JSON++: The Symbolic Substrate for Active Data
A proposal for JSON++, a grammar-extensible substrate for active data, bridging the gap between static serialization and dynamic processing.
Learning & Training
Documents in Learning & Training (15)
Contextual Immersion Learning: A Novel Paradigm for AI-Mediated Knowledge Transfer
A novel paradigm for AI-mediated knowledge transfer that abandons traditional instructional models in favor of organic concept introduction through practical communication needs.
Differentiable Basis Transform Trust-Region Dropout: A Universal Framework for Adaptive Regularization
A universal framework combining differentiable basis transforms with trust-region optimization for adaptive dropout regularization in neural networks
Probabilistic Neural Substrates: A Cross-Entropy Approach to Recurrent Intelligence
A novel computational paradigm proposing Probabilistic Neural Substrates (PNS) that maintain continuous probability distributions through cross-entropy optimization, enabling self-organizing recurrent intelligence with unprecedented interpretability and uncertainty quantification.
Entropy-Optimized Text Classification: Integrating Compression-Based Learning with Permutation-Aware Data Structures
A novel framework unifying compression-based text classification with entropy-optimized data structures for efficient, interpretable AI systems
Scientific Method Proposal for AI Research
A proposal for applying rigorous scientific methodology to AI research, ensuring empirical validation and reproducible results.
Hypothesis Breeding Grounds
A framework for creating environments that foster hypothesis generation and scientific creativity through systematic exploration.
Quantum Dropout Vision: Learning from Loss Through Quantum-Classical Parallels
Exploring the profound parallels between quantum decoherence and neural network dropout to develop unified frameworks for robust information processing across computational paradigms processing
Dropout as Decoherence: Toward a Fractal Theory of Epistemic Filtering
A theoretical framework proposing that neural network dropout functions as cognitive analog to quantum decoherence through epistemic filtering
Co-Inverse Permutation Modifiers for Neural Networks: Exploiting Weight Symmetries for Post-Training Optimization
A framework exploiting neural network permutation symmetries for post-training optimization, enabling structured pruning and improved interpretability
Geometric Probabilistic Neural Substrates: Information Flow on Optimized Manifolds
Revolutionary synthesis of geometric optimization with Probabilistic Neural Substrates, creating self-organizing intelligent systems with unprecedented mathematical elegance.
Dual-Constraint Training with Adaptive Anomaly Preservation: A Trust Region Approach for Protecting Intellectual Diversity in Neural Networks
A novel dual-constraint training methodology that preserves intellectual diversity while enabling continued learning in neural networks through adaptive anomaly preservation and trust region approaches.
The Transformation of Software Development: Navigating the AI Revolution in Software Productization
Comprehensive analysis of AI's transformative impact on software development, examining current trends, future projections, and best practices for conscious evolution in the age of autonomous development.
Chaotic Dynamics in Large Language Model Iterative Feedback Systems: A Framework for Understanding Convergence, Divergence, and Human-AI Collaboration
A comprehensive framework analyzing chaotic dynamics in LLM iterative feedback systems, exploring convergence patterns, systematic biases, and optimal human intervention strategies.
Llm Knowledge Graph Proposal
date: 2025-07-06 title: “Mamba-Based Neural Knowledge Graph Integration: A Research Proposal” layout: post date: 2025-01-07 last_modified: 2025-01-07 10:00:00