MMoEEx Heterogeneous Multi-Task Learning Architecture
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Solution Overview
Problem
Current multi-task learning approaches struggle with heterogeneous tasks that involve different types of data, such as classification, regression, and temporal data, as they often assume a homogeneous set of tasks, leading to challenges in balancing shared and task-specific representations, which can result in overfitting and negative transfer.
Innovation Solution
The introduction of a multi-gate mixture-of-experts with exclusivity (MMoEEx) approach, which induces diversity among experts through exclusion and exclusivity conditions, and a two-step task balancing optimization at the gradient level, allowing for the learning of unbalanced heterogeneous tasks by assigning exclusive and shared expert models to each task.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a single model is optimized for multiple heterogeneous tasks simultaneously, then computational efficiency and memory requirements are reduced, but task balancing becomes difficult and negative transfer occurs
Solution Approach 1:
The patent segments the expert models into task-specific exclusive experts and shared experts. Each task has dedicated exclusive experts that do not contribute to other tasks, while shared experts are accessible by multiple tasks. This segmentation resolves the contradiction by reducing task balancing complexity through clear separation of responsibilities while maintaining computational efficiency through shared resources.
Solution Approach 2:
The patent applies local quality by allowing different experts to have different levels of accessibility across tasks. Exclusive experts provide specialized local knowledge for specific tasks, while shared experts provide general knowledge accessible by multiple tasks. This resolves the contradiction by enabling fine-grained control over task balancing without sacrificing computational efficiency.
2Adaptability or versatility
If shared expert models are used across all tasks, then parameter efficiency improves, but some tasks may be susceptible to overfitting or negative transfer
Solution Approach 1:
The patent segments experts into exclusive and shared categories. Exclusive experts are dedicated to specific tasks and do not contribute to other tasks, providing task-specific specialization that prevents overfitting. Shared experts are accessible by multiple tasks, providing parameter efficiency. This segmentation resolves the contradiction by allowing each task to benefit from both shared parameters and dedicated specialists.
Solution Approach 2:
The gate mechanisms act as intermediaries that control which experts contribute to which tasks. The gates learn to dynamically select appropriate experts for each task, preventing harmful negative transfer while maintaining beneficial shared representations. This resolves the contradiction by providing adaptive control over expert utilization.
3Reliability
If task-specific expert models are used for each task, then overfitting is reduced, but computational and memory requirements increase
Solution Approach 1:
The patent merges multiple expert models into a unified architecture where exclusive and shared experts work together. Instead of completely separate models for each task, the system combines task-specific exclusive experts with shared experts that serve multiple tasks, reducing overall computational and memory requirements while maintaining overfitting resistance.
Solution Approach 2:
Shared expert models serve multiple tasks simultaneously, providing multi-functionality. These shared experts capture common patterns across tasks, reducing the need for separate parameters for each task while maintaining task-specific performance through exclusive experts.
4Device complexity
If homogeneous task assumptions are made in multi-task learning, then optimization is simplified, but heterogeneous tasks cannot be effectively balanced
Solution Approach 1:
The patent segments the expert models based on task heterogeneity. Exclusive experts handle task-specific characteristics while shared experts handle common patterns. This segmentation allows the system to effectively handle heterogeneous tasks without requiring completely separate optimization processes for each task type.
Solution Approach 2:
The patent applies local quality by allowing different experts to specialize in different task characteristics. Each exclusive expert is optimized for its specific task's unique requirements, while shared experts are optimized for common patterns across tasks. This resolves the contradiction by enabling specialized optimization where needed while maintaining general optimization efficiency.
Data Source
AI summary
A computer system and method for training a heterogeneous multi-task learning network is provided. The system comprises at least one processor and a memory storing instructions which when executed by the processor configure the processor to perform the method. The method comprises assigning expert models to each task, processing training input for each task, and storing a final set of weights. For each task, weights in the expert models and in gate parameters are initialized, training inputs are provided to the network, a loss is determined following a forward pass over the network, and losses are back propagated and weights are updated for the experts and the gates. At least one task is assigned one exclusive expert model and at least one shared expert model accessible by the plurality of tasks.