furyton Goto Github PK

arxiv-feed

arXiv daily update

awesome-generative-information-retrieval

awesome-llm4rs-papers

Large Language Model-enhanced Recommender System Papers

awesome-transformers-lm-analytics

This paper list focuses on the theoretical and empirical analysis of language models, especially large language models (LLMs). The papers in this list investigate the learning behavior, generalization ability, and other properties of language models through theoretical analysis, empirical analysis, or a combination of both.

booklet-style-pdf-reordering-python-script

a very simple Python script for reordering pdf files in booklet style

cg-lab

CG course experiment at SDU. Just a MinGW is enough. I install it using MSYS2.

chatroom

chat room resembled QQ

chinese-character-recognition

This is the project for the course FML this summer.

csrec

furymark

really stupid markdown editor

furyton.github.io

personal blog

gr-as-mvdr

[SIGIR'24] Generative Retrieval as Multi-Vector Dense Retrieval

hexo-theme-cards

Another Simple & Swift theme for Hexo

huggingface-pretrained-model-downloader

download the pretrained model published on huggingface.co. just a small script download file through Tsinghua mirrors(mirrors.tuna.tsinghua.edu.cn/hugging-face-models).

mentornet_pytorch

misc-feed

miscellaneous feed source, RSS shown in gh-page

my-little-notes

This is actually my simple github page using Hakyll :\

old-blog

pl0-compiler

This is a PL0 compiler written mainly in C. A project for Principle of Compiler course in SDU.

recommender-baseline-model

common used Recommend Baseline Model, including the traditional statistical model, and the Nerual Network Model. Focus on the SRS (Sequential Recommend System).

sgl

simple-dl-training-framework

statistical-learning-method

theory-of-computation-notes

an extended note of theory of computation, currently just include CFG part.

vimcode

Make VIM look and act like VSCode, hence vimcode.

webpage

myWebPage4Homework

xv6-lab

implementation of xv6 labs from MIT 6.S081 2020