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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/qmeng99/deepmesh

공식 코드

DeepMesh: Mesh-based Cardiac Motion Tracking using Deep Learning

https://github.com/quercushernandez/DeepLearningMOR

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Deep learning of thermodynamics-aware reduced-order models from data

https://github.com/quercushernandez/ThermodynamicsGNN

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Thermodynamics-informed graph neural networks

https://github.com/qxcv/asnets

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Action Schema Networks: Generalised Policies with Deep Learning

https://github.com/rabadiah/generalization-capabilities-and-robustness-of-hybrid-machine-learning-models

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Generalization capabilities and robustness of hybrid models grounded in physics compared to purely deep learning models

https://github.com/radi-cho/gatedtabtransformer

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The GatedTabTransformer. An enhanced deep learning architecture for tabular modeling

https://github.com/rafusan/dl-alzheimer

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Deep Learning for Early Alzheimer Disease Detection with MRI Scans

https://github.com/raghavian/redl

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Operating critical machine learning models in resource constrained regimes

https://github.com/rahulshah1397/ladder

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LADDER: Revisiting the Cosmic Distance Ladder with Deep Learning Approaches and Exploring its Applications

https://github.com/rajpurkarlab/chest-ct-pretraining

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Video Pretraining Advances 3D Deep Learning on Chest CT Tasks

https://github.com/raoyongming/GFNet

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SF2Former: Amyotrophic Lateral Sclerosis Identification From Multi-center MRI Data Using Spatial and Frequency Fusion Transformer

https://github.com/raseidi/cosmo

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CoSMo: a Framework to Instantiate Conditioned Process Simulation Models

https://github.com/raspstephan/CBRAIN-CAM

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Deep learning to represent sub-grid processes in climate models

https://github.com/ratschlab/GP-VAE

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GP-VAE: Deep Probabilistic Time Series Imputation

https://github.com/rattlesnakey/q-mckt

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A Question-centric Multi-experts Contrastive Learning Framework for Improving the Accuracy and Interpretability of Deep Sequential Knowledge Tracing Models

https://github.com/ravidziv/simplifyingimbalancedtraining

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Simplifying Neural Network Training Under Class Imbalance

https://github.com/ravisankaradepu/degenerate_saddle

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Are Saddles Good Enough for Deep Learning?

https://github.com/raymond-sci/AND

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Unsupervised Deep Learning by Neighbourhood Discovery

https://github.com/raywu0123/Brain-Tumor-Segmentation

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Deep Learning Based Segmentation of Various Brain Lesions for Radiosurgery

https://github.com/razeineldin/DeepSeg

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DeepSeg: Deep Neural Network Framework for Automatic Brain Tumor Segmentation using Magnetic Resonance FLAIR Images

https://github.com/rbischof/relative_balancing

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Multi-Objective Loss Balancing for Physics-Informed Deep Learning

https://github.com/rbturnbull/terrier

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Terrier: A Deep Learning Repeat Classifier

https://github.com/rce-fhv/loadforecasting

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Load Forecasting for Households and Energy Communities: Are Deep Learning Models Worth the Effort?

https://github.com/rdz98/poisonfeddlrs

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Poisoning Deep Learning Based Recommender Model in Federated Learning Scenarios

https://github.com/realcrane/human-trajectory-prediction-via-neural-social-physics

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Human Trajectory Prediction via Neural Social Physics

https://github.com/reconfigurable-ml-pipeline/ipa

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IPA: Inference Pipeline Adaptation to Achieve High Accuracy and Cost-Efficiency

https://github.com/reddragon/efficient-dl-survey-paper

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Efficient Deep Learning: A Survey on Making Deep Learning Models Smaller, Faster, and Better

https://github.com/redlynx96/deep-learning-based-analysis-of-basins-of-attraction

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Deep Learning-based Analysis of Basins of Attraction

https://github.com/reek129/QuantumAI_MultiClass_Sign_Recognition

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Quantum Computing Supported Adversarial Attack-Resilient Autonomous Vehicle Perception Module for Traffic Sign Classification

https://github.com/remicres/otbtf

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A framework for remote sensing images processing using deep learning technique

https://github.com/reml-lab/URSABench

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URSABench: Comprehensive Benchmarking of Approximate Bayesian Inference Methods for Deep Neural Networks

https://github.com/reml-lab/hetvae

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Heteroscedastic Temporal Variational Autoencoder For Irregular Time Series

https://github.com/reml-lab/mTAN

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Multi-Time Attention Networks for Irregularly Sampled Time Series

https://github.com/renyiryry/tnt_neurips_2021

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Tensor Normal Training for Deep Learning Models

https://github.com/rfchallenge/rfchallenge_singlechannel_starter

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RF Challenge: The Data-Driven Radio Frequency Signal Separation Challenge

https://github.com/riblidezso/frcnn_cad

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Detecting and classifying lesions in mammograms with Deep Learning

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