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Shape Representation Of 3D Point Clouds
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/hrbigelow/transformer-aiayn
https://github.com/hrisi/tree-species-classification
https://github.com/hubiodatalab/druggen
https://github.com/huggingface/transformers
https://github.com/hxdengBerkeley/PointCNN.Pytorch
https://github.com/hyunwookl/testam
https://github.com/hyunwoongko/transformer
https://github.com/hz-ants/pointnet.pytorch
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/iballester/SPiKE
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
https://github.com/icewing1996/bert_dep
https://github.com/ictnlp/DiverseNMT
https://github.com/ictnlp/NA-MNMT
https://github.com/ictnlp/PTE-NMT
https://github.com/ictnlp/TLAT-NMT
https://github.com/idiotprofessorchen/bert.github.io
https://github.com/iejMac/ScriptWriter
https://github.com/ikergarcia1996/Simultaneous-Machine-Translation
https://github.com/ikh-innovation/roboweldar-votenet
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
https://github.com/imanlab/deep_movement_primitives
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/insigh/THUMT
https://github.com/irmvlab/point-mamba
https://github.com/ishritam/Image-captioning-with-visual-attention
https://github.com/isl-org/Open3D-ML
KPConv: Flexible and Deformable Convolution for Point Clouds
https://github.com/ismav/sparseNMF
https://github.com/jadore801120/attention-is-all-you-need-pytorch
https://github.com/jageshmaharjan/BERT_Service
https://github.com/jaisenbe58r/NLP-Transformer_Translator
https://github.com/jamesdvance/predicting_clicks
https://github.com/jangjoongkeon/JK
https://github.com/jbdel/OMG_UMONS_submission
https://github.com/jean-kunz/ml_research_papers
https://github.com/jelifysh/Transformers
https://github.com/jeongchanyoung-1234/De-En-NMT
https://github.com/jgcbrouns/Introducing-Sparsity-in-the-Transformer
https://github.com/jiahuei/Self-Critical-SCST-TensorFlow
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.