Decoupled Encoder-Decoder Models for Shared Embedding Tasks
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Solution Overview
Problem
Existing machine-trained models require significant computing resources for execution and maintenance due to their end-to-end task-specific nature, limiting flexibility and efficiency.
Innovation Solution
A decoupled model architecture that separates encoder-type models for converting input data types into embeddings and decoder-type models for converting embeddings into output data types, allowing dynamic selection and combination of models based on task requirements, with a shared embedding space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional end-to-end machine-trained models are used for each specific task, then task performance is achieved, but computing resources required for execution and maintenance increase significantly
Solution Approach 1:
The patent segments traditional end-to-end machine-trained models into separate encoder-type models and decoder-type models. Encoder models convert input data items of different types into embeddings in a shared embedding space, while decoder models convert embeddings back into output data items. This segmentation allows the system to reuse encoder and decoder models across multiple tasks, reducing the total number of models needed and thereby conserving computing resources for execution and maintenance.
2Reliability
If separate end-to-end models are maintained for each task, then task-specific performance is optimized, but model maintenance and updates become increasingly complex
Solution Approach 1:
The patent creates universal encoder and decoder models that can be applied across multiple tasks through a shared embedding space. Encoder models process various input data types (text, images, audio) and convert them to embeddings, while decoder models convert embeddings back to output data items. This multi-functionality allows a single encoder or decoder model to serve multiple tasks, significantly simplifying model maintenance and updates compared to maintaining separate end-to-end models for each task.
3Adaptability or versatility
If a large number of specialized models are stored for diverse tasks, then task coverage is improved, but storage and maintenance requirements increase
Solution Approach 1:
The patent segments the model architecture into reusable encoder and decoder components that operate through a shared embedding space, replacing the need for numerous specialized end-to-end models.
Solution Approach 2:
The patent implements universal encoder and decoder models that can handle diverse tasks through the shared embedding space, enabling a small number of models to provide extensive task coverage.
Data Source
AI summary
A technique executes tasks using a data store of machine-trained models. The data store specifically includes a subset of encoder-type machine-trained models for converting input data items having different input data types into respective embeddings in a vector space, and a subset of decoder-type machine-trained models for converting embeddings in the same vector space into data items having respective different output data types. When executing a particular task that involves one or more data types, the technique selects one or more machine-trained models that match those data types. In some implementations, the technique provides a clipboard store for storing embeddings produced by the encoder-type machine-trained models and consumable by the decoder-type machine-trained models. The technique includes provisions for ensuring that any decoder-type machine-model is capable of processing embeddings produced by different versions of the encoder-type machine-trained models.