Continual Learning in the Presence of Repetition

no code implementations • 7 May 2024 • Hamed Hemati, Lorenzo Pellegrini, Xiaotian Duan, Zixuan Zhao, Fangfang Xia, Marc Masana, Benedikt Tscheschner, Eduardo Veas, Yuxiang Zheng, Shiji Zhao, Shao-Yuan Li, Sheng-Jun Huang, Vincenzo Lomonaco, Gido M. van de Ven

Continual learning (CL) provides a framework for training models in ever-evolving environments.

Class Incremental Learning Incremental Learning

XAI Methods for Neural Time Series Classification: A Brief Review

no code implementations • 18 Aug 2021 • Ilija Šimić, Vedran Sabol, Eduardo Veas

Deep learning models have recently demonstrated remarkable results in a variety of tasks, which is why they are being increasingly applied in high-stake domains, such as industry, medicine, and finance.

Explainable Artificial Intelligence (XAI) Time Series +2

Acting upon Imagination: when to trust imagined trajectories in model based reinforcement learning

no code implementations • 12 May 2021 • Adrian Remonda, Eduardo Veas, Granit Luzhnica

Additionally, we introduce methods that exploit the forward propagation of the dynamics model to evaluate if the remainder of the plan aligns with expected results and assess the remainder of the plan in terms of the expected reward.

Model-based Reinforcement Learning Model Predictive Control +2

Formula RL: Deep Reinforcement Learning for Autonomous Racing using Telemetry Data

no code implementations • 22 Apr 2021 • Adrian Remonda, Sarah Krebs, Eduardo Veas, Granit Luzhnica, Roman Kern

This paper explores the use of reinforcement learning (RL) models for autonomous racing.

reinforcement-learning Reinforcement Learning (RL)