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 201–225 of 641 papers

TitleStatusHype
Hyperparameters in Reinforcement Learning and How To Tune Them—0
PFNs4BO: In-Context Learning for Bayesian OptimizationCode1
ChatGPT as your Personal Data Scientist—0
Deep Pipeline Embeddings for AutoMLCode1
Learning Activation Functions for Sparse Neural NetworksCode0
XTab: Cross-table Pretraining for Tabular TransformersCode1
High-throughput Cotton Phenotyping Big Data Pipeline Lambda Architecture Computer Vision Deep Neural Networks—0
Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature EngineeringCode1
AutoML-GPT: Automatic Machine Learning with GPT—0
EA-HAS-Bench:Energy-Aware Hyperparameter and Architecture Search BenchmarkCode1
MLCopilot: Unleashing the Power of Large Language Models in Solving Machine Learning TasksCode1
Benchmarking Automated Machine Learning Methods for Price Forecasting Applications—0
Deep Fast Vision: Accelerated Deep Transfer Learning Vision Prototyping and BeyondCode1
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
Optimizing Neural Networks through Activation Function Discovery and Automatic Weight InitializationCode1
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
A Framework for the Automated Parameterization of a Sensorless Bearing Fault Detection Pipeline—0
Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML—0
Show:102550
← PrevPage 9 of 26Next →
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