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Distal Interference: Exploring the Limits of Model-Based Continual Learning
Heinrich van Deventer, Anna Sergeevna Bosman
Continual learning is the sequential learning of different tasks by a machine learning model. Continual learning is known to be hindered by catastrophic interference or forgetting,…
Cauchy Loss Function: Robustness Under Gaussian and Cauchy Noise
Thamsanqa Mlotshwa, Heinrich van Deventer, Anna Sergeevna Bosman
In supervised machine learning, the choice of loss function implicitly assumes a particular noise distribution over the data. For example, the frequently used mean squared error (M…
ATLAS: Universal Function Approximator for Memory Retention
Heinrich van Deventer, Anna Bosman
Artificial neural networks (ANNs), despite their universal function approximation capability and practical success, are subject to catastrophic forgetting. Catastrophic forgetting…
KASAM: Spline Additive Models for Function Approximation
Heinrich van Deventer, Pieter Janse van Rensburg, Anna Bosman
Neural networks have been criticised for their inability to perform continual learning due to catastrophic forgetting and rapid unlearning of a past concept when a new concept is i…