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 476–500 of 641 papers

TitleStatusHype
Channel-wise Hessian Aware trace-Weighted Quantization of Neural Networks—0
ChatGPT as your Personal Data Scientist—0
CLAMS: A System for Zero-Shot Model Selection for Clustering—0
CMA-ES for Post Hoc Ensembling in AutoML: A Great Success and Salvageable Failure—0
Communication-Computation Efficient Device-Edge Co-Inference via AutoML—0
Comparing AutoML and Deep Learning Methods for Condition Monitoring using Realistic Validation Scenarios—0
Complex Mixer for MedMNIST Classification Decathlon—0
Concurrent Neural Tree and Data Preprocessing AutoML for Image Classification—0
Considerations of automated machine learning in clinical metabolic profiling: Altered homocysteine plasma concentration associated with metformin exposure—0
Constructing a meta-learner for unsupervised anomaly detection—0
Continual Learning in Practice—0
COPA: Comparing the Incomparable to Explore the Pareto Front—0
Creation and Evaluation of a Food Product Image Dataset for Product Property Extraction—0
Dancing along Battery: Enabling Transformer with Run-time Reconfigurability on Mobile Devices—0
DarwinML: A Graph-based Evolutionary Algorithm for Automated Machine Learning—0
Data-Algorithm-Architecture Co-Optimization for Fair Neural Networks on Skin Lesion Dataset—0
Data Analytics and Machine Learning Methods, Techniques and Tool for Model-Driven Engineering of Smart IoT Services—0
DataAssist: A Machine Learning Approach to Data Cleaning and Preparation—0
Data Augmentation of Multivariate Sensor Time Series using Autoregressive Models and Application to Failure Prognostics—0
Data augmentation with automated machine learning: approaches and performance comparison with classical data augmentation methods—0
Data Pipeline Training: Integrating AutoML to Optimize the Data Flow of Machine Learning Models—0
Data Readiness Report—0
Deciphering AutoML Ensembles: cattleia's Assistance in Decision-Making—0
Deep Learning and Machine Learning, Advancing Big Data Analytics and Management: Unveiling AI's Potential Through Tools, Techniques, and Applications—0
DeepLine: AutoML Tool for Pipelines Generation using Deep Reinforcement Learning and Hierarchical Actions Filtering—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