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 561570 of 641 papers

TitleStatusHype
Improved Training Speed, Accuracy, and Data Utilization via Loss Function Optimization0
Improving generalisation of AutoML systems with dynamic fitness evaluations0
Improving Machine Reading Comprehension with Single-choice Decision and Transfer Learning0
Incorporating domain knowledge into neural-guided search0
Incorporating domain knowledge into neural-guided search via in situ priors and constraints0
Incremental Search Space Construction for Machine Learning Pipeline Synthesis0
Industrial Data Science for Batch Manufacturing Processes0
Integration Of Evolutionary Automated Machine Learning With Structural Sensitivity Analysis For Composite Pipelines0
Interpret-able feedback for AutoML systems0
Iterative Compression of End-to-End ASR Model using AutoML0
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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