#training-algorithms2 articles

2 articles

AINeutralarXiv – CS AI · 5d ago7/104

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Topological derivative approach for deep neural network architecture adaptation

Researchers developed a novel algorithm using topological derivatives to automatically determine where and how to add new layers to neural networks during training. The approach uses mathematical principles from optimal control theory and topology optimization to adaptively grow network architecture, showing superior performance compared to baseline networks and other adaptation strategies.

AIBullishOpenAI News · Apr 186/105

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Evolved Policy Gradients

Researchers have released Evolved Policy Gradients (EPG), an experimental metalearning approach that evolves the loss function of AI learning agents to enable faster training on new tasks. The method allows agents to generalize beyond their training data, successfully performing basic tasks in novel scenarios they weren't specifically trained for.