3 dépôts
Awesome GitHub RepositoriesShared-Bottom Architectures
Multi-task learning architectures that share bottom layers across multiple prediction objectives to learn from related tasks simultaneously.
Distinct from Multi-Task Learning Models: Distinct from Multi-Task Learning Models: specifically focuses on the shared-bottom architecture pattern rather than general multi-task sequence models.
Explore 3 awesome GitHub repositories matching artificial intelligence & ml · Shared-Bottom Architectures. Refine with filters or upvote what's useful.
Awesome Shared-Bottom Architectures GitHub Repositories
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- shenweichen/deepctr
shenweichen/DeepCTR
8,039Voir sur GitHubDeepCTR is a specialized software framework and deep learning model library designed for predicting click-through rates and implementing recommendation systems. It provides a suite of tabular data models and architectures tailored for binary classification and sparse feature processing. The framework includes dedicated toolkits for multi-task learning and sequential interest modeling. It allows for the simultaneous estimation of multiple related targets through shared-bottom and gated expert neural networks, while capturing evolving user behavior using attention mechanisms and transformers.
Implements shared-bottom architectures to estimate multiple related targets using shared neural representations.
Pythonautointclick-through-ratectr - facebookresearch/sapiens
facebookresearch/sapiens
5,388Voir sur GitHubSapiens est un modèle de vision humaine haute résolution conçu pour des tâches de vision par ordinateur centrées sur l'humain de haute précision. Il fonctionne comme une suite d'outils pour estimer la pose humaine, la profondeur et la géométrie de surface. Le projet utilise une architecture vision transformer comme backbone pour effectuer plusieurs tâches via un encodeur partagé. Cette architecture permet la prédiction simultanée des structures squelettiques, des emplacements des articulations et de la distance entre une caméra et un sujet humain. Les capacités du modèle couvrent la segmentation des parties du corps humain pour isoler les régions anatomiques des arrière-plans et la prédiction des normales de surface pour récupérer les détails géométriques 3D à partir d'images 2D. Ces tâches sont soutenues par un framework d'apprentissage multi-tâches qui utilise la régression au niveau des pixels et le masquage par segmentation sémantique.
Uses a shared-bottom architecture to extract features for multiple specialized human-centric output heads.
Python - shenweichen/deepctr-torch
shenweichen/DeepCTR-Torch
3,376Voir sur GitHubDeepCTR-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
Shares bottom layers across multiple prediction objectives to learn click and conversion rates simultaneously.
Pythonctr-modelsdeep-learningdeepctr
