Parallel Graph Inference from Images and Text
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Solution Overview
Problem
Graph generation systems face challenges in producing complete graphs in a single step due to their sequential or autoregressive nature, which introduces complexity and limits their ability to self-correct, leading to custom approaches for different applications.
Innovation Solution
A machine learning model is trained to infer graphs from various inputs like images, text, and videos, outputting all nodes and edges in parallel, using a unified approach that eliminates the need for sequential decoding and allows for end-to-end learning, enabling faster inference and simplifying the learning process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If graph generation systems generate graphs one piece at a time in sequential or autoregressive manner, then the generation process can be controlled step-by-step, but the system complexity increases and the ability to self-correct is limited
Solution Approach 1:
The graph generation process is segmented into two independent parallel processes: node generation and edge generation. Each process operates independently but contributes to the complete graph output, allowing the system to maintain control while reducing sequential dependencies that limit self-correction and increase complexity.
Solution Approach 2:
The patent merges the node generation and edge generation processes into a unified parallel framework where both operations occur simultaneously rather than sequentially. This combining of operations reduces system complexity by eliminating the need for multiple sequential decoding steps while maintaining the ability to generate complete graphs in one pass.
2Productivity
If graph generation systems produce all nodes and edges at one time, then the inference speed improves, but the systems lack the self-correction capability of sequential methods
Solution Approach 1:
The simultaneous generation of nodes and edges is achieved through segmentation into independent parallel processes. Each process (node generation and edge generation) maintains its own computational pathway, enabling faster inference while preserving the structural integrity and logical consistency that support self-correction capabilities.
3Adaptability or versatility
If custom approaches are developed for different applications, then the systems can handle diverse problem formulations, but the overall system complexity increases
Solution Approach 1:
The parallel decoding framework is designed as a universal system that can handle diverse graph generation tasks through a single unified architecture. By making the system multi-functional through its parallel node and edge generation capabilities, it can accommodate different problem formulations without requiring separate custom approaches, thereby reducing overall system complexity while maintaining versatility.
Data Source
AI summary
The present disclosure relates to systems and methods that receive an input and infer a predicted graph based on information in the input. The systems and methods provide a representation of the predicted graph with a set of nodes and a set of edges. Various processing or tasks may be performed on the information provided in the predicted graph.