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

Showing 141150 of 641 papers

TitleStatusHype
Deep-n-Cheap: An Automated Search Framework for Low Complexity Deep LearningCode1
Deep Pipeline Embeddings for AutoMLCode1
BERT-Sort: A Zero-shot MLM Semantic Encoder on Ordinal Features for AutoMLCode1
AutoML Segmentation for 3D Medical Image Data: Contribution to the MSD Challenge 2018Code1
Bi-level Alignment for Cross-Domain Crowd CountingCode1
EA-HAS-Bench:Energy-Aware Hyperparameter and Architecture Search BenchmarkCode1
Construction of Hierarchical Neural Architecture Search Spaces based on Context-free GrammarsCode1
Conditional Positional Encodings for Vision TransformersCode1
Efficient End-to-End AutoML via Scalable Search Space DecompositionCode1
FLAML: A Fast and Lightweight AutoML LibraryCode1
Show:102550
← PrevPage 15 of 65Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1marc.boulleRank (AutoML5)6.4Unverified
2reference_mbRank (AutoML5)5.2Unverified
3postech.mlg_exbrainRank (AutoML5)5.2Unverified
4abhishek4Rank (AutoML5)4.6Unverified
5referenceRank (AutoML5)4.4Unverified
6reference_lsRank (AutoML5)4Unverified
7djajeticRank (AutoML5)3Unverified
8aad_freiburgRank (AutoML5)1.6Unverified
#ModelMetricClaimedVerifiedStatus
1Logistic RegressionAccuracy97.02Unverified
#ModelMetricClaimedVerifiedStatus
1Zero-shot-BERT-SORT1:1 Accuracy55Unverified
#ModelMetricClaimedVerifiedStatus
1Logistic Regressionaccuracy98.33Unverified