Evolved multi-channel activation functions that incorporate missingness and confidence scores improve classification performance on datasets with missing data.
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PowLU replaces SwiGLU with a rational-power activation to reduce outlier amplification and numerical instability during large-scale LLM pre-training while matching performance.
RBMs with Gaussian weights rarely induce or easily learn distributions with strong higher-order interactions on visible units, except when the hidden-unit activation function is Exponential.
citing papers explorer
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Evolving Multi-Channel Confidence-Aware Activation Functions for Missing Data with Channel Propagation
Evolved multi-channel activation functions that incorporate missingness and confidence scores improve classification performance on datasets with missing data.
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PowLU: An Activation Function for Stable Pre-Training of LLMs
PowLU replaces SwiGLU with a rational-power activation to reduce outlier amplification and numerical instability during large-scale LLM pre-training while matching performance.
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Activation Functions, Statistics and Learning of Higher-Order Interactions in Restricted Boltzmann Machines
RBMs with Gaussian weights rarely induce or easily learn distributions with strong higher-order interactions on visible units, except when the hidden-unit activation function is Exponential.