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/ajfisch/few-shot-cp
Adaptive Risk Minimization: Learning to Adapt to Domain Shift
https://github.com/akinsezer26/PersonDetection_TelegramBot
https://github.com/akrlowicz/lung-cancer-tissue-classification
Lung and Colon Cancer Histopathological Image Dataset (LC25000)
https://github.com/akulkailwoo/zalando-fashion-mnist
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
https://github.com/alan-turing-institute/MLJ.jl
https://github.com/alan-turing-institute/SemAIDA
https://github.com/alan-turing-institute/signatures-psychiatry
A signature-based machine learning model for bipolar disorder and borderline personality disorder
https://github.com/alex-delavega/umap_jades_2025
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
https://github.com/alexanderbrenning/spdiag
Spatial machine-learning model diagnostics: a model-agnostic distance-based approach
https://github.com/alexeyignatiev/xplainer
https://github.com/alexrenz/adaps
NuPS: A Parameter Server for Machine Learning with Non-Uniform Parameter Access
https://github.com/algowatchpenn/GerryFair
An Empirical Study of Rich Subgroup Fairness for Machine Learning
https://github.com/ali-ramadhan/6S898-climate-parameterization
DiffEqFlux.jl - A Julia Library for Neural Differential Equations
https://github.com/ali-ramadhan/neural-differential-equation-climate-parameterizations
DiffEqFlux.jl - A Julia Library for Neural Differential Equations
https://github.com/alignmentresearch/learned-planner
Risks from Learned Optimization in Advanced Machine Learning Systems
https://github.com/all-umass/metric_learn
https://github.com/allenai/dolma
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
https://github.com/alshedivat/DeterminantalPointProcesses.jl
https://github.com/alstonlo/torch-influence
Second-Order Stochastic Optimization for Machine Learning in Linear Time
https://github.com/alvarorobledo/fyp-foolbox
Foolbox: A Python toolbox to benchmark the robustness of machine learning models
https://github.com/am-sirdaniel/Facial_Keypoint_Detection
Facial Key Points Detection using Deep Convolutional Neural Network - NaimishNet
https://github.com/amanjain252002/Stock-Price-Prediction
Stock Price Prediction Using Machine Learning and LSTM-Based Deep Learning Models
https://github.com/amazingcodeLYL/Traffic_signs_detection_darket
https://github.com/amiratag/InterpretationFragility
https://github.com/amish-mishra/cder-protein-classifier
Supervised Learning of Labeled Pointcloud Differences via Cover-Tree Entropy Reduction
https://github.com/amlas-tool/AMLAS-Tool-Visio-Add-In
Guidance on the Assurance of Machine Learning in Autonomous Systems (AMLAS)
https://github.com/amplab/cyclades
https://github.com/amyami187/effective_dimension
https://github.com/anahid1988/DeepRUL
Quantifying Interpretability and Trust in Machine Learning Systems
https://github.com/anasbadawy/Facial-Keypoint-Detection
Facial Key Points Detection using Deep Convolutional Neural Network - NaimishNet
https://github.com/anathan90/RADiSA
Optimization for Large-Scale Machine Learning with Distributed Features and Observations
https://github.com/ancastillar/A-high-bias-low-variance-introduction-to-Machine-Learning-for-physicists.-
A high-bias, low-variance introduction to Machine Learning for physicists
https://github.com/andre-juan/good_quantum_kernels
https://github.com/andresbecker/master_thesis
Local Explanation Methods for Deep Neural Networks Lack Sensitivity to Parameter Values
https://github.com/anhinga/fluid
Dataflow Matrix Machines as a Generalization of Recurrent Neural Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.