Pinned Repositories
AMEVulDetector
Smart Contract Vulnerability Detection From Pure Neural Network to Interpretable Graph Feature and Expert Pattern Fusion (IJCAI-21 Accepted)
ge-sc
MANDO is a new heterogeneous graph representation to learn the heterogeneous contract graphs' structures to accurately detect vulnerabilities in smart contract source code at both coarse-grained contract-level and fine-grained line-level.
GNNSCVulDetector
Smart Contract Vulnerability Detection Using Graph Neural Networks (IJCAI-20 Accepted)
GraphNeuralNetwork
《深入浅出图神经网络：GNN原理解析》配套代码
heyCoke
Config files for my GitHub profile.
nlp-tutorial
Natural Language Processing Tutorial for Deep Learning Researchers
text-classification-surveys
文本分类资源汇总，包括深度学习文本分类模型，如SpanBERT、ALBERT、RoBerta、Xlnet、MT-DNN、BERT、TextGCN、MGAN、TextCapsule、SGNN、SGM、LEAM、ULMFiT、DGCNN、ELMo、RAM、DeepMoji、IAN、DPCNN、TopicRNN、LSTMN 、Multi-Task、HAN、CharCNN、Tree-LSTM、DAN、TextRCNN、Paragraph-Vec、TextCNN、DCNN、RNTN、MV-RNN、RAE等，浅层学习模型，如LightGBM 、SVM、XGboost、Random Forest、C4.5、CART、KNN、NB、HMM等。介绍文本分类数据集，如MR、SST、MPQA、IMDB、Yelp、20NG、AG、R8、DBpedia、Ohsumed、SQuAD、SNLI、MNLI、MSRP、MRDA、RCV1、AAPD，评价指标，如accuracy、Precision、Recall、F1、EM、MRR、HL、Micro-F1、Macro-F1、P@K，和技术挑战，包括多标签文本分类。
TextCNN
Convolutional Neural Networks for Sentence Classification in PyTorch
heyCoke's Repositories
heyCoke/AMEVulDetector
Smart Contract Vulnerability Detection From Pure Neural Network to Interpretable Graph Feature and Expert Pattern Fusion (IJCAI-21 Accepted)
heyCoke/ge-sc
MANDO is a new heterogeneous graph representation to learn the heterogeneous contract graphs' structures to accurately detect vulnerabilities in smart contract source code at both coarse-grained contract-level and fine-grained line-level.
heyCoke/GNNSCVulDetector
Smart Contract Vulnerability Detection Using Graph Neural Networks (IJCAI-20 Accepted)
heyCoke/GraphNeuralNetwork
《深入浅出图神经网络：GNN原理解析》配套代码
heyCoke/heyCoke
Config files for my GitHub profile.
heyCoke/nlp-tutorial
Natural Language Processing Tutorial for Deep Learning Researchers
heyCoke/text-classification-surveys
文本分类资源汇总，包括深度学习文本分类模型，如SpanBERT、ALBERT、RoBerta、Xlnet、MT-DNN、BERT、TextGCN、MGAN、TextCapsule、SGNN、SGM、LEAM、ULMFiT、DGCNN、ELMo、RAM、DeepMoji、IAN、DPCNN、TopicRNN、LSTMN 、Multi-Task、HAN、CharCNN、Tree-LSTM、DAN、TextRCNN、Paragraph-Vec、TextCNN、DCNN、RNTN、MV-RNN、RAE等，浅层学习模型，如LightGBM 、SVM、XGboost、Random Forest、C4.5、CART、KNN、NB、HMM等。介绍文本分类数据集，如MR、SST、MPQA、IMDB、Yelp、20NG、AG、R8、DBpedia、Ohsumed、SQuAD、SNLI、MNLI、MSRP、MRDA、RCV1、AAPD，评价指标，如accuracy、Precision、Recall、F1、EM、MRR、HL、Micro-F1、Macro-F1、P@K，和技术挑战，包括多标签文本分类。
heyCoke/TextCNN
Convolutional Neural Networks for Sentence Classification in PyTorch