Emergent Learning Paradigms

Emergent Learning Paradigms

The dynaxx phase space: emergent learning paradigms beyond backpropagation

Backpropagation has been the bedrock of deep learning for decades, but its limitations—credit assignment in deep networks, biological implausibility, ...

Emergent Learning Paradigms

The Dynaxx Shift: From Loss Landscapes to Learning Phase Transitions

This guide explores a paradigm shift in understanding neural network training: moving beyond static loss landscapes to dynamic learning phase transiti...

Emergent Learning Paradigms

The dynaxx method: engineering emergent learning systems for real-world complexity

This guide explores the dynaxx method, a structured approach for designing emergent learning systems that adapt to real-world complexity. Unlike tradi...

Emergent Learning Paradigms

Dynaxx on the Data-Physics Interface: When Learning Algorithms Discover Conservation Laws

Introduction: The Paradigm Shift from Fitting to DiscoveringFor years, my work in computational science was dominated by a simple paradigm: we had a p...

Emergent Learning Paradigms

The Dynaxx Dynamo: Harnessing Self-Organizing Criticality in Meta-Learning Loops

Introduction: The Perilous Plateau of Static Meta-LearningIn my years of consulting with organizations from algorithmic trading firms to autonomous ro...