Feb 8, 2021 · We propose a novel approach for multi-objective training of neural networks to approximate the Pareto front during inference.

Apr 28, 2021 · Experiments on three different multi-objective tasks show that the outputs of the set of learners are indeed well-spread on the Pareto front.

Feb 8, 2021 · This work proposes a novel approach for multi-objective training of neural networks to approximate the Pareto front during inference using a dynamic loss ...

We propose a novel approach for multi-objective training of neural networks to approximate the Pareto front during inference.

We propose a novel learning approach to estimate the Pareto front by maximizing the dominated hypervolume (HV) of the average loss vectors corresponding to a ...

This paper presents a novel approach to multiobjective algorithms aimed at mod- eling the Pareto set using neural networks. Whereas previous methods mainly.

This repository contains the source code to train multiple neural networks for simple multi-objective (MO) regression as an illustration of the HV ...

We propose a novel approach for multi-objective training of neural networks to approximate the Pareto front during inference.

Mar 9, 2023 · In our approach, we train the neural networks multi-objectively using a dynamic loss function, wherein each network's losses (corresponding to ...

Multi-Objective Learning to Predict Pareto Fronts Using Hypervolume Maximization, arxiv, 2021, Deist st al. [paper] [code] [PPT]. 10. Self-Evolutionary ...