negative-sampling

🎯 Task-oriented embedding tuning for BERT, CLIP, etc.

#自然语言处理#基于Pytorch和torchtext的自然语言处理深度学习框架。

This repository collects 100 papers related to negative sampling methods.

Extremely simple and fast word2vec implementation with Negative Sampling + Sub-sampling

Reinforced Negative Sampling over Knowledge Graph for Recommendation, WWW2020

#自然语言处理#SimXNS is a research project for information retrieval. This repo contains official implementations by MSRA NLC team.

MixGCF: An Improved Training Method for Graph Neural Network-based Recommender Systems, KDD2021

#计算机科学#⚡️ Implementation of TRON: Transformer Recommender using Optimized Negative-sampling, accepted at ACM RecSys 2023.

Embedding模型代码和学习笔记总结

Implements various negative sampling techniques for learning knowledge graph embeddings

[Paper][LREC-COLING 2024] Unleashing the Power of Imbalanced Modality Information for Multi-modal Knowledge Graph Completion

Implementation of paper "Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval"

Treat Different Negatives Differently: Enriching Loss Functions with Domain and Range Constraints for Link Prediction

A PyTorch Implementation of the Skipgram Negative Sampling Word2Vec Model as Described in Mikolov et al.

This Machine Learning project deals with Coupon Recommendations based on Revenue Uplift

A Jax implementation of word2vec's skip-gram model with negative sampling as described in Mikolov et al., 2013

#自然语言处理#Course Materials (along with assignments) for Intro to NLP, done as a part for requirement of the course "Introduction to NLP" (course-code: CS7.401.S22) @ IIITH. Note: If you are cloning this or tak...

"SCONE: A Novel Stochastic Sampling to Generate Contrastive Views and Hard Negative Samples for Recommendation", WSDM 2025

PyTorch implementation for " Conditional Negative Sampling for Contrastive Learning of Visual Representations" (https://arxiv.org/abs/2010.02037).

[Paper][IJCNN2023] Modality-Aware Negative Sampling for Multi-modal Knowledge Graph Embedding