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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/luludak/deltann

공식 코드

DeltaNN: Assessing the Impact of Computational Environment Parameters on the Performance of Image Recognition Models

https://github.com/lululxvi/deepxde

공식 코드

DeepXDE: A deep learning library for solving differential equations

https://github.com/lupantech/dl4math

공식 코드

A Survey of Deep Learning for Mathematical Reasoning

https://github.com/lvxianwei/DeepMerge

공식 코드

DeepMerge: Deep-Learning-Based Region-Merging for Image Segmentation

https://github.com/lykaust15/DLBI

공식 코드

DLBI: Deep learning guided Bayesian inference for structure reconstruction of super-resolution fluorescence microscopy

https://github.com/lykaust15/Deep_learning_examples

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Deep learning in bioinformatics: introduction, application, and perspective in big data era

https://github.com/lykaust15/NN_decision_boundary

공식 코드

On the Decision Boundary of Deep Neural Networks

https://github.com/lykaust15/SupportNet

공식 코드

SupportNet: solving catastrophic forgetting in class incremental learning with support data

https://github.com/lzhangbv/acpsgd

공식 코드

Evaluation and Optimization of Gradient Compression for Distributed Deep Learning

https://github.com/lzhengchun/BraggNN

공식 코드

BraggNN: Fast X-ray Bragg Peak Analysis Using Deep Learning

https://github.com/m-3lab/awesome-industrial-anomaly-detection

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Deep Industrial Image Anomaly Detection: A Survey

https://github.com/m2dgithub/DL-Reg

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DL-Reg: A Deep Learning Regularization Technique using Linear Regression

https://github.com/m4nh/ars

공식 코드

Semi-Automatic Labeling for Deep Learning in Robotics

https://github.com/m4nh/loop

공식 코드

Effective Deployment of CNNs for 3DoF Pose Estimation and Grasping in Industrial Settings

https://github.com/mChataign/DupireNN

공식 코드

Deep Local Volatility

https://github.com/ma0ho/elpvpower

공식 코드

Deep Learning-based Pipeline for Module Power Prediction from EL Measurements

https://github.com/maabuu/posebusters

공식 코드

PoseBusters: AI-based docking methods fail to generate physically valid poses or generalise to novel sequences

https://github.com/mabdelhack/hyperspectral_image_generator

공식 코드

An Open-source Tool for Hyperspectral Image Augmentation in Tensorflow

https://github.com/machine-discovery/research

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Reducing the Long Tail Losses in Scientific Emulations with Active Learning

https://github.com/mad-stat/EGDL

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Epidemic-guided deep learning for spatiotemporal forecasting of Tuberculosis outbreak

https://github.com/maet3608/nuts-ml

공식 코드

nuts-flow/ml: data pre-processing for deep learning

https://github.com/maguirre1/deepLAI

공식 코드

A deep learning classifier for local ancestry inference

https://github.com/mahdifarvardin/medvse

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Efficient Deep Learning-based Estimation of the Vital Signs on Smartphones

https://github.com/mahdifarvardin/mtvital

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Efficient Deep Learning-based Estimation of the Vital Signs on Smartphones

https://github.com/mahdikherad/RS_Deep_graph

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Recommendation system using a deep learning and graph analysis approach

https://github.com/mahmoodlab/PORPOISE

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Pan-Cancer Integrative Histology-Genomic Analysis via Interpretable Multimodal Deep Learning

https://github.com/mahmouddaif/AraDIC

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AraDIC: Arabic Document Classification using Image-Based Character Embeddings and Class-Balanced Loss

https://github.com/majid9418/Breast-Tumor-Classification-Histopathological

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Breast Tumor Classification Using EfficientNet Deep Learning Model

https://github.com/makcedward/nlpaug

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A Survey of Deep Learning for Scientific Discovery

https://github.com/makrout/Deep-Learning-without-Weight-Transport

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Deep Learning without Weight Transport

https://github.com/maksym33/FeatureImportanceDL

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Feature Importance Ranking for Deep Learning

https://github.com/malekijoo/FedZipRevision

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FEDZIP: A Compression Framework for Communication-Efficient Federated Learning

https://github.com/malidib/Craters_MaskRCNN

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Automated crater shape retrieval using weakly-supervised deep learning

https://github.com/mangye16/ReID-Survey

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Deep Learning for Person Re-identification: A Survey and Outlook

https://github.com/manharsharma007/GI-Tract-Segmentation

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Automated GI tract segmentation using deep learning

https://github.com/manjunath-mahendra/NextConvGeN

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Convex space learning for tabular synthetic data generation

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.