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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/Kodiologist/Citematic

A Probabilistic Theory of Deep Learning

https://github.com/Koukyosyumei/AIJack

Deep Learning with Differential Privacy

https://github.com/LIBOL/ODL

Online Deep Learning: Learning Deep Neural Networks on the Fly

https://github.com/LIVIAETS/boundary-loss

Deep learning approach to left ventricular non-compaction measurement

https://github.com/LIVIAETS/surface-loss

Deep learning approach to left ventricular non-compaction measurement

https://github.com/LIX-shape-analysis/SURFMNet

Unsupervised Deep Learning for Structured Shape Matching

https://github.com/LPMP/LPMP

Deep Graph Matching via Blackbox Differentiation of Combinatorial Solvers

https://github.com/LRacoci/permutation-graphml

Relational inductive biases, deep learning, and graph networks

https://github.com/Labaien96/InceptionTime-State-of-the-art-Time-Series-Classification

InceptionTime: Finding AlexNet for Time Series Classification

https://github.com/Lackel/DNA

HDLTex: Hierarchical Deep Learning for Text Classification

https://github.com/Ldpe2G/PCANet

PCANet: A Simple Deep Learning Baseline for Image Classification?

https://github.com/LebronGG/PointCnn

ABC: A Big CAD Model Dataset For Geometric Deep Learning

https://github.com/LeeHyeJin91/Wide_and_Deep

Wide & Deep Learning for Recommender Systems

https://github.com/LevidRodriguez/Machine-Learning-Books-and-more

Notes on Deep Learning for NLP

https://github.com/Li-fAngyU/DSIN_paddle

Deep Session Interest Network for Click-Through Rate Prediction

https://github.com/Liang-yc/CalorieEstimation

Deep Learning-Based Food Calorie Estimation Method in Dietary Assessment

https://github.com/LionQYW/Dien----Deep-Interest-Evolution-Network-for-Click-Through-Rate-Prediction

Deep Interest Evolution Network for Click-Through Rate Prediction

https://github.com/Liu-1994/DL_based_RSC

Over the Air Deep Learning Based Radio Signal Classification

https://github.com/Liuhongzhi2018/Medical-Images

Medical Image Segmentation Using Deep Learning: A Survey

https://github.com/Liuhongzhi2018/Medical_images_research

Medical Image Segmentation Using Deep Learning: A Survey

https://github.com/LouisFoucard/w-net

Xception: Deep Learning with Depthwise Separable Convolutions

https://github.com/LucaHermes/graph-unet-traffic-prediction

Relational inductive biases, deep learning, and graph networks

https://github.com/LucasFidon/GeneralizedWassersteinDiceLoss

Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations

https://github.com/Lukeasargen/targetClassifier

Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics

https://github.com/LunNova/ScionLight-reimpl

Training Deep Learning Models with Norm-Constrained LMOs

https://github.com/Lxrd-AJ/Advanced_ML

A Review on Deep Learning Techniques Applied to Semantic Segmentation

https://github.com/M-Lampert/DyGLib

Temporal Graph Networks for Deep Learning on Dynamic Graphs

https://github.com/M3DV/SimTA

MIA-Prognosis: A Deep Learning Framework to Predict Therapy Response

https://github.com/MECLabTUDA/M3d-Cam

GaNDLF: A Generally Nuanced Deep Learning Framework for Scalable End-to-End Clinical Workflows in Medical Imaging

https://github.com/MGH-LMIC/windows_optimization

Practical Window Setting Optimization for Medical Image Deep Learning

https://github.com/MICLab-Unicamp/e2dhipseg

Hippocampus Segmentation on Epilepsy and Alzheimer's Disease Studies with Multiple Convolutional Neural Networks

https://github.com/Manuscrit/SelectiveBackPropagation

Accelerating Deep Learning by Focusing on the Biggest Losers

https://github.com/MarcRoigVilamala/DeepProbCEP

DeepProbLog: Neural Probabilistic Logic Programming

https://github.com/MarcoForte/DeepInteractiveSegmentation

Getting to 99% Accuracy in Interactive Segmentation

https://github.com/MarcoForte/FBA-Matting

Getting to 99% Accuracy in Interactive Segmentation

https://github.com/MaxHalford/creme

Green AI

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