Matrix Data Encoding for Deep Neural Network Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing deep neural networks are inefficient in handling combinatorial optimization problems, as they typically require data in list form, while many such problems are more naturally represented in matrix form, particularly in graph data, necessitating a new structure to encode matrix data for effective processing.
Innovation Solution
The proposed solution involves an apparatus and method for encoding matrix data using an initial encoding layer that outputs encoded row and column vectors, followed by additional encoding layers, which include specific encoding blocks performing element-wise operations and self-attention-based networks to transform the data into a form suitable for deep neural networks, allowing for efficient combinatorial optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is represented in list form for deep neural network processing, then the neural network can process the data, but combinatorial optimization problems cannot be effectively solved because they are naturally represented in matrix form
Solution Approach 1:
The patent introduces an encoding layer as an intermediary component that transforms matrix-form combinatorial optimization data into list-form representations suitable for deep neural network processing. This encoding layer acts as a mediator between the matrix data structure (natural for combinatorial problems) and the list-based input requirements of neural networks, enabling effective processing of combinatorial optimization problems while maintaining the advantages of both formats
2Ease of operation
If matrix data is directly input to deep neural network, then the natural representation of combinatorial problems is preserved, but the neural network cannot process the data effectively due to format incompatibility
Solution Approach 1:
The patent segments the processing pipeline into distinct components: an encoding layer that breaks down matrix data into row and column vector representations, followed by separate processing paths for these segmented vectors. This segmentation allows the matrix structure to be preserved in a meaningful way while adapting it to the neural network's processing requirements, maintaining both representation suitability and processing effectiveness
Data Source
AI summary
An apparatus for encoding matrix data according to an embodiment includes an initial encoding layer that outputs a plurality of encoded row vectors and a plurality of encoded column vectors for one or more pieces of matrix data representing information on a relationship between items having a plurality of characteristics based on the matrix data, a plurality of initial row vectors corresponding to the number of rows of the matrix data, and a plurality of initial column vectors corresponding to the number of columns of the matrix data, and one or more encoding layers that are disposed after the initial encoding layer and perform additional encoding on the plurality of encoded row vectors and the plurality of encoded column vectors.