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Found 4 papers, 1 papers with code
Distal Interference: Exploring the Limits of Model-Based Continual Learning
Continual learning is the sequential learning of different tasks by a machine learning model.
Cauchy Loss Function: Robustness Under Gaussian and Cauchy Noise
In supervised machine learning, the choice of loss function implicitly assumes a particular noise distribution over the data.
ATLAS: Universal Function Approximator for Memory Retention
Furthermore, we present ATLAS: a novel ANN architecture based on the new theorem.
KASAM: Spline Additive Models for Function Approximation
SAM is extended with the Kolmogorov-Arnold representation theorem to a novel universal function approximator, called the Kolmogorov-Arnold Spline Additive Model - KASAM.
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