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Dynamic Recursive Entropy Model (DREM) with math and PyTorch
- Dynamic Recursive Entropy Model (DREM) redefines entropy as a recursive input-output function within adaptive systems, supporting structural evolution and complexity generation.
- DREM extends the Fractal Flux framework by integrating entropy as a driver of recursive systemic evolution, emphasizing self-similarity and adaptivity across dimensions.
- Entropy in DREM is modeled recursively, with entropy feedback contributing to useful variance, order emergence, and controlled system adaptation.
- Applications of DREM include utilizing entropy noise in machine learning, AI creativity engines, physics simulations, and multiversal models as an active system component.
- A mathematical signature of DREM introduces a momentum-like formulation for recursive entropy injection in response to system structure and feedback dynamics.
- A PyTorch implementation prototype of DREM demonstrates recursive entropy injection in a module, emphasizing dynamic learnable scaling and recursive feedback mechanisms.
- Future research in DREM includes exploring DRE Autoencoders, entropy-based curriculum learning, dimensional perturbation models, and fractal memory architectures for enhanced recursive learning.
- Dynamic Recursive Entropy complements the Dynamic Entropy Model (DEM) by incorporating recursive feedback mechanisms, enabling long-range systemic learning and evolution within systems.
- Practical implications of integrating DEM and DREM span adaptive learning systems in machine learning, complex system modeling in biology, and the developmental needs of Artificial General Intelligence (AGI) systems.
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