Transformer-Based Dynamic Embeddings with Sparse Self-Attention
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Solution Overview
Problem
Current embedding generation processes result in information loss due to simple aggregation, masking, or hiding certain features and data relations, leading to irregular data interactions being overlooked.
Innovation Solution
A system utilizing a transformer-based embedding generation model with a sparse self-attention mechanism that concatenates feature and temporal position encoding datasets, employing an enhanced loss function and residual model to generate holistic embeddings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If simple aggregation is used for embedding generation, then the process is computationally efficient and simple, but information loss occurs and complex data relations are masked
Solution Approach 1:
The patent segments the embedding generation process into distinct transformer encoder layers with self-attention mechanisms, where each layer processes specific aspects of the input data separately before aggregation. This segmentation allows complex relations to be captured in intermediate representations without requiring a monolithic complex model, thus reducing information loss while managing computational complexity.
Solution Approach 2:
The patent introduces intermediate key and value vectors as mediators between the input embeddings and final output embeddings. These intermediaries allow the model to transform and preserve complex data relations through multiple attention-based transformation steps, preventing information loss that would occur in direct simple aggregation.
2Loss of information
If transformer-based models with self-attention are used, then complex data relations are preserved, but computational complexity and processing time increase
Solution Approach 1:
The patent applies partial self-attention by computing attention only for certain token pairs rather than all possible pairs, or by using approximate attention mechanisms. This partial action preserves the ability to capture complex data relations for critical interactions while reducing the overall computational burden and processing time compared to full self-attention.
Solution Approach 2:
The patent segments the self-attention computation into multiple smaller steps across different transformer layers, processing different aspects of data relations in sequence rather than computing all relations simultaneously. This segmentation reduces memory requirements and allows for more efficient parallelization, decreasing processing time while preserving relation capture capability.
3Measurement precision
If feature engineering is performed to capture complex interactions, then model accuracy improves, but operational costs and processing complexity increase
Solution Approach 1:
The patent enables the model to automatically learn and represent complex feature interactions through the self-attention mechanism without requiring manual feature engineering. The transformer model serves itself by dynamically identifying and weighting important feature relationships during training, eliminating the need for external feature engineering processes while maintaining high model accuracy.
Solution Approach 2:
The patent changes the approach from fixed hand-crafted features to dynamic learned features through attention weights. The model automatically adjusts the importance parameters of different features based on the input data, allowing it to capture complex interactions adaptively without the complexity of manual feature engineering pipelines.
Data Source
AI summary
In various embodiments, systems and methods of generating embeddings using transformer-based generative AI processes are disclosed. A sequence dataset is received and a feature dataset including a plurality of feature sets and a temporal position encoding dataset including a plurality of individual encoding sets is extracted from the sequence dataset. Each of the plurality of feature sets are concatenated with a corresponding one of the plurality of individual encoding sets to generate a concatenated feature set. An embedding generation model is implemented to generate an embedding based on the concatenated feature set. The embedding generation model comprises a sparse self-attention mechanism. The embedding is stored in an embedding store.