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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/stanfordmlgroup/chexpert-labeler

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Structured dataset documentation: a datasheet for CheXpert

https://github.com/star-ailab/armor

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Adversarially Robust Learning with Optimal Transport Regularized Divergences

https://github.com/stefan-ainetter/SCANnotatepp

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Leveraging Automatic CAD Annotations for Supervised Learning in 3D Scene Understanding

https://github.com/stefan-m-lenz/JuliaConnectoR

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The JuliaConnectoR: a functionally oriented interface for integrating Julia in R

https://github.com/stefaniafresca/DL-ROM

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Deep learning-based reduced order models in cardiac electrophysiology

https://github.com/stefanradev93/BayesFlow

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Amortized Bayesian model comparison with evidential deep learning

https://github.com/stefbraun/rnn_benchmarks

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LSTM Benchmarks for Deep Learning Frameworks

https://github.com/steffenczolbe/neuralizer

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Neuralizer: General Neuroimage Analysis without Re-Training

https://github.com/stemylonas/DeepSurf

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DeepSurf: A surface-based deep learning approach for the prediction of ligand binding sites on proteins

https://github.com/steve-zeyu-zhang/DiabetesDiagnosis

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DiabetesNet: A Deep Learning Approach to Diabetes Diagnosis

https://github.com/sttdanonymous/sttd

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Uncertainty Quantification via Spatial-Temporal Tweedie Model for Zero-inflated and Long-tail Travel Demand Prediction

https://github.com/sttich/dl-recommendation

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Deep Learning for Sequential Recommendation: Algorithms, Influential Factors, and Evaluations

https://github.com/subbareddy248/awesome-brain-encoding--decoding

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Deep Neural Networks and Brain Alignment: Brain Encoding and Decoding (Survey)

https://github.com/subrotonpi/clone_evaluation

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On the Use of Deep Learning Models for Semantic Clone Detection

https://github.com/suli24/edl-stormcast

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Evidential Deep Learning for Probabilistic Modelling of Extreme Storm Events

https://github.com/sumedhvdatar/PortraitSegmentation

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Portrait Segmentation Using Deep Learning

https://github.com/suminhan/traffic-uagcrntf

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Enhancing Spatiotemporal Traffic Prediction through Urban Human Activity Analysis

https://github.com/sunkairan/MapReduce-Based-Deep-Learning

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Large-scale Artificial Neural Network: MapReduce-based Deep Learning

https://github.com/survival-org/dl4survival

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Deep Learning for Survival Analysis: A Review

https://github.com/susanwe/RLCT

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Deep Learning is Singular, and That's Good

https://github.com/svjlucas/mangroveai

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A Deep Learning-Based Approach for Mangrove Monitoring

https://github.com/swag-kaust/storseismic

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StorSeismic: A new paradigm in deep learning for seismic processing

https://github.com/swatipadhee/Crisis-Aid-Terms

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Clustering of Social Media Messages for Humanitarian Aid Response during Crisis

https://github.com/sway-am/interpretability-of-cnn-during-pv-classification

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Machine learning approaches for automatic defect detection in photovoltaic systems

https://github.com/sweta20/Detecting-Cyberbullying-Across-SMPs

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Deep Learning for Detecting Cyberbullying Across Multiple Social Media Platforms

https://github.com/swhan0329/panorama_image_inpainting

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PIINET: A 360-degree Panoramic Image Inpainting Network Using a Cube Map

https://github.com/swing-research/cryolithe

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End-to-end localized deep learning for Cryo-ET

https://github.com/sydney-machine-learning/BayesianAutoencoders

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Revisiting Bayesian Autoencoders with MCMC

https://github.com/sydney-machine-learning/BayesianGraphNeuralNetworks

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Bayesian graph convolutional neural networks via tempered MCMC

https://github.com/sydney-machine-learning/bayesianneuralnetworks-mcmc-tutorial

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Bayesian neural networks via MCMC: a Python-based tutorial

https://github.com/sydney-machine-learning/deeplearning-crypto

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Review of deep learning models for crypto price prediction: implementation and evaluation

https://github.com/sydney-machine-learning/deeplearning-decadalworldeconomy

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Recursive deep learning framework for forecasting the decadal world economic outlook

https://github.com/sydney-machine-learning/deeplearning_timeseries

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Evaluation of deep learning models for multi-step ahead time series prediction

https://github.com/sydney-machine-learning/quantiledeeplearning

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Quantile deep learning models for multi-step ahead time series prediction

https://github.com/sydney-machine-learning/sentimentanalysis_bhagavadgita

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Semantic and sentiment analysis of selected Bhagavad Gita translations using BERT-based language framework

https://github.com/syednabilashraf/SynthEnsemble

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SynthEnsemble: A Fusion of CNN, Vision Transformer, and Hybrid Models for Multi-Label Chest X-Ray Classification

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