catastrophic forgetting
4 notes, newest first.
ADORN: Adaptive Drift handling for Open RAN using Reinforcement Learning
The paper proposes a Q-learning based adaptive retraining approach for Open Radio Access Networks (O-RAN) using Reinforcement Learning (RL). The goal is to balance forecasting accuracy and retraining cost, which can…
ADORN: Adaptive Drift handling for Open RAN using Reinforcement Learning
The paper proposes an adaptive retraining method for Open Radio Access Networks (O-RAN) using Reinforcement Learning (RL). The goal is to balance forecasting accuracy and retraining cost in the presence of dynamic…
ADORN: Adaptive Drift handling for Open RAN using Reinforcement Learning
Researchers propose a Q-learning based adaptive retraining approach for Open Radio Access Networks (O-RAN) using Reinforcement Learning (RL) to address the issue of drift that degrades the performance of AI/ML models…
RECALL: Recovery Experience Collection for Active Lifelong Learning in Vision-Language-Action Models
The paper proposes a new approach for training Vision-Language-Action (VLA) models through active, continual learning. This method aims to address the inefficiencies of traditional passive imitation learning by…
