In multi-task learning, multiple tasks are solved jointly, sharing inductive bias between them.
Multi-task learning is inherently a multi-objective problem because different tasks may …

Many computer vision applications require solving multiple tasks in real-time. A neural
network can be trained to solve multiple tasks simultaneously using multi-task learning. This …

Microscopy is a rapid diagnosis method for many infectious diseases like tuberculosis (TB).
In TB bacilli identification, specimens are stained using Ziehl–Neelsen or Auramine dye and …

In this paper, we present a novel meta-feature generation method in the context of meta-
learning, which is based on rules that compare the performance of individual base learners …

Multi-task learning technique is widely utilized in machine learning modeling where
commonalities and differences across multiple tasks are exploited. However, multiple …

Multi-objective particle swarm optimization algorithms (MOPS) are used successfully to
solve real-life optimization problems. The multi-objective algorithms based on particle …

This chapter describes various approaches for the hyperparameter optimization (HPO) and
combined algorithm selection and hyperparameter optimization problems (CASH). It starts …

Multinomial logistic loss and L 2 regularization are often conflicting objectives as more
robust regularization leads to restrained multinomial parameters. For many practical …

Abstract Machine learning users need methods that can help them identify algorithms or
even workflows (combination of algorithms with preprocessing tasks, using or not …

Abstract Machine Learning algorithms have a broad applicability, although generally a huge
effort is necessary to find a good configuration for a given task. The tuning of free …