Multi task Learning

Multi-task learning with Multi-gate Mixture-of-experts

Towards Data Science

Multi-task learning is a machine learning method in which a model learns to solve multiple tasks simultaneously. The assumption is that by learning to complete multiple correlated tasks with the same…...

A Primer on Multi-task Learning — Part 1

Analytics Vidhya

Multi-task Learning (MTL) is a collection of techniques intended to learn multiple tasks simultaneously instead of learning them separately. The motivation behind MTL is to create a “Generalist”…

Optimizing Multi-task Learning Models in Practice

Towards Data Science

Why Multi-task learning Multi-task learning Multi-task learning (MTL) [1] is a field in machine learning in which we utilize a single model to learn multiple tasks simultaneously. Multi-task learning ...

Multi-task Learning: All You Need to Know(Part-1)

Python in Plain English

Figure: Framework of Multi-task learning Multi-task learning is becoming incredibly popular. This article provides an overview of the current state of multi-task learning. It discusses the extensive m...

Multi-Task Machine Learning: Solving Multiple Problems Simultaneously

Towards Data Science

Single-task learning is the process of learning to predict a single outcome (binary, multi-class, or continuous) from a labeled data set. By contrast, multi-task learning is the process of jointly…

Multi-Task Learning for Classification with Keras

Towards Data Science

Learn how to build a model capable of performing multiple image classifications concurrently with Multiple-Task Learning Photo by Markus Winkler on Unsplash Multi-task learning (MLT) is a subfield of...

Multitask learning in TensorFlow with the Head API

Towards Data Science

A fundamental characteristic of human learning is that we learn many things simultaneously. The equivalent idea in machine learning is called multi-task learning (MTL), and it has become increasingly…...

Multi-Task Learning with torch in R

R-bloggers

Multi-task learning (MTL) is an approach where a single neural network model is trained to perform multiple related tasks simultaneously. This methodology can improve model generalization, reduce over...