3 repository-uri
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
Găsește cele mai bune repo-uri cu AI.Vom căuta cele mai potrivite repository-uri folosind AI.
- shenweichen/deepctr
shenweichen/DeepCTR
8,039Vezi pe 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
5,388Vezi pe GitHubSapiens este un model de viziune umană de înaltă rezoluție conceput pentru sarcini de computer vision centrate pe om, de înaltă precizie. Acesta funcționează ca o suită de instrumente pentru estimarea posturii umane, a adâncimii și a geometriei suprafeței. Proiectul utilizează un backbone de tip vision transformer pentru a îndeplini sarcini multiple printr-un encoder partajat. Această arhitectură permite predicția simultană a structurilor scheletice, a locațiilor articulațiilor și a distanței dintre o cameră și un subiect uman. Capabilitățile modelului acoperă segmentarea părților corpului uman pentru a izola regiunile anatomice de fundal și predicția normalelor suprafeței pentru a recupera detalii geometrice 3D din imagini 2D. Aceste sarcini sunt susținute de un framework de învățare multi-task care utilizează regresia la nivel de pixel și mascarea prin segmentare semantică.
Uses a shared-bottom architecture to extract features for multiple specialized human-centric output heads.
Python - shenweichen/deepctr-torch
shenweichen/DeepCTR-Torch
3,376Vezi pe 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
