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[Up-to-date] Large Language Model Agent: A Survey on Methodology, Applications and Challenges
《代码随想录》LeetCode 刷题攻略：200道经典题目刷题顺序，共60w字的详细图解，视频难点剖析，50余张思维导图，支持C++，Java，Python，Go，JavaScript等多语言版本，从此算法学习不再迷茫！🔥🔥 来看看，你会发现相见恨晚！🚀
Awesome LeetCode resources to learn Data Structures and Algorithms and prepare for Coding Interviews.
LMDeploy is a toolkit for compressing, deploying, and serving LLMs.
Transformer based on a variant of attention that is linear complexity in respect to sequence length
Repo to accompany my mastering LLM engineering course
This repo is meant to serve as a detailed guide for Machine Learning/AI interviews.
Self-supervised graph learning with hyperbolic embedding for temporal health event prediction (IEEE Cybernetics)
[ICLR'24] Enhancing Healthcare Predictions with Personalized Knowledge Graphs
Awesome-LLM: a curated list of Large Language Model
Cell2Sentence: Teaching Large Language Models the Language of Biology
A collection of resources to study Transformers in depth.
Code for BEHRT: Transformer for Electronic Health Records
KDD 2024 | FlexCare: Leveraging Cross-Task Synergy for Flexible Multimodal Healthcare Prediction
This is the homepage of a new book entitled "Mathematical Foundations of Reinforcement Learning."
32 projects in the framework of Deep Reinforcement Learning algorithms: Q-learning, DQN, PPO, DDPG, TD3, SAC, A2C and others. Each project is provided with a detailed training log.
Do Transformers Really Perform Bad for Graph Representation? [NIPS-2021]
DrFuse: Learning Disentangled Representation for Clinical Multi-Modal Fusion with Missing Modality and Modal Inconsistency (AAAI24)
A pytorch library for graph and hypergraph computation.
This is the GitHub repository for our ICLR22 paper: "You are AllSet: A Multiset Function Framework for Hypergraph Neural Networks"
[ML4H 2022] This is the code for our paper `Counterfactual and Factual Reasoning over Hypergraphs for Interpretable Clinical Predictions on EHR'.
本项目是一个面向小白开发者的大模型应用开发教程，在线阅读地址：https://datawhalechina.github.io/llm-universe/
HEART: Learning Better Representation of EHR data with a Heterogeneous Relation-Aware Transformer, Journal of Biomedical Informatics
The official implementation of the ICML'24 paper "A Graph is Worth K Words: Euclideanizing Graph using Pure Transformer".