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 571580 of 641 papers

TitleStatusHype
SCARLET-NAS: Bridging the Gap between Stability and Scalability in Weight-sharing Neural Architecture SearchCode0
Towards Automated Machine Learning: Evaluation and Comparison of AutoML Approaches and Tools0
MoGA: Searching Beyond MobileNetV3Code0
Towards AutoML in the presence of Drift: first results0
Techniques for Automated Machine Learning0
Automated Machine Learning in Practice: State of the Art and Recent Results0
ShrinkML: End-to-End ASR Model Compression Using Reinforcement Learning0
Visus: An Interactive System for Automatic Machine Learning Model Building and Curation0
Transfer Learning for Risk Classification of Social Media Posts: Model Evaluation StudyCode0
Encoding high-cardinality string categorical variablesCode0
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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