gyanlala

gyanlala's Stars

• SYSU-SCC/sysu-thesis

  中山大学 LaTeX 论文项目模板

  Language:TeX60974

• LFhase/GALA

  [NeurIPS 2023] Does Invariant Graph Learning via Environment Augmentation Learn Invariance?

  Language:Python212

• owenliang/mnist-clip

  a super easy clip model with mnist dataset for study

  Language:Python8812

• wgrathwohl/JEM

  Project site for "Your Classifier is Secretly an Energy-Based Model and You Should Treat it Like One"

  Language:Python41863

• openai/CLIP

  CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image

  Language:Jupyter Notebook26.9k3.4k

• sindresorhus/awesome

  😎 Awesome lists about all kinds of interesting topics

  342k28.3k

• hendrycks/outlier-exposure

  Deep Anomaly Detection with Outlier Exposure (ICLR 2019)

  Language:Python550109

• wetliu/energy_ood

  Language:Python40365

• qitianwu/GraphOOD-GNNSafe

  The official implementation for ICLR23 paper "GNNSafe: Energy-based Out-of-Distribution Detection for Graph Neural Networks"

  Language:Python765

• Arthurma71/AdvDrop

  Language:Python111

• sdu-zyx/GCL4SR

  Language:Python238

• HKUDS/DCRec

  [WWW'2023] "DCRec: Debiased Contrastive Learning for Sequential Recommendation"

  Language:Python576

• HKUDS/SSLRec

  [WSDM'2024 Oral] "SSLRec: A Self-Supervised Learning Framework for Recommendation"

  Language:Python51566

• Doragd/Algorithm-Practice-in-Industry

  搜索、推荐、广告、用增等工业界实践文章收集（来源：知乎、Datafuntalk、技术公众号）

  Language:Python2.8k348

• qqxiaochongqq/DFN

  Deep Feedback Network for Recommendation

  Language:Python9427

• solidglue/Recommender_System

  推荐系统入门指南，全面介绍了工业级推荐系统的理论知识（王树森推荐系统公开课-基于小红书的场景讲解工业界真实的推荐系统），如何基于TensorFlow2训练模型，如何实现高性能、高并发、高可用的Golang推理微服务。Comprehensively introduced the theory of industrial recommender system, how to trainning models based on TensorFlow2, how to implement the high-performance、high-concurrency and high-available inference services base on Golang.

  Language:Jupyter Notebook50043

• tangxyw/RecSysPapers

  推荐/广告/搜索领域工业界经典以及最前沿论文集合。A collection of industry classics and cutting-edge papers in the field of recommendation/advertising/search.

  Language:Python1.5k215

• datawhalechina/self-llm

  《开源大模型食用指南》针对**宝宝量身打造的基于Linux环境快速微调（全参数/Lora）、部署国内外开源大模型（LLM）/多模态大模型（MLLM）教程

  Language:Jupyter Notebook11.3k1.3k

• exacity/deeplearningbook-chinese

  Deep Learning Book Chinese Translation

  Language:TeX36k9.1k

• DeepGraphLearning/GraphLoG

  Implementation of Self-supervised Graph-level Representation Learning with Local and Global Structure (ICML 2021).

  Language:Python7719

• wangshusen/SearchEngine

  搜索引擎原理

  1.6k130

• wangshusen/RecommenderSystem

  2.7k398

• zjunlp/Mol-Instructions

  [ICLR 2024] Mol-Instructions: A Large-Scale Biomolecular Instruction Dataset for Large Language Models

  Language:Python25916

• wwxFromTju/awesome-reinforcement-learning-lib

  GitHub's code repository is all you need

  33340

• Graph-and-Geometric-Learning/MolGroup

  Learning to Group Auxiliary Datasets for Molecule, NeurIPS2023

  Language:Python18

• HICAI-ZJU/iMoLD

  Official implementation for Learning Invariant Molecular Representation in Latent Discrete Space (NeurIPS 2023)

  Language:Python212

• labmlai/annotated_deep_learning_paper_implementations

  🧑‍🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠

  Language:Python57.8k5.9k

• openai/lm-human-preferences

  Code for the paper Fine-Tuning Language Models from Human Preferences

  Language:Python1.3k163

• opendilab/awesome-RLHF

  A curated list of reinforcement learning with human feedback resources (continually updated)

  3.6k222

• lucidrains/PaLM-rlhf-pytorch

  Implementation of RLHF (Reinforcement Learning with Human Feedback) on top of the PaLM architecture. Basically ChatGPT but with PaLM

  Language:Python7.7k673