网络视频
一、Transformer网络视频
[2] Transformer从零详细解读(可能是你见过最通俗易懂的讲解)
二、GNN网络视频
[2] GNN所有论文列表：GNN所有论文列表
三、CNN网络视频
四、NAS网络视频
[2] 斯坦福--神经网络结构搜索
[3] 张浩宇--神经网络结构搜索
五、神经网络与深度学习视频
六、拉格朗日-KKT-对偶问题-凸优化
[2] 王木头学科学--随机梯度下降、动量法、AdaGrad
七、深度学习+组合优化视频
[1] 国防科大-李凯文--深度学习+组合优化 ------ 视频用到的论文PDF
八、迁移学习视频
[1] 基于迁移学习的昂贵异构多目标优化(比勒费尔德大学王曦璐博士)
[2] 基于知识迁移的数据驱动进化优化(香港浸会大学杨翠娥博士)
九、多目标优化视频
[2] EMO Driven by GAN (Dr. Cheng He)
Github代码
一、论文代码搜索网址
[1] 论文代码搜索网址
二、多目标相关代码
[1] Guo Yu, Yaochu Jin, and Markus Olhofer. A multi-objective evolutionary algorithm for finding knee regions using two localized dominance relationships. IEEE Transactions on Evolutionary Computation, 25(1):145-158, 2021 代码
[2] PlatEMO 代码
三、深度学习+VRP-TSP相关代码
[1] Knowledge+VRP: Learning Generalizable Models for Vehicle Routing Problems via Knowledge Distillation代码
[2] NN+VRP: Efficient Neural Neighbourhood Search for Pickup and Delivery Problems 代码
[3] Transformer+VRP: Learning to Iteratively Solve Routing Problems with Dual-Aspect Collaborative Transformer 代码
[4] GNN+TSP： "Learning to Solve NP-Complete Problems -- A Graph Neural Network for the Decision TSP" by M. Prates, P. Avelar, H. Lemos, L. Lamb and M. Vardi, AAAI 2019 代码
[5] DL+TSP: NeuroLKH: Combining Deep Learning Model with Lin-Kernighan-Helsgaun Heuristic for Solving the Traveling Salesman Problem代码
四、深度学习+job shop, flow shop相关代码
[1] Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning 代码
[2] Flexible Job Shop Scheduling via Graph Neural Network and Deep Reinforcement Learning代码 论文
[3] Matrix Encoding Networks for Neural Combinatorial Optimization代码 论文
五、冷水机组优化相关代码
[1] Optimal Chiller Loading Based on Collaborative Neurodynamic Optimization 代码 论文
六、生物基因拷贝数变异相关代码
[1] Huang, T., Li Jun-qing*（李俊青，通信）, Jia, B., Sang, H. (2021). CNV-MEANN: A Neural Network and Mind Evolutionary Algorithm-Based Detection of Copy Number Variations From Next-Generation Sequencing Data. Front. Genet. 2021, 12: 700874. 代码 论文
[2] Xuan Wang, Li, Junqing(通讯作者), Tihao Huang, CNVABNN: An AdaBoost algorithm and neural networks-based detection of copy number variations from NGS data. Computational Biology and Chemistry, 2022, 99, 107720.代码 论文
[3] Abyzov A(通讯作者), Urban A E, Snyder M, et al. CNVnator: an approach to discover, genotype, and characterize typical and atypical CNVs from family and population genome sequencing. Genome research, 2011, 21(6): 974-984.代码 论文
[4] Yuan X(通讯作者), Yu J, Xi J, et al. CNV_IFTV: an isolation forest and total variation-based detection of CNVs from short-read sequencing data. IEEE/ACM transactions on computational biology and bioinformatics, 2019, 18(2): 539-549.代码 论文
[5] Boeva V(通讯作者), Popova T, Bleakley K, et al. Control-FREEC: a tool for assessing copy number and allelic content using next-generation sequencing data. Bioinformatics, 2012, 28(3): 423-425., 2019, 18(2): 539-549.代码 论文