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 621630 of 641 papers

TitleStatusHype
Auto-Keras: An Efficient Neural Architecture Search SystemCode0
Lessons learned from the AutoML challenge0
Rafiki: Machine Learning as an Analytics Service SystemCode0
AutoML from Service Provider's Perspective: Multi-device, Multi-tenant Model Selection with GP-EI0
Transfer Learning with Neural AutoML0
Autostacker: A Compositional Evolutionary Learning System0
AMC: AutoML for Model Compression and Acceleration on Mobile DevicesCode2
Layered TPOT: Speeding up Tree-based Pipeline OptimizationCode3
AMLA: an AutoML frAmework for Neural Network Design0
Autostacker: an Automatic Evolutionary Hierarchical Machine Learning System0
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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