AI RESEARCH
Evolving Multi-Channel Confidence-Aware Activation Functions for Missing Data with Channel Propagation
arXiv CS.LG
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ArXi:2602.13864v2 Announce Type: replace-cross Learning in the presence of missing data can result in biased predictions and poor generalizability, among other difficulties, which data imputation methods only partially address. In neural networks, activation functions significantly affect performance yet typical options (e.g., ReLU, Swish) operate only on feature values and do not account for missingness indicators or confidence scores.