Tengfei_Matthew's Projects
知识图谱问答系统
PyTorch implementation for COMPLETER: Incomplete Multi-view Clustering via Contrastive Prediction (CVPR 2021)
Awesome Deep Learning papers for industrial Search, Recommendation and Advertising. They focus on Embedding, Matching, Ranking (CTR and CVR prediction), Post Ranking, Multi-task Learning, Graph Neural Networks, Transfer Learning, Reinforcement Learning, Self-supervised Learning and so on.
A collection of resources and papers on Diffusion Models
A collection of research papers and software related to explainability in graph machine learning.
Graph Neural Network
A curated list of AWESOME papers, datasets and tutorials within Multimodal Knowledge Graph.
Reading list for research topics in multimodal machine learning
[Ai4Science@ICML 2022] A curated list of resources for pre-training on (molecular) graphs.
Papers about pretraining and self-supervised learning on Graph Neural Networks (GNN).
Scripts that Bio2RDF users have created to generate RDF versions of scientific datasets
📮 An integrative registry of biological databases, ontologies, and nomenclatures.
Easy-to-use,Modular and Extendible package of deep-learning based CTR models .
The related work for denoising and debiase in drug discovery
Python package built to ease deep learning on graph, on top of existing DL frameworks.
[NeurIPS 2022] DRAGON 🐲: Deep Bidirectional Language-Knowledge Graph Pretraining
A knowledge graph and a set of tools for drug repurposing
drugbank相关数据的处理,包括获取药物sdf结构文件,drug相关信息,drug作用相关蛋白信息,drugbank中drug id与其他数据库的映射,protein蛋白相关信息到其他数据库的映射
动态图表示论文汇总
Feature selector is a tool for dimensionality reduction of machine learning datasets
Running large language models on a single GPU for throughput-oriented scenarios.
A list of recent papers about Graph Neural Network methods applied in NLP areas.
Listing the research works related to risk control based on GNN and its interpretability. 1. we can learn the application of GNN in risk control (including fraud detection). 2. For possible prediction, we can use the interpretability of GNN to explaine how can we get such results.