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  1. Home
  3. Learning To Route: Per-sample Adaptive Routing For Multimodal Multitask Prediction.
  1. Home
  3. Learning To Route: Per-sample Adaptive Routing For Multimodal Multitask Prediction.

Related Experiment Videos

Learning to Route: Per-Sample Adaptive Routing for Multimodal Multitask Prediction.

Marzieh Ajirak1, Oded Bein1, Ellen Rose Bowen1

  • 1Weill Cornell Medicine, Cornell University, NY, USA.

Advances in Neural Information Processing Systems
|June 8, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a novel adaptive routing framework for multitask, multimodal prediction. The model dynamically processes diverse data and task interactions per sample, improving personalized healthcare outcomes.

Related Experiment Videos

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computational Linguistics

Background:

  • Data heterogeneity and varying task interactions pose challenges in multitask, multimodal prediction.
  • Existing models often use fixed strategies, failing to adapt to sample-specific data characteristics.

Purpose of the Study:

  • To develop a unified framework for adaptive routing in multitask, multimodal prediction.
  • To dynamically select modality processing pathways and task-sharing strategies on a per-sample basis.

Main Methods:

  • Introduced a routing-based architecture with multiple modality paths (raw and fused text/numeric features).
  • Learned to route inputs to the most informative modality-task expert combination.
  • Employed shared or independent heads for task-specific predictions, trained end-to-end.
  • Evaluated on synthetic and real-world psychotherapy notes for depression and anxiety prediction.

    • Main Results:

    • The proposed method consistently outperformed fixed multitask and single-task baselines.
    • The learned routing policy offered interpretable insights into modality relevance and task structure.
    • Demonstrated effective per-subject adaptive information processing.

    Conclusions:

    • The adaptive routing framework effectively handles data and task correlation heterogeneity.
    • This approach advances personalized healthcare by tailoring information processing to individual needs.
    • The model provides a flexible and interpretable solution for complex prediction tasks.