Adding temporal memory via LIF, precision-weighted gating, and anticipatory prediction to MoE routers recovers effective expert selection at distribution transitions, with ablation confirming a super-additive beta-ant interaction.
Predictive coding: Towards a future of deep learning beyond backpropagation?
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Predictive coding is recast as deep hierarchical Gaussian filters to restore precision-weighted message passing, yielding closed-form inference and online precision learning that matches backpropagation speed on FashionMNIST while outperforming on online and concept-drift tasks.
Coupled entropy maximized by coupled stretched exponentials is the unique universal entropy meeting scale-specific uncertainty measurement and Hanel-Thurner extensivity requirements.
The book presents principles from optimization and information theory to explain deep network architectures and enable new interpretable models.
citing papers explorer
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Affinity Is Not Enough: Recovering the Free Energy Principle in Mixture-of-Experts
Adding temporal memory via LIF, precision-weighted gating, and anticipatory prediction to MoE routers recovers effective expert selection at distribution transitions, with ablation confirming a super-additive beta-ant interaction.
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Closed-form predictive coding via hierarchical Gaussian filters
Predictive coding is recast as deep hierarchical Gaussian filters to restore precision-weighted message passing, yielding closed-form inference and online precision learning that matches backpropagation speed on FashionMNIST while outperforming on online and concept-drift tasks.
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The unique, universal entropy for complex systems
Coupled entropy maximized by coupled stretched exponentials is the unique universal entropy meeting scale-specific uncertainty measurement and Hanel-Thurner extensivity requirements.
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Principles and Practice of Deep Representation Learning: or a Mathematical Theory of Memory
The book presents principles from optimization and information theory to explain deep network architectures and enable new interpretable models.