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 381390 of 641 papers

TitleStatusHype
AnnotatedTables: A Large Tabular Dataset with Language Model Annotations0
An Open Source AutoML Benchmark0
Approximation capability of neural networks on sets of probability measures and tree-structured data0
ArchBERT: Bi-Modal Understanding of Neural Architectures and Natural Languages0
Architecture-Aware Learning Curve Extrapolation via Graph Ordinary Differential Equation0
Are Large Language Models the New Interface for Data Pipelines?0
Exploring the Intersection between Neural Architecture Search and Continual Learning0
A Scalable Workflow to Build Machine Learning Classifiers with Clinician-in-the-Loop to Identify Patients in Specific Diseases0
ASP: Automatic Selection of Proxy dataset for efficient AutoML0
Assessing the Use of AutoML for Data-Driven Software Engineering0
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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