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 4150 of 641 papers

TitleStatusHype
ChaCha for Online AutoMLCode2
MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image AnalysisCode2
On Hyperparameter Optimization of Machine Learning Algorithms: Theory and PracticeCode2
Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDLCode2
Frugal Optimization for Cost-related HyperparametersCode2
Towards Automatically-Tuned Deep Neural NetworksCode2
AMC: AutoML for Model Compression and Acceleration on Mobile DevicesCode2
LEMUR Neural Network Dataset: Towards Seamless AutoMLCode1
Enabling AutoML for Zero-Touch Network Security: Use-Case Driven AnalysisCode1
Towards Zero Touch Networks: Cross-Layer Automated Security Solutions for 6G Wireless NetworksCode1
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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