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 181190 of 641 papers

TitleStatusHype
Improve Deep Forest with Learnable Layerwise Augmentation Policy ScheduleCode0
Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine LearningCode0
Imbalanced Regression Pipeline RecommendationCode0
Improved Training Speed, Accuracy, and Data Utilization Through Loss Function OptimizationCode0
Automated Imbalanced LearningCode0
Mithridates: Auditing and Boosting Backdoor Resistance of Machine Learning PipelinesCode0
Automated Creative Optimization for E-Commerce AdvertisingCode0
Identifying and Harnessing the Building Blocks of Machine Learning Pipelines for Sensible Initialization of a Data Science Automation ToolCode0
Improvement of Computational Performance of Evolutionary AutoML in a Heterogeneous EnvironmentCode0
Hardware Aware Ensemble Selection for Balancing Predictive Accuracy and CostCode0
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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