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 201–250 of 641 papers

TitleStatusHype
Hyperparameters in Reinforcement Learning and How To Tune ThemCode0
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
AutoTransfer: AutoML with Knowledge Transfer -- An Application to Graph Neural NetworksCode0
KGLiDS: A Platform for Semantic Abstraction, Linking, and Automation of Data ScienceCode1
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
Cross-Modal Fine-Tuning: Align then RefineCode1
Unified Functional Hashing in Automatic Machine LearningCode1
Mithridates: Auditing and Boosting Backdoor Resistance of Machine Learning PipelinesCode0
DivBO: Diversity-aware CASH for Ensemble Learning—0
Open Problems in Applied Deep LearningCode4
RF+clust for Leave-One-Problem-Out Performance Prediction—0
Improvement of Computational Performance of Evolutionary AutoML in a Heterogeneous EnvironmentCode0
POPNASv3: a Pareto-Optimal Neural Architecture Search Solution for Image and Time Series Classification—0
Speeding Up Multi-Objective Hyperparameter Optimization by Task Similarity-Based Meta-Learning for the Tree-Structured Parzen EstimatorCode1
Examining marginal properness in the external validation of survival models with squared and logarithmic lossesCode0
AutoPINN: When AutoML Meets Physics-Informed Neural Networks—0
Benchmarking AutoML algorithms on a collection of synthetic classification problemsCode0
Towards Automated Design of Bayesian Optimization via Exploratory Landscape AnalysisCode0
Hyperparameter optimization in deep multi-target predictionCode1
The Technological Emergence of AutoML: A Survey of Performant Software and Applications in the Context of Industry—0
AutoML-based Almond Yield Prediction and Projection in California—0
Construction of Hierarchical Neural Architecture Search Spaces based on Context-free GrammarsCode1
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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