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Awesome GitHub RepositoriesProgressive Layer Separation
Architectures that progressively split shared and task-specific components in multi-task learning.
Distinct from Click-Through Rate Multi-Task Models: Distinct from Shared-Bottom Architectures: separates shared and task-specific features through progressive layers rather than a single shared bottom.
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Awesome Progressive Layer Separation GitHub Repositories
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- shenweichen/deepctr-torch
shenweichen/DeepCTR-Torch
3,376在 GitHub 上查看DeepCTR-Torch is a deep learning library for building click-through rate prediction models. It provides a modular framework for assembling custom prediction architectures from pre-built core, interaction, and sequence layers, enabling the construction of deep neural networks that estimate click probability from user behavior data. The library specializes in feature interaction modeling, offering components for learning low-order, high-order, and adaptive-order feature crosses. It supports multi-task learning for predicting multiple objectives simultaneously, such as click and conversion rates
Route information through progressive layers that split shared and task-specific components for better multi-task learning.
Pythonctr-modelsdeep-learningdeepctr
