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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 326–350 of 641 papers

TitleStatusHype
AutoML Systems For Medical Imaging—0
Hyperparameters in Reinforcement Learning and How To Tune Them—0
ChatGPT as your Personal Data Scientist—0
Learning Activation Functions for Sparse Neural NetworksCode0
High-throughput Cotton Phenotyping Big Data Pipeline Lambda Architecture Computer Vision Deep Neural Networks—0
AutoML-GPT: Automatic Machine Learning with GPT—0
Benchmarking Automated Machine Learning Methods for Price Forecasting Applications—0
Constructing a meta-learner for unsupervised anomaly detection—0
Complex Mixer for MedMNIST Classification Decathlon—0
eTOP: Early Termination of Pipelines for Faster Training of AutoML Systems—0
AutoRL Hyperparameter LandscapesCode0
Classification of integers based on residue classes via modern deep learning algorithmsCode0
Multi-Microgrid Collaborative Optimization Scheduling Using an Improved Multi-Agent Soft Actor-Critic Algorithm—0
Synthesis of Mathematical programs from Natural Language Specifications—0
Efficient Multi-stage Inference on Tabular Data—0
AutoEn: An AutoML method based on ensembles of predefined Machine Learning pipelines for supervised Traffic Forecasting—0
Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML—0
A Framework for the Automated Parameterization of a Sensorless Bearing Fault Detection Pipeline—0
AutoTransfer: AutoML with Knowledge Transfer -- An Application to Graph Neural NetworksCode0
OmniForce: On Human-Centered, Large Model Empowered and Cloud-Edge Collaborative AutoML System—0
Towards Personalized Preprocessing Pipeline Search—0
Scalable End-to-End ML Platforms: from AutoML to Self-serve—0
AutoML for neuromorphic computing and application-driven co-design: asynchronous, massively parallel optimization of spiking architecturesCode0
AutoML in The Wild: Obstacles, Workarounds, and Expectations—0
AutoDOViz: Human-Centered Automation for Decision Optimization—0
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All datasetsChalearn-AutoML-1Breast Cancer Coimbra Data SetOrdinalDatasetWine

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1marc.boulleRank (AutoML5)6.4—Unverified
2reference_mbRank (AutoML5)5.2—Unverified
3postech.mlg_exbrainRank (AutoML5)5.2—Unverified
4abhishek4Rank (AutoML5)4.6—Unverified
5referenceRank (AutoML5)4.4—Unverified
6reference_lsRank (AutoML5)4—Unverified
7djajeticRank (AutoML5)3—Unverified
8aad_freiburgRank (AutoML5)1.6—Unverified
#ModelMetricClaimedVerifiedStatus
1Logistic RegressionAccuracy97.02—Unverified
#ModelMetricClaimedVerifiedStatus
1Zero-shot-BERT-SORT1:1 Accuracy55—Unverified
#ModelMetricClaimedVerifiedStatus
1Logistic Regressionaccuracy98.33—Unverified