SOTAVerified

AutoML

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

Papers

Showing 151160 of 641 papers

TitleStatusHype
MetaPoison: Practical General-purpose Clean-label Data PoisoningCode1
Architecture Disentanglement for Deep Neural NetworksCode1
DHP: Differentiable Meta Pruning via HyperNetworksCode1
Deep-n-Cheap: An Automated Search Framework for Low Complexity Deep LearningCode1
MixPath: A Unified Approach for One-shot Neural Architecture SearchCode1
Automatically Optimized Gradient Boosting Trees for Classifying Large Volume High Cardinality Data Streams Under Concept DriftCode1
FLAML: A Fast and Lightweight AutoML LibraryCode1
Once-for-All: Train One Network and Specialize it for Efficient DeploymentCode1
AutoML: A Survey of the State-of-the-ArtCode1
MixConv: Mixed Depthwise Convolutional KernelsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1marc.boulleRank (AutoML5)6.4Unverified
2reference_mbRank (AutoML5)5.2Unverified
3postech.mlg_exbrainRank (AutoML5)5.2Unverified
4abhishek4Rank (AutoML5)4.6Unverified
5referenceRank (AutoML5)4.4Unverified
6reference_lsRank (AutoML5)4Unverified
7djajeticRank (AutoML5)3Unverified
8aad_freiburgRank (AutoML5)1.6Unverified
#ModelMetricClaimedVerifiedStatus
1Logistic RegressionAccuracy97.02Unverified
#ModelMetricClaimedVerifiedStatus
1Zero-shot-BERT-SORT1:1 Accuracy55Unverified
#ModelMetricClaimedVerifiedStatus
1Logistic Regressionaccuracy98.33Unverified