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 426–450 of 641 papers

TitleStatusHype
Conditional Positional Encodings for Vision TransformersCode1
An AutoML-based Approach to Multimodal Image Sentiment Analysis—0
CATE: Computation-aware Neural Architecture Encoding with TransformersCode1
Dancing along Battery: Enabling Transformer with Run-time Reconfigurability on Mobile Devices—0
Leveraging Benchmarking Data for Informed One-Shot Dynamic Algorithm Selection—0
Data Analytics and Machine Learning Methods, Techniques and Tool for Model-Driven Engineering of Smart IoT Services—0
Incremental Search Space Construction for Machine Learning Pipeline Synthesis—0
PyGlove: Symbolic Programming for Automated Machine Learning—0
JITuNE: Just-In-Time Hyperparameter Tuning for Network Embedding Algorithms—0
Robusta: Robust AutoML for Feature Selection via Reinforcement Learning—0
A Neophyte With AutoML: Evaluating the Promises of Automatic Machine Learning ToolsCode0
AutoDS: Towards Human-Centered Automation of Data Science—0
Whither AutoML? Understanding the Role of Automation in Machine Learning Workflows—0
Fits and Starts: Enterprise Use of AutoML and the Role of Humans in the Loop—0
How Much Automation Does a Data Scientist Want?—0
Application of an automated machine learning-genetic algorithm (AutoML-GA) coupled with computational fluid dynamics simulations for rapid engine design optimization—0
Hyperboost: Hyperparameter Optimization by Gradient Boosting surrogate models—0
DiffAutoML: Differentiable Joint Optimization for Efficient End-to-End Automated Machine Learning—0
Real-Time AutoML—0
ECONOMIC HYPERPARAMETER OPTIMIZATION WITH BLENDED SEARCH STRATEGY—0
Joint Search of Data Augmentation Policies and Network Architectures—0
Squirrel: A Switching Hyperparameter OptimizerCode1
Amazon SageMaker Autopilot: a white box AutoML solution at scale—0
Ensemble Squared: A Meta AutoML System—0
Efficient Automatic CASH via Rising Bandits—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