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cs.LG2024★ 1 cited
A Robust Quantile Huber Loss With Interpretable Parameter Adjustment In Distributional Reinforcement Learning
Parvin Malekzadeh, Konstantinos N. Plataniotis, Zissis Poulos +1
Distributional Reinforcement Learning (RL) estimates return distribution mainly by learning quantile values via minimizing the quantile Huber loss function, entailing a threshold p…
cs.LG2019
Training CNNs faster with Dynamic Input and Kernel Downsampling
Zissis Poulos, Ali Nouri, Andreas Moshovos
We reduce training time in convolutional networks (CNNs) with a method that, for some of the mini-batches: a) scales down the resolution of input images via downsampling, and b) re…