Evolved multi-channel activation functions that incorporate missingness and confidence scores improve classification performance on datasets with missing data.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
RBMs using exponential activation functions can represent and learn data structures with strong higher-order interactions better than linear, step or ReLU activations, but only inside an analytically determined parameter window.
citing papers explorer
-
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.
-
Activation Functions, Statistics and Learning of Higher-Order Interactions in Restricted Boltzmann Machines
RBMs using exponential activation functions can represent and learn data structures with strong higher-order interactions better than linear, step or ReLU activations, but only inside an analytically determined parameter window.