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cs.LG2026
Same Brain, Different Prediction: How Preprocessing Choices Undermine EEG Decoding Reliability
Dengzhe Hou, Zihao Wu, Lingyu Jiang +3
Electroencephalography (EEG) is a cornerstone of brain-computer interfaces and clinical neuroscience, yet deep learning models are typically trained and evaluated under a single, u…
cs.LG2026
The Rank and Gradient Lost in Non-stationarity: Sample Weight Decay for Mitigating Plasticity Loss in Reinforcement Learning
Zihao Wu, Hongyao Tang, Yi Ma +3
Deep reinforcement learning (RL) suffers from plasticity loss severely due to the nature of non-stationarity, which impairs the ability to adapt to new data and learn continually.…
cs.LG2024
A Method on Searching Better Activation Functions
Haoyuan Sun, Zihao Wu, Bo Xia +5
The success of artificial neural networks (ANNs) hinges greatly on the judicious selection of an activation function, introducing non-linearity into network and enabling them to mo…