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 601610 of 641 papers

TitleStatusHype
Model LineUpper: Supporting Interactive Model Comparison at Multiple Levels for AutoML0
MONCAE: Multi-Objective Neuroevolution of Convolutional Autoencoders0
Multi-Agent Automated Machine Learning0
Multi-Microgrid Collaborative Optimization Scheduling Using an Improved Multi-Agent Soft Actor-Critic Algorithm0
Multimodal Representation Learning and Fusion0
Multi-Objective Automatic Machine Learning with AutoxgboostMC0
Multi-objective Evolutionary Search of Variable-length Composite Semantic Perturbations0
Naive Automated Machine Learning -- A Late Baseline for AutoML0
NAS-Bench-Suite: NAS Evaluation is (Now) Surprisingly Easy0
NAS-Bench-Suite-Zero: Accelerating Research on Zero Cost Proxies0
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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