q{\psi\_{S}}\left(K\right)^{T}\_{\left[:n\right]}\right)\psi\_{S}\left(V\right)\_{\left[:n\right]} $$

where $n$ is the Sortfut budget hyperparameter.

SortCut Sinkhorn Attention

Sparse Sinkhorn Attention

ELiSH

The Quest for the Golden Activation Function

**SAINT** is a hybrid deep learning approach to solving tabular data problems. SAINT performs attention over both rows and columns, and it includes an enhanced embedding method. The architecture, pre-training and training pipeline are as follows: 

- $L$ layers with 2 attention blocks each, one self-attention block, and a novel intersample attention blocks that computes attention across samples are used.
- For pre-training, this involves minimizing the contrastive and denoising losses between a given data point and its views generated by [CutMix](https://paperswithcode.com/method/cutmix) and [mixup](https://paperswithcode.com/method/mixup). During finetuning/regular training, data passes through an embedding layer and then the SAINT model. Lastly, the contextual embeddings from SAINT are used to pass only the embedding corresponding to the CLS token through an [MLP](https://paperswithcode.com/method/feedforward-network) to obtain the final prediction.

Source	The Quest for the Golden Activation Function
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com