Categories: FAANG

Theory, Analysis, and Best Practices for Sigmoid Self-Attention

*Primary Contributors
Attention is a key part of the transformer architecture. It is a sequence-to-sequence mapping that transforms each sequence element into a weighted sum of values. The weights are typically obtained as the softmax of dot products between keys and queries. Recent work has explored alternatives to softmax attention in transformers, such as ReLU and sigmoid activations. In this work, we revisit sigmoid attention and conduct an in-depth theoretical and empirical analysis. Theoretically, we prove that transformers with sigmoid attention are universal function approximators and…

Building a Logistic Regression Classifier in PyTorch

Logistic regression is a type of regression that predicts the probability of an event. It is used for classification problems and has many applications in the fields of machine learning, artificial intelligence, and data mining. The formula of logistic regression is to apply a sigmoid function to the output of…

March 29, 2023

In "AI/ML Research"

Logistic Regression in OpenCV

Logistic regression is a simple but popular machine learning algorithm for binary classification that uses the logistic, or sigmoid, function at its core. It also comes implemented in the OpenCV library. In this tutorial, you will learn how to apply OpenCV’s logistic regression algorithm, starting with a custom two-class dataset…

December 30, 2023

In "AI/ML Research"