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 101110 of 641 papers

TitleStatusHype
AutoML for Climate Change: A Call to ActionCode1
Hyperparameter optimization in deep multi-target predictionCode1
CATE: Computation-aware Neural Architecture Encoding with TransformersCode1
Efficient Architecture Search for Diverse TasksCode1
Efficient Relation-aware Scoring Function Search for Knowledge Graph EmbeddingCode1
Cross-Modal Fine-Tuning: Align then RefineCode1
Does Unsupervised Architecture Representation Learning Help Neural Architecture Search?Code1
Direct Differentiable Augmentation SearchCode1
Conditional Positional Encodings for Vision TransformersCode1
Automatically Optimized Gradient Boosting Trees for Classifying Large Volume High Cardinality Data Streams Under Concept DriftCode1
Show:102550
← PrevPage 11 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