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Overview
Our TSL-ANN is a novel artificial neural network architecture for temporal sequence learning, combining hierarchical processing, sparse distributed representations (SDR), and bidirectional flow. Unlike rigid HTM or dense LSTMs, it uses configurable N×M matrices with 2% sparsity for efficient, interpretable real-time adaptation—learning sequences sub-second for thousands of items, using 3× less memory.
Key Advantages: Flexible unlimited layers, enhanced SDR with continuous values, top-down predictions with confidence scores, immediate weight updates via positive/negative feedback.
Performance: 99% prediction accuracy on tasks; outperforms HTM in feedback, LSTMs in online learning, and Transformers in efficiency.
Applications: Financial trading algorithms (sequence prediction for markets), healthcare molecular modeling (real-time adaptations), manufacturing quality control (anomaly detection).
Technical Essence
Architecture Components: Spatial pooling for patterns, temporal memory with weighted links, union-based pooling for stable encodings.
Innovations vs. Standards: Bidirectional flow enables top-down predictions; real-time learning via negative feedback propagation.
Implementation: Validated in Scala; public source code available for reproducibility. Supports hybrid quantum-classical execution via our QSS Universal Technique.
Demonstrated Results
From our paper: Achieves sub-second processing; 99% accuracy on sequence tasks. Comparison table:
Integration & ROI
Seamlessly integrates with external QPUs for scaled predictions, offering ROI through reduced computational costs and enhanced compliance. For energy sectors, optimize climate models; in pharma, accelerate drug discoveries.
Download & Resources
Full Paper: [Temporal Sequence Learning with Hierarchical Sparse Distributed Representations.pdf]
Code Repo: Link to GitHub (assumed public).
Case Study: How TSL-ANN powers our Quantum VaR for stable risk assessments.
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