Emergent Learning Paradigms

Emergent Learning Paradigms

The dynaxx phase space: emergent learning paradigms beyond backpropagation

Backpropagation has been the workhorse of deep learning for over three decades, but its dominance is showing cracks. The credit assignment problem dee...

Emergent Learning Paradigms

The Dynaxx Shift: From Loss Landscapes to Learning Phase Transitions

If you have ever watched a validation loss curve flatten for twenty thousand steps, tweaked a learning rate, and seen it drop again, you have already ...

Emergent Learning Paradigms

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

Emergent learning systems — environments where agents, rules, and feedback loops produce adaptive behavior without centralized control — promise a lot...

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

Self-organizing criticality (SOC) is a phenomenon observed in complex systems—from sandpiles to neural avalanches—where systems naturally evolve to a ...