https://github.com/mingyuyng/OFDM-guided-JSCC
Deep Joint Source Channel Coding for WirelessImage Transmission with OFDM
Papers with Code 아카이브
BIG-bench Machine Learning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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This branch include most common machine learning fundamental algorithms.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/minhcong0891/wheat-detection
https://github.com/mini-pw/2020L-WarsztatyBadawcze-InzynieriaCech
https://github.com/minzhang-1/PointHop
PointHop: An Explainable Machine Learning Method for Point Cloud Classification
https://github.com/minzhang-1/PointHop-PointHop2_Spark
PointHop: An Explainable Machine Learning Method for Point Cloud Classification
https://github.com/minzhang-1/PointHop2
PointHop: An Explainable Machine Learning Method for Point Cloud Classification
https://github.com/mirerfangheibi/Deep-Learning-Resources
https://github.com/mishig25/adversarial-reprogramming-keras
https://github.com/mit-han-lab/mcunet
https://github.com/mitmath/18S096SciML
Universal Differential Equations for Scientific Machine Learning
https://github.com/mitmedialab/vizml
VizML: A Machine Learning Approach to Visualization Recommendation
https://github.com/mkorshe/awesome_awesome_chemoinformatics
https://github.com/mkusner/counterfactual-fairness
https://github.com/ml-jku/TPDistance
https://github.com/ml-research/a-typology-for-exploring-the-mitigation-of-shortcut-behavior
A Typology for Exploring the Mitigation of Shortcut Behavior
https://github.com/ml-research/do-not-trust-prediction-scores-for-membership-inference-attacks
Membership Inference Attacks against Machine Learning Models
https://github.com/ml-research/to-trust-or-not-to-trust-prediction-scores-for-membership-inference-attacks
Membership Inference Attacks against Machine Learning Models
https://github.com/mlbazaar/cardea
Cardea: An Open Automated Machine Learning Framework for Electronic Health Records
https://github.com/mlc-ai/web-llm
TensorIR: An Abstraction for Automatic Tensorized Program Optimization
https://github.com/mlcommons/training
https://github.com/mli-lab/sample_complexity_ss_recon
https://github.com/mli-lab/scaling_laws_for_deep_learning_based_image_reconstruction
https://github.com/mlpack/ensmallen
ensmallen: a flexible C++ library for efficient function optimization
https://github.com/mlpack/mlpack
Designing and building the mlpack open-source machine learning library
https://github.com/mlr-org/mlr
OpenML: An R Package to Connect to the Machine Learning Platform OpenML
https://github.com/model-card/model-card
https://github.com/mohsenshahhosseini/GEM-ITH-Ensemble
Optimizing Ensemble Weights and Hyperparameters of Machine Learning Models for Regression Problems
https://github.com/monte-flora/scikit-explain
Quantifying Model Complexity via Functional Decomposition for Better Post-Hoc Interpretability
https://github.com/morganmcg1/ImageNette_ImageWoof_ImageWang
ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning
https://github.com/mosmos/face_key_point_detection
Facial Key Points Detection using Deep Convolutional Neural Network - NaimishNet
https://github.com/mrklees/deepconv-chess
https://github.com/mrsergazinov/particle-force-cnn
Machine learning approach to force reconstruction in photoelastic materials
https://github.com/mrtzh/whynot
https://github.com/mshunshin/SegNetCMR
https://github.com/mtorabirad/PricePrediction
https://github.com/murilo-goncalves/ML-Fairness
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.