An Introduction to Transformers

04/20/2023
by   Richard E. Turner, et al.
0

The transformer is a neural network component that can be used to learn useful representations of sequences or sets of datapoints. The transformer has driven recent advances in natural language processing, computer vision, and spatio-temporal modelling. There are many introductions to transformers, but most do not contain precise mathematical descriptions of the architecture and the intuitions behind the design choices are often also missing. Moreover, as research takes a winding path, the explanations for the components of the transformer can be idiosyncratic. In this note we aim for a mathematically precise, intuitive, and clean description of the transformer architecture.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
09/14/2020

Efficient Transformers: A Survey

Transformer model architectures have garnered immense interest lately du...
research
07/19/2022

Formal Algorithms for Transformers

This document aims to be a self-contained, mathematically precise overvi...
research
03/22/2023

TRON: Transformer Neural Network Acceleration with Non-Coherent Silicon Photonics

Transformer neural networks are rapidly being integrated into state-of-t...
research
10/16/2019

Injecting Hierarchy with U-Net Transformers

The Transformer architecture has become increasingly popular over the pa...
research
08/31/2022

Efficient Sparsely Activated Transformers

Transformer-based neural networks have achieved state-of-the-art task pe...
research
02/25/2021

How to represent part-whole hierarchies in a neural network

This paper does not describe a working system. Instead, it presents a si...
research
05/05/2023

Neuromodulation Gated Transformer

We introduce a novel architecture, the Neuromodulation Gated Transformer...

Please sign up or login with your details

Forgot password? Click here to reset