# Multitask optimization and convergence stability with hierarchical feature learning for self guided optimization

## TL;DR

This paper introduces a unified deep learning architecture that improves multitask optimization stability and performance through novel attention and pooling mechanisms.

## Contribution

The novel UMDA architecture introduces hybrid cross-view attention, adaptive task branching, and graph-based pooling for stable multitask optimization.

## Key findings

- Achieved 88.3% multitask classification accuracy
- Reached 0.973 cross-view feature consistency
- Reduced gradient variance by 4.2% during training

## Abstract

The optimization process of multimodal multitask architectures faces three major problems which include unstable optimization and unresolved cross-task interference and insufficient alignment between different feature views. The solution of these system failure points needs direct management of view-specific relationships and task-dependent feature extraction and multi-instance data processing methods. The Unified Multitask and Multiview Deep Architecture (UMDA) solves all optimization problems through its four interconnected computational blocks which operate as a unified system. The Hybrid Cross-View Attention module generates two types of attention operators which establish controlled inter-view relationships through entropy-based concentration mechanisms and cross-view consistency penalties and dispersion constraints that stop modalities from collapsing into each other. The Adaptive Task-Specific Branching module uses dual-path factorization to identify common elements in task projections which generates influence matrices that handle hierarchical task relationships through penalty functions for divergence and consistency. The Graph-Based Multi-Instance Pooling operator processes multi-instance data by building graphs and performing Laplacian propagation and structural signature aggregation based on higher-order tensor interactions that follow entropy and graph-smoothness rules. The Self-Guided Learning method achieves stable optimization through two mechanisms which use gradient magnitudes to adjust task-specific learning rates and combine weighted gradients to reduce objective function variance. The combined mechanisms in the system achieve 88.3% multitask classification accuracy and 0.973 cross-view feature consistency and 4.2% gradient variance reduction during identical training and resource conditions.