Multi-task Learning Approach for Unified Biometric Estimation from Fetal Ultrasound Anomaly Scans

Authors

Zangir Iklassov, Mohamed Bin Zayed University of Artificial IntelligenceFollow
Ikboljon Sobirov, Mohamed Bin Zayed University of Artificial IntelligenceFollow
Ruben Solozabal, Mohamed Bin Zayed University of Artificial IntelligenceFollow
Martin Takáč, Mohamed Bin Zayed University of Artificial IntelligenceFollow

Document Type

Conference Proceeding

Publication Title

Proceedings of Machine Learning Research

Abstract

This study addresses a gap in the utilization of Reinforcement Learning (RL) and Machine Learning (ML) techniques in solving the Stochastic Vehicle Routing Problem (SVRP) that involves the challenging task of optimizing vehicle routes under uncertain conditions. We propose a novel end-to-end framework that comprehensively addresses the key sources of stochasticity in SVRP and utilizes an RL agent with a simple yet effective architecture and a tailored training method. Through comparative analysis, our proposed model demonstrates superior performance compared to a widely adopted state-of-the-art metaheuristic, achieving a significant 3.43% reduction in travel costs. Furthermore, the model exhibits robustness across diverse SVRP settings, highlighting its adaptability and ability to learn optimal routing strategies in varying environments. The publicly available implementation of our framework serves as a valuable resource for future research endeavors aimed at advancing RL-based solutions for SVRP.

First Page

502

Last Page

517

Publication Date

1-1-2023

Keywords

Planning, Reinforcement Learning, Scheduling Optimization, Stochastic Vehicle Routing Problem

Recommended Citation

Z. Iklassov et al., "Multi-task Learning Approach for Unified Biometric Estimation from Fetal Ultrasound Anomaly Scans," Proceedings of Machine Learning Research, vol. 222, pp. 502 - 517, Jan 2023.