https://github.com/mdozmorov/scRNA-seq_notes
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
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/mdsufz/PredicTF
https://github.com/medford-group/bdqm-vip
A Universal Framework for Featurization of Atomistic Systems
https://github.com/meerkatai/Yolo-v4-Object-Detection-Classification
https://github.com/megagonlabs/ruler
https://github.com/mehdidc/pylearn2
https://github.com/meier-johannes94/explainableaiimagemeasures
IROF: a low resource evaluation metric for explanation methods
https://github.com/melikaemami/Generalization-Error-of-GLMs
Generalization Error of Generalized Linear Models in High Dimensions
https://github.com/melsophos/cicy
Machine learning for complete intersection Calabi-Yau manifolds: a methodological study
https://github.com/mepx/mep-basic-src
New Evolutionary Computation Models and their Applications to Machine Learning
https://github.com/mevers/animated_dimensionality_reduction
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
https://github.com/meyerv11045/Self-Driving-RC-Car
https://github.com/mi2-warsaw/MI2DataLab_Seminarium
Tunability: Importance of Hyperparameters of Machine Learning Algorithms
https://github.com/michaelaerni/interpolation_robustness
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
https://github.com/michaildoukas/head2head
https://github.com/michellbrito/CSCE_614_Term_Project
https://github.com/michelle0924hhx/DiffPrivate_FedLearning
https://github.com/michhar/azureml-keras-yolov3-custom
https://github.com/microljy/graph_level_drug_discovery
https://github.com/microsoft/ADBench
https://github.com/microsoft/CBM
Cyclic Boosting -- an explainable supervised machine learning algorithm
https://github.com/microsoft/FLAML
https://github.com/microsoft/Icebreaker
Icebreaker: Element-wise Active Information Acquisition with Bayesian Deep Latent Gaussian Model
https://github.com/microsoft/msccl
https://github.com/microsoft/otdd
https://github.com/microsoft/promptbase
InterpretML: A Unified Framework for Machine Learning Interpretability
https://github.com/microsoft/qlib/tree/main/examples/benchmarks/DoubleEnsemble
https://github.com/microsoft/unadversarial
https://github.com/miguelard/cloth_point_cloud_segmentation
https://github.com/mihaioltean/evolve-tsp-heuristics
New Evolutionary Computation Models and their Applications to Machine Learning
https://github.com/mikel-brostrom/Yolov5_DeepSort_Pytorch
https://github.com/mila-iqia/blocks
https://github.com/mila-iqia/fuel
https://github.com/mila-udem/blocks
https://github.com/mila-udem/fuel
https://github.com/mindspore-ai/models/tree/master/official/cv/yolov4
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.