22 citations · 59 across the 10 of their papers we have counts for
4 papers · 1 filter
Harnessing the Power of Explanations for Incremental Training: A LIME-Based Approach
Arnab Neelim Mazumder, Niall Lyons, Ashutosh Pandey +2
Explainability of neural network prediction is essential to understand feature importance and gain interpretable insight into neural network performance. However, explanations of n…
Uncertainty-based Meta-Reinforcement Learning for Robust Radar Tracking
Julius Ott, Lorenzo Servadei, Gianfranco Mauro +3
Nowadays, Deep Learning (DL) methods often overcome the limitations of traditional signal processing approaches. Nevertheless, DL methods are barely applied in real-life applicatio…
Utilizing Explainable AI for improving the Performance of Neural Networks
Huawei Sun, Lorenzo Servadei, Hao Feng +3
Nowadays, deep neural networks are widely used in a variety of fields that have a direct impact on society. Although those models typically show outstanding performance, they have…
MEET: A Monte Carlo Exploration-Exploitation Trade-off for Buffer Sampling
Julius Ott, Lorenzo Servadei, Jose Arjona-Medina +7
Data selection is essential for any data-based optimization technique, such as Reinforcement Learning. State-of-the-art sampling strategies for the experience replay buffer improve…