Neural Network Spacetime Embeddings for Data Dependency Prediction
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Solution Overview
Problem
Training neural networks based on graph representations of data dependencies is computationally complex and often results in artifactual data dependencies and inaccurate predictions due to challenges in handling directed cycles.
Innovation Solution
The use of spacetime representations generated by neural networks to indicate data dependencies and their directions, allowing for accurate prediction even in the presence of cycles, by embedding graph representations into spacetime and using Lorentzian distances to construct edges in updated graph representations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If graph representations are used to represent data dependencies, then data dependency relationships can be captured, but handling directed cycles creates computational complexity and mathematical challenges
Solution Approach 1:
The patent transforms the graph representation problem from traditional graph theory into a spacetime embedding framework. By mapping graph nodes and edges into a continuous spacetime manifold with Lorentzian metric, the invention handles directed cycles naturally through the causal structure of spacetime, avoiding the computational complexities that arise from cyclic dependencies in traditional graph representations.
2Adaptability or versatility
If traditional graph representations are used with directed cycles, then cyclic data dependencies can be represented, but this results in inaccurate or imprecise predictions during inferencing
Solution Approach 1:
The invention changes the fundamental parameters of the representation system by introducing spacetime coordinates and Lorentzian metric tensors. This parameter transformation allows cyclic dependencies to be represented with precise causal relationships, where the spacetime interval and causal structure provide mathematically rigorous definitions of dependency directions, eliminating the imprecision that plagues traditional graph-based approaches.
3Measurement precision
If computational resources are increased to handle graph representations accurately, then prediction accuracy can be improved, but this increases training time and memory requirements
Solution Approach 1:
The patent replaces the mechanical computational approach of traditional graph algorithms with a geometric spacetime framework. By using Lorentzian distance calculations and causal structure analysis instead of iterative graph traversal and cycle detection algorithms, the invention achieves accurate prediction of data dependencies with reduced computational overhead, thereby decreasing training time while maintaining or improving accuracy.
Data Source
AI summary
Apparatuses, systems, and techniques to indicate data dependencies. In at least one embodiment, one or more neural networks are used to generate one or more indicators of one or more data dependencies and one or more indicators of direction of the one or more data dependencies.