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Deep Learning
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/zlin7/lvd
공식 코드Locally Valid and Discriminative Prediction Intervals for Deep Learning Models
https://github.com/zsdonghao/tensorlayer
공식 코드TensorLayer: A Versatile Library for Efficient Deep Learning Development
https://github.com/zuowx/deeplink-t
공식 코드DeepLINK-T: deep learning inference for time series data using knockoffs and LSTM
https://github.com/zxj32/GCN-GRU
공식 코드Deep Learning for Predicting Dynamic Uncertain Opinions in Network Data
https://github.com/zzb-nice/doa_est_master
공식 코드https://github.com/zzzace2000/nodegam
공식 코드NODE-GAM: Neural Generalized Additive Model for Interpretable Deep Learning
https://gitlab.com/CBDS/DeepSolaris
공식 코드https://gitlab.com/Kariryaa/maskit
공식 코드https://gitlab.com/NERSC/roofline-on-nvidia-gpus
공식 코드Hierarchical Roofline Performance Analysis for Deep Learning Applications
https://gitlab.com/apozas/bayesian-dl-quantum
공식 코드https://gitlab.com/blibli/fetdtialign
공식 코드FetDTIAlign: A Deep Learning Framework for Affine and Deformable Registration of Fetal Brain dMRI
https://gitlab.com/dlse-workshop/dlse-workshop-community-report
공식 코드Deep Learning & Software Engineering: State of Research and Future Directions
https://gitlab.com/hanfengzhai2/gnn-fem-polyplas
공식 코드Stress Predictions in Polycrystal Plasticity using Graph Neural Networks with Subgraph Training
https://gitlab.com/innerve/sperm-motility-analysis
공식 코드Predicting Semen Motility using three-dimensional Convolutional Neural Networks
https://gitlab.com/rwth-itsec/explainability-analyzed-dga-models
공식 코드https://huggingface.co/diabolic6045/indian_cities_image_classification
공식 코드https://huggingface.co/nguyenthanhasia/BERTLaw
공식 코드JNLP Team: Deep Learning for Legal Processing in COLIEE 2020
https://zenodo.org/record/4734223
공식 코드https://zenodo.org/record/zenodo.8206635
공식 코드Revisiting the Performance of Deep Learning-Based Vulnerability Detection on Realistic Datasets
https://bitbucket.org/problog/deepproblog
https://dagshub.com/ShaileshSridhar2403/Re-CDEP
Interpretations are useful: penalizing explanations to align neural networks with prior knowledge
https://git.rwth-aachen.de/bcs/projects/tp/multiclass-yeast-seg
Multiclass Yeast Segmentation in Microstructured Environments with Deep Learning
https://github.com/1iyc/T2T-Analysis
https://github.com/2023-MindSpore-1/ms-code-14/tree/main/Wide_and_Deep
https://github.com/2023-MindSpore-1/ms-code-14/tree/main/Wide_and_Deep_Multitable
https://github.com/2023-MindSpore-1/ms-code-205
Xception: Deep Learning with Depthwise Separable Convolutions
https://github.com/2023-MindSpore-1/ms-code-214/tree/main/fishnet99
https://github.com/2023-MindSpore-1/ms-code-214/tree/main/hed
HED-UNet: Combined Segmentation and Edge Detection for Monitoring the Antarctic Coastline
https://github.com/2023-MindSpore-1/ms-code-220/tree/main/dlrm
Deep Learning Recommendation Model for Personalization and Recommendation Systems
https://github.com/2023-MindSpore-1/ms-code-224/tree/main/AdderNGD
AdderNet: Do We Really Need Multiplications in Deep Learning?
https://github.com/2023-MindSpore-1/ms-code-64
Deep Learning for Person Re-identification: A Survey and Outlook
https://github.com/2023-MindSpore-1/ms-code-7/tree/main/vanillanet
https://github.com/2023-MindSpore-4/Code-5/tree/main/hed
HED-UNet: Combined Segmentation and Edge Detection for Monitoring the Antarctic Coastline
https://github.com/2023-MindSpore-4/Code10/tree/main/fishnet99
https://github.com/2023-MindSpore-4/Code12/tree/main/d2l/chapter_05_deep-learning-computation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.