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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/wi-pi/rethinking-image-scaling-attacks
공식 코드Rethinking Image-Scaling Attacks: The Interplay Between Vulnerabilities in Machine Learning Systems
https://github.com/wiebket/bt4vt
공식 코드https://github.com/wikistat/Fair-ML-4-Ethical-AI
공식 코드A survey of bias in Machine Learning through the prism of Statistical Parity for the Adult Data Set
https://github.com/williamblanzeisky/SBDG
공식 코드Introducing a Family of Synthetic Datasets for Research on Bias in Machine Learning
https://github.com/williamblanzeisky/SyntheticDataGeneration
공식 코드Introducing a Family of Synthetic Datasets for Research on Bias in Machine Learning
https://github.com/wilstrup/sr-small-data
공식 코드Symbolic regression outperforms other models for small data sets
https://github.com/windmark/static-gesture-recognition
공식 코드https://github.com/wisdom-moon/autostreamer
공식 코드https://github.com/wkirgsn/thermal-nn
공식 코드Thermal Neural Networks: Lumped-Parameter Thermal Modeling With State-Space Machine Learning
https://github.com/wm-bupt/oracle-mnist
공식 코드Oracle-MNIST: a Realistic Image Dataset for Benchmarking Machine Learning Algorithms
https://github.com/wsdm-cup-2017/lettuce
공식 코드Ensemble of Neural Classifiers for Scoring Knowledge Base Triples
https://github.com/wsmoses/Enzyme
공식 코드Instead of Rewriting Foreign Code for Machine Learning, Automatically Synthesize Fast Gradients
https://github.com/wuchengyuan88/room-occupancy-topology
공식 코드https://github.com/wuchengyuan88/topology-mixed-data
공식 코드Topological Machine Learning for Mixed Numeric and Categorical Data
https://github.com/wuhanstudio/interpretable-ml-covid-19
공식 코드Interpretable Machine Learning for COVID-19: An Empirical Study on Severity Prediction Task
https://github.com/xaiseries/xem
공식 코드XEM: An Explainable-by-Design Ensemble Method for Multivariate Time Series Classification
https://github.com/xcgoner/icml2019_zeno
공식 코드Zeno: Distributed Stochastic Gradient Descent with Suspicion-based Fault-tolerance
https://github.com/xianlizeng/fairbayes
공식 코드https://github.com/xingchenzhao/MixDANN
공식 코드Robust White Matter Hyperintensity Segmentation on Unseen Domain
https://github.com/xinleihe/contrastiveleaks
공식 코드Quantifying and Mitigating Privacy Risks of Contrastive Learning
https://github.com/xjtushujun/slem-theory
공식 코드Learning an Explicit Hyperparameter Prediction Function Conditioned on Tasks
https://github.com/xnnba1984/thyroid-cancer
공식 코드Improving The Diagnosis of Thyroid Cancer by Machine Learning and Clinical Data
https://github.com/xushizhou/fair_data_representation
공식 코드Fair Data Representation for Machine Learning at the Pareto Frontier
https://github.com/y-xue/radfed
공식 코드https://github.com/ya332/robust_sleepnets
공식 코드https://github.com/yala/NeuraCrypt
공식 코드NeuraCrypt: Hiding Private Health Data via Random Neural Networks for Public Training
https://github.com/yandexdataschool/ML-sWeights-experiments
공식 코드https://github.com/yangarbiter/torchaudio-benchmark
공식 코드https://github.com/yangland/rtfps
공식 코드RtFPS: An Interactive Map that Visualizes and Predicts Wildfires in the US
https://github.com/yassine-laguel/spqr
공식 코드First-order Optimization for Superquantile-based Supervised Learning
https://github.com/ycchen1989/Var-QuantumCircuits-DeepRL
공식 코드Variational Quantum Circuits for Deep Reinforcement Learning
https://github.com/ycd2016/acaioc2
공식 코드Highly Efficient Memory Failure Prediction using Mcelog-based Data Mining and Machine Learning
https://github.com/yen-nguyen-thi-thanh/wtot_coclust_match
공식 코드https://github.com/yerevann/mimic3-benchmarks
공식 코드Multitask learning and benchmarking with clinical time series data
https://github.com/yeungyh/pickle
공식 코드Physics-Informed Machine Learning Method for Large-Scale Data Assimilation Problems
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.