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cs.LG2025
Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approach
Adithya Mohan, Dominik RöÃle, Daniel Cremers +1
Recent advancements in Deep Reinforcement Learning (DRL) have demonstrated its applicability across various domains, including robotics, healthcare, energy optimization, and autono…
cs.LG2025
Machine Learning Models for Soil Parameter Prediction Based on Satellite, Weather, Clay and Yield Data
Calvin Kammerlander, Viola Kolb, Marinus Luegmair +6
Efficient nutrient management and precise fertilization are essential for advancing modern agriculture, particularly in regions striving to optimize crop yields sustainably. The Ag…
cs.LG2024
Gaussian-Based and Outside-the-Box Runtime Monitoring Join Forces
Vahid Hashemi, Jan KÅetÃnský, Sabine Rieder +2
Since neural networks can make wrong predictions even with high confidence, monitoring their behavior at runtime is important, especially in safety-critical domains like autonomous…