Stars
微舆：人人可用的多Agent舆情分析助手，打破信息茧房，还原舆情原貌，预测未来走向，辅助决策！从0实现，不依赖任何框架。
Message Passing Neural Networks for Molecule Property Prediction
MrPhil / Gladoscheckin
Forked from Devilstore/GladoscheckinGlados自动签到
Implementations of many meta-learning algorithms to solve the few-shot learning problem in Pytorch
PyTorch implementation of VQ-VAE by Aäron van den Oord et al.
Implementation of Generating Diverse High-Fidelity Images with VQ-VAE-2 in PyTorch
Pytorch implementation of Transfusion, "Predict the Next Token and Diffuse Images with One Multi-Modal Model", from MetaAI
SuperPrompt is an attempt to engineer prompts that might help us understand AI agents.
A curated list of resources for using LLMs to develop more competitive grant applications.
Code for paper "Vocabulary Learning via Optimal Transport for Neural Machine Translation"
A list of awesome papers and cool resources on optimal transport and its applications in general! As you will notice, this list is currently mostly focused on optimal transport for machine learning…
a collection of AWESOME things about Optimal Transport in Deep Learning
BioWordVec & BioSentVec: pre-trained embeddings for biomedical words and sentences
Bioinformatics'2020: BioBERT: a pre-trained biomedical language representation model for biomedical text mining
MilGNet: Deep Multiple Instance Learning on Heterogeneous Graph for Drug-disease Association Prediction
基于Python实现的GLaDOS自动签到项目。可部署在青龙面板，支持多账号。
为GPT/GLM等LLM大语言模型提供实用化交互接口，特别优化论文阅读/润色/写作体验，模块化设计，支持自定义快捷按钮&函数插件，支持Python和C++等项目剖析&自译解功能，PDF/LaTex论文翻译&总结功能，支持并行问询多种LLM模型，支持chatglm3等本地模型。接入通义千问, deepseekcoder, 讯飞星火, 文心一言, llama2, rwkv, claude2, m…
End-to-end Structure-Aware Convolutional Networks for Knowledge Base Completion
A Python library for knowledge graph representation learning (graph embedding).
A PyTorch Implementation of Focal Loss.
Share, discover, and collect prompts from the community. Free and open source — self-host for your organization with complete privacy.
Source code of the submission titled as "GraphHAM: Graph Embedding with Hierarchical Attentive Membership"