Multi-Task Learning Policy

A Multi-Task Learning Policy is a machine learning optimization learning policy that governs how multiple learning tasks share model parameters and training resources to improve generalization performance through task synergy.

• AKA: MTL Policy, Joint Learning Policy, Shared Learning Policy, Multi-Objective Learning Policy.
• Context:
  • It can typically define Task Weight Assignment for loss function balancing.
  • It can typically specify Parameter Sharing Strategy across task-specific layers.
  • It can typically establish Task Sampling Protocol for training batch composition.
  • It can often implement Dynamic Task Prioritization based on learning progress.
  • It can often enforce Negative Transfer Prevention through task relationship monitoring.
  • It can often enable Curriculum Learning Schedule for task complexity progression.
  • ...
  • It can range from being a Hard Parameter Sharing Policy to being a Soft Parameter Sharing Policy, depending on its sharing mechanism.
  • It can range from being a Static Multi-Task Learning Policy to being a Adaptive Multi-Task Learning Policy, depending on its adjustment capability.
  • It can range from being a Homogeneous Multi-Task Learning Policy to being a Heterogeneous Multi-Task Learning Policy, depending on its task diversity.
  • It can range from being a Synchronous Multi-Task Learning Policy to being an Asynchronous Multi-Task Learning Policy, depending on its update timing.
  • ...
  • It can guide Multi-Task Neural Architecture through design principles.
  • It can optimize Multi-Task Training Process through resource allocation.
  • It can improve Multi-Task Model Performance through knowledge transfer.
  • It can reduce Multi-Task Overfitting Risk through regularization effect.
  • ...
• Example(s):
  • Domain-Specific Multi-Task Learning Policys, such as:
    • NLP Multi-Task Learning Policy for language understanding tasks.
    • Computer Vision Multi-Task Learning Policy for visual recognition tasks.
    • Speech Multi-Task Learning Policy for audio processing tasks.
  • Architecture-Based Multi-Task Learning Policys, such as:
    • Shared Encoder Policy with common representation learning.
    • Cross-Stitch Network Policy with task-specific combination.
    • Multi-Gate Mixture-of-Experts Policy with selective sharing.
  • Optimization-Based Multi-Task Learning Policys, such as:
    • GradNorm Policy for gradient magnitude balancing.
    • Uncertainty Weighting Policy for task importance estimation.
    • Dynamic Weight Average Policy for performance-based adjustment.
  • ...
• Counter-Example(s):
  • Single-Task Learning Policy, which focuses on one task without sharing.
  • Transfer Learning Policy, which adapts from source to target rather than joint learning.
  • Independent Learning Policy, which trains separate models without interaction.
  • Sequential Learning Policy, which learns tasks one after another rather than simultaneously.
• See: Multi-Task Learning, Transfer Learning Policy, Meta-Learning Policy, Parameter Sharing, Task Relationship, Joint Training, Optimization Strategy, Learning Policy.

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