Papers with Code 아카이브
AutoML
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Automated Machine Learning (AutoML) is a general concept which covers diverse techniques for automated model learning including automatic data preprocessing, architecture search, and model selection. Source: Evaluating recommender systems for AI-driven data science (1905.09205)
Source: CHOPT : Automated Hyperparameter Optimization Framework for Cloud-Based Machine Learning Platforms
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ajbrock/SMASH
공식 코드SMASH: One-Shot Model Architecture Search through HyperNetworks
https://github.com/albertipot/nar
공식 코드Generalized Global Ranking-Aware Neural Architecture Ranker for Efficient Image Classifier Search
https://github.com/alchemi5t/MNADrc
공식 코드https://github.com/alexa/bort
공식 코드https://github.com/alexadlu/after
공식 코드https://github.com/alexandertornede/extreme_algorithm_selection
공식 코드Extreme Algorithm Selection With Dyadic Feature Representation
https://github.com/alexandrewang915/autost
공식 코드AutoST: Training-free Neural Architecture Search for Spiking Transformers
https://github.com/aleximmer/ntk-marglik
공식 코드Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels
https://github.com/alfurka/synloc
공식 코드Generating Synthetic Data with Locally Estimated Distributions for Disclosure Control
https://github.com/alibaba-miil/binas
공식 코드https://github.com/alibaba/federatedscope
공식 코드FederatedScope: A Flexible Federated Learning Platform for Heterogeneity
https://github.com/alibaba/lightweight-neural-architecture-search
공식 코드Zen-NAS: A Zero-Shot NAS for High-Performance Deep Image Recognition
https://github.com/alimama-creative/AutoCO
공식 코드https://github.com/allenai/dnw
공식 코드https://github.com/amanpriyanshu/dp-hyperparamtuning
공식 코드Efficient Hyperparameter Optimization for Differentially Private Deep Learning
https://github.com/amazon-research/bo-early-stopping
공식 코드https://github.com/amazon-research/fraud-dataset-benchmark
공식 코드https://github.com/amazon-science/co-with-gnns-example
공식 코드Combinatorial Optimization with Automated Graph Neural Networks
https://github.com/amazon-science/fraud-dataset-benchmark
공식 코드https://github.com/amourwaltz/bayeslms
공식 코드Bayesian Neural Network Language Modeling for Speech Recognition
https://github.com/amzn/emukit
공식 코드https://github.com/anandkanav92/htune
공식 코드Black Magic in Deep Learning: How Human Skill Impacts Network Training
https://github.com/andreamattiagaravagno/colabnas
공식 코드Colab NAS: Obtaining lightweight task-specific convolutional neural networks following Occam's razor
https://github.com/andrewkkan/fathom
공식 코드https://github.com/angusdujw/ftd-distillation
공식 코드Minimizing the Accumulated Trajectory Error to Improve Dataset Distillation
https://github.com/ankilab/spoken-100
공식 코드https://github.com/anminggu/kmixup-cifar10
공식 코드k-Mixup Regularization for Deep Learning via Optimal Transport
https://github.com/anoise/periodformer
공식 코드Does Long-Term Series Forecasting Need Complex Attention and Extra Long Inputs?
https://github.com/anonxyz123/fgk62af5
공식 코드https://github.com/antaegun20/CycleGANAS
공식 코드CycleGANAS: Differentiable Neural Architecture Search for CycleGAN
https://github.com/antoninschrab/fl-mmdagg
공식 코드https://github.com/antoyang/NAS-Benchmark
공식 코드https://github.com/aoiang/few-shot-NAS
공식 코드https://github.com/aoiang/lamoo
공식 코드https://github.com/apourchot/to_share_or_not_to_share
공식 코드To Share or Not To Share: A Comprehensive Appraisal of Weight-Sharing
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.