4278 lines
103 KiB
JSON
4278 lines
103 KiB
JSON
{
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"scenario": "S2_research_knowledge_graph",
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"keywords": [
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"深度学习"
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],
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"nodes": [
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{
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"id": "repo:dongeliu/dlvc",
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"type": "repo",
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"props": {
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"language": "C++",
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"stars": 0,
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"description": "一种视频编解码原型系统，内嵌深度学习编码工具显著提高压缩效率。",
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"readme_head": "This software package is the reference software for Deep Learning-Based Video Coding (DLVC). The reference software includes both encoder and decoder functionality.\n\nReference software is useful in ai"
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}
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},
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{
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"id": "repo:co63oc/mcTVM",
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"props": {
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"language": "Python",
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"description": "mcTVM是MetaX-MACA生态下的开源深度学习编译框架项目，基于Apache TVM v0.18.0版本进行扩展开发，新增对沐曦（MetaX）GPU的专属支持，打通沐曦GPU与TVM框架的适配通道，实现深度学习模型在沐曦GPU上的高效编译、优化与部署。mcTVM助力完善沐曦GPU的软件生态，为开发者提供便捷、高效的深度学习模型部署解决方案，适用于人工智能、异构计算等相关领域的研发与应用场景。",
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"readme_head": ""
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}
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},
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{
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"id": "repo:opensci/pDeep",
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"type": "repo",
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"label": "opensci/pDeep",
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"props": {
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"language": "Python",
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"stars": 0,
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"description": "pDeep是一种基于深度学习的质谱预测系统。",
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"readme_head": "# pDeep\nPredicting MS/MS Spectra of Peptides with Deep Learning\n\nPlease visit https://github.com/pFindStudio/pDeep/tree/master/pDeep2 for the improved version of pDeep --- pDeep2.\n"
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}
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},
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{
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"id": "repo:Edgedev/Edge-Computing-Engine",
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"type": "repo",
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"label": "Edgedev/Edge-Computing-Engine",
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"props": {
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"language": "C++",
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"stars": 4,
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"forks": 1,
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"description": "Edge : 一个开源的科学计算引擎",
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"readme_head": "\n\n<div align=center><img src=\"https://github.com/AllenZYJ/Edge-Computing-Engine/blob/debian-gcc10-test/picture/01.svg\"/></div>\n# Edge-Engine\n\n\n\n## Edge : 一个开源的科学计算引擎\n\n[README for English_version](./RE"
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}
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},
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{
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"id": "repo:clj111/Edge-Computing-Engine",
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"type": "repo",
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"label": "clj111/Edge-Computing-Engine",
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"props": {
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"language": "C++",
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"stars": 0,
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"forks": 0,
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"description": "Edge : 一个开源的科学计算引擎",
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"readme_head": "#### 从命令行创建一个新的仓库\nnew line\n\n```bash\ntouch README.md\ngit init\ngit add README.md\ngit commit -m \"first commit\"\ngit remote add origin https://git.trustie.net/Edge/Edge-Computing-Engine.git\ngit push -u ori"
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}
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},
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{
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"id": "repo:AIEven/AIkun",
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"type": "repo",
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"label": "AIEven/AIkun",
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"props": {
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"language": "Python",
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"stars": 0,
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"forks": 0,
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"description": "",
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"readme_head": "# Frequency-domain-deep-learning\n## 频域深度学习入门教程\n\n## 概述\n\n这是一个用于在频域进行深度学习的入门教程，看完这个文档后，希望读者能够理解和掌握频域深度学习的核心概念、技术和应用。\n\n## 📖 适用人群\n\n- **初学者**: 对频域处理感兴趣的深度学习爱好者\n- **研究人员**: 希望将频域方法应用到研究中的学者\n- **工程师**: 需要在实际项"
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}
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"language": "Python3.6",
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"stars": 0,
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"description": "",
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"readme_head": "# aa\n\n"
