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 181190 of 641 papers

TitleStatusHype
Efficient Model Adaptation for Continual Learning at the Edge0
Discovering Adaptable Symbolic Algorithms from Scratch0
Predicting delays in Indian lower courts using AutoML and Decision ForestsCode0
Assessing the Use of AutoML for Data-Driven Software Engineering0
A Survey on Multi-Objective Neural Architecture Search0
Q(D)O-ES: Population-based Quality (Diversity) Optimisation for Post Hoc Ensemble Selection in AutoML0
DataAssist: A Machine Learning Approach to Data Cleaning and Preparation0
Multi-objective Evolutionary Search of Variable-length Composite Semantic Perturbations0
Efficient and Joint Hyperparameter and Architecture Search for Collaborative FilteringCode0
SigOpt Mulch: An Intelligent System for AutoML of Gradient Boosted Trees0
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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