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Hybrid Machine Learning

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코드

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

https://github.com/KimGroup/QGasML

공식 코드

Machine learning discovery of new phases in programmable quantum simulator snapshots

https://github.com/OneClickDeepLearning/classificationOfResidentialRequests

공식 코드

Hybrid Machine Learning Models of Classifying Residential Requests for Smart Dispatching

https://github.com/UCL-CCS/ML-PB-Covid-drug

공식 코드

Pandemic Drugs at Pandemic Speed: Infrastructure for Accelerating COVID-19 Drug Discovery with Hybrid Machine Learning- and Physics-based Simulations on High Performance Computers

https://github.com/Zheng-Meng/Dynamics-Reconstruction-ML

공식 코드

Reconstructing dynamics from sparse observations with no training on target system

https://github.com/andresmolares/hybrid-ml-management-hab-impact

공식 코드

Hybrid Machine Learning techniques in the management of harmful algal blooms impact

https://github.com/anhphuong-ngo/Quantum_neural_network_Regression_Battery_Capacity_Degradation

공식 코드

A Quantum Neural Network Regression for Modeling Lithium-ion Battery Capacity Degradation

https://github.com/bilepeng/risnet_partial_csi

공식 코드

RISnet: A Scalable Approach for Reconfigurable Intelligent Surface Optimization with Partial CSI

https://github.com/dtrizna/quo.vadis

공식 코드

Quo Vadis: Hybrid Machine Learning Meta-Model based on Contextual and Behavioral Malware Representations

https://github.com/ephrem-admasu/windspeed_prediction

공식 코드

Short-term Wind Speed Forecasting for Power Integration in Smart Grids based on Hybrid LSSVM-SVMD Method

https://github.com/huckiyang/EyeNet

공식 코드

A Novel Hybrid Machine Learning Model for Auto-Classification of Retinal Diseases

https://github.com/lewis-od/Quantum-Optics

공식 코드

A hybrid machine-learning algorithm for designing quantum experiments

https://github.com/mahyaqorbani/accident-impact-prediction-using-deep-convolutional-and-recurrent-neural-networks

공식 코드

Accident Impact Prediction based on a deep convolutional and recurrent neural network model

https://github.com/matteochieregato/GradientboostingCovid19

공식 코드

A hybrid machine learning/deep learning COVID-19 severity predictive model from CT images and clinical data

https://github.com/megagonlabs/sato

공식 코드

Sato: Contextual Semantic Type Detection in Tables

https://github.com/nahshonmokua/LoRaWAN-IndoorPathLossModeling-ML/blob/main/MLR%20(ANOVA%20%26%20SHADOW%20ANALYSIS).ipynb

공식 코드

A Statistical Evaluation of Indoor LoRaWAN Environment-Aware Propagation for 6G: MLR, ANOVA, and Residual Distribution Analysis

https://github.com/philippaltmann/sequent

공식 코드

SEQUENT: Towards Traceable Quantum Machine Learning using Sequential Quantum Enhanced Training

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

공식 코드

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

https://github.com/sinatayebati/cladnet-ml-for-am

공식 코드

A hybrid machine learning framework for clad characteristics prediction in metal additive manufacturing

https://github.com/weichseltree/euro2020

Hybrid Machine Learning Forecasts for the UEFA EURO 2020

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