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}
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},
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{
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"id": "repo:njuiselab/Gandalf",
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"type": "repo",
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"label": "njuiselab/Gandalf",
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"props": {
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"language": "Python",
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"stars": 0,
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"description": "",
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"readme_head": "# Gandalf\nSome codes and results about our paper, here is the structure of our directory:\n\n- **/src:** High level interface of out implementation of model generator across frameworks by JSON\n\n - conf"
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}
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},
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{
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"id": "repo:CocytusXRS/AI_test",
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"type": "repo",
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"label": "CocytusXRS/AI_test",
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"props": {
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"language": "Jupyter notebook",
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"stars": 0,
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"description": "",
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"readme_head": "\n\n## 自动化测试 —— 基于等价融合算子的深度学习框架差分测试技术 \n\n\n\n| 姓名 | 学号 |\n| ------ | --------- |\n| 徐润石 | 201250167 |\n| 于欣博 | 201250165 |\n| 刘云辉 | 201250166 |\n| 桂金鑫 | 201850107 |\n\n\n\n### 1. 深度学习框架选择 —— PyTorch\n\n#### "
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}
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},
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{
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"id": "repo:ly15927029790/CodeRecommendation",
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"type": "repo",
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"label": "ly15927029790/CodeRecommendation",
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"props": {
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"language": "Java",
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"stars": 0,
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"forks": 0,
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"description": "代码生成式补全工具",
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"readme_head": "## 项目概述:\n\n基于上下文分析和深度学习的代码生成式补全工具DeepAPIRec是一款由复旦大学软件工程实验室CodeWisdom团队推出的基于代码上下文和深度学习的智能化API代码推荐工具。DeepAPIRec考虑代码上下文中的API使用代码及其结构信息，并通过结合Child-Sum Tree-LSTM网络和N-ary LSTM网络作为API推荐的深度学习模型。此外，DeepAPIRec构建"
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}
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},
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{
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"id": "repo:Hua135/sam-optimizers",
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"type": "repo",
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"label": "Hua135/sam-optimizers",
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"props": {
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"language": "Python",
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"stars": 0,
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"forks": 0,
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"description": "深度神经网络的泛化能力是机器学习领域的核心问题之一。传统优化算法如随机梯度下降（SGD）和Adam仅最小化训练损失值，容易导致模型收敛到尖锐的最小值点，从而影响泛化性能。近年来，锐度感知最小化（Sharpness-Aware Minimization, SAM）通过同时最小化损失值和损失锐度，有效提升了模型的泛化能力，但其计算开销约为传统优化器的两倍。本文系统研究了SAM及其两种高效变体——ESA",
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"readme_head": "# sam-optimizers\n# 新型优化算法的实现与分析 - SAM系列优化器\n\n本项目实现了多种基于锐度感知最小化（Sharpness-Aware Minimization, SAM）的优化算法，并在CIFAR-10/100数据集上进行对比实验。\n\n## 实验环境\n\n### 硬件要求\n- AMD GPU (支持ROCm) 或 NVIDIA GPU (支持CUDA)\n- 至少8GB显存（推荐"
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}
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},
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{
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"id": "repo:ashh/jittor-chanshiguan-track2-straightpcf-denoise",
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"type": "repo",
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"label": "ashh/jittor-chanshiguan-track2-straightpcf-denoise",
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"props": {
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"language": "Python",
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"stars": 0,
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"forks": 0,
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"description": "赛道二三维点云降噪项目，基于 StraightPCF 复现并改进三阶段耦合速度场方法：单速度预训练、双速度耦合训练、距离缩放模块精调。采用 patch 级迭代推理与稳健重建，保证输入输出点数一致。最佳提交成绩：72.63（CD 60.31 / P2S 84.96）。",
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"readme_head": "# 赛道二点云降噪 —— 基于 StraightPCF 的三阶段耦合速度场降噪\n\n**赛道**：赛道二：基于深度学习的三维点云降噪任务 \n**团队名**：铲屎官\n\n本仓库为「计图（Jittor）点云降噪赛题」的参赛代码。任务是给定从三维物体表面采样并加入噪声的点云，预测每个点的位移向量，将含噪点「推回」真实物体表面附近，输出与输入点数严格一致的降噪点云。最终成绩由 Chamfer Distanc"
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}
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},
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{
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"id": "repo:shenyan/machineLearningTemplate",
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"type": "repo",
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"label": "shenyan/machineLearningTemplate",
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"props": {
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"language": "Python",
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"stars": 1,
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"description": "基于pytorch lightning的机器学习模板, 用于对机器学习算法进行训练, 验证, 测试等, 目前实现了神经网路, 深度学习, k折交叉, 自动保存训练信息等. ",
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"readme_head": ""
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