Pinned Repositories
2i_bench
The code for the experiments in the paper "Techniques for Inverted Index Compression" by Giulio Ermanno Pibiri and Rossano Venturini. ACM Computing Surveys (CSUR).
A-Dynamic-Meta-Learning-Model-for-Time-Sensitive-Cold-Start-Recommendations
A novel dynamic recommendation model that focuses on users who have interactions in the past but turn relatively inactive recently
aaai-2021-counterfactuals
deep-finance
Deep Learning for Finance
deepx_core
DynGNN
some code and papers related to dynamic graph neural networks
leetcode_company_wise_questions
masr-1
中文语音识别系列，读者可以借助它快速训练属于自己的中文语音识别模型，或直接使用预训练模型测试效果。
mtgbmcode
mtgbmcode
XGrad-CAM
kiminh's Repositories
kiminh/AlignX
From 1,000,000 Users to Every User: Scaling Up Personalized Preference for User-level Alignment
kiminh/BCEmbedding
Netease Youdao's open-source embedding and reranker models for RAG products.
kiminh/context-cite
Attribute (or cite) statements generated by LLMs back to in-context information.
kiminh/e-commerce-product-search
Relevance Prediction in E-commerce Product Search
kiminh/FeatInsight
FeatInsight is a feature platform based on OpenMLDB
kiminh/generative-rec
kiminh/GiGL
Gigantic Graph Learning (GiGL) Framework: Large-scale training and inference for Graph Neural Networks
kiminh/glake
GLake: optimizing GPU memory management and IO transmission.
kiminh/graphrag
A modular graph-based Retrieval-Augmented Generation (RAG) system
kiminh/HanLP
中文分词 词性标注 命名实体识别 依存句法分析 成分句法分析 语义依存分析 语义角色标注 指代消解 风格转换 语义相似度 新词发现 关键词短语提取 自动摘要 文本分类聚类 拼音简繁转换 自然语言处理
kiminh/KddCup-2024-OAG-Challenge-1st-Solutions
kiminh/Liger-Kernel
Efficient Triton Kernels for LLM Training
kiminh/meridian
Meridian is an MMM framework that enables advertisers to set up and run their own in-house models.
kiminh/MobileAgent
Mobile-Agent: The Powerful Mobile Device Operation Assistant Family
kiminh/Multi-Agent-AI-Bidding-System
First-of-its-kind platform that combines multi-agent reinforcement learning with real-time market analysis to provide data-driven bidding recommendations that maximize both win probability and profitability. The system simulates realistic competitive environments where AI agents learn optimal bidding strategies, helping users make informed decision
kiminh/multi-process
python并发学习调研过程
kiminh/note_llm
kiminh/opencompass
OpenCompass is an LLM evaluation platform, supporting a wide range of models (Llama3, Mistral, InternLM2,GPT-4,LLaMa2, Qwen,GLM, Claude, etc) over 100+ datasets.
kiminh/pairec
A Go web framework for quickly building recommendation online services based on JSON configuration.
kiminh/paper_reading
kiminh/petastorm
Petastorm library enables single machine or distributed training and evaluation of deep learning models from datasets in Apache Parquet format. It supports ML frameworks such as Tensorflow, Pytorch, and PySpark and can be used from pure Python code.
kiminh/Quest
[ICML 2024] Quest: Query-Aware Sparsity for Efficient Long-Context LLM Inference
kiminh/RAG-Retrieval
Unify Efficient Fine-tuning of RAG Retrieval, including Embedding, ColBERT,Cross Encoder
kiminh/realbook
Easier audio-based machine learning with TensorFlow.
kiminh/RecFound
kiminh/recsys-examples
Examples for Recommenders - easy to train and deploy on accelerated infrastructure.
kiminh/source_code_reading
kiminh/stdexec
`std::execution`, the proposed C++ framework for asynchronous and parallel programming.
kiminh/TorchEasyRec
An easy-to-use framework for large scale recommendation algorithms.
kiminh/U-MARVEL