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 5175 of 641 papers

TitleStatusHype
Efficient Architecture Search for Diverse TasksCode1
EA-HAS-Bench:Energy-Aware Hyperparameter and Architecture Search BenchmarkCode1
AutoGAN-Distiller: Searching to Compress Generative Adversarial NetworksCode1
Efficient Hyper-parameter Search for Knowledge Graph EmbeddingCode1
Direct Differentiable Augmentation SearchCode1
Does Unsupervised Architecture Representation Learning Help Neural Architecture Search?Code1
AnyMatch -- Efficient Zero-Shot Entity Matching with a Small Language ModelCode1
Architecture Disentanglement for Deep Neural NetworksCode1
AutoGL: A Library for Automated Graph LearningCode1
AutoField: Automating Feature Selection in Deep Recommender SystemsCode1
DHP: Differentiable Meta Pruning via HyperNetworksCode1
Conditional Positional Encodings for Vision TransformersCode1
An Information Theory-inspired Strategy for Automatic Network PruningCode1
Deep-n-Cheap: An Automated Search Framework for Low Complexity Deep LearningCode1
Deep Fast Vision: Accelerated Deep Transfer Learning Vision Prototyping and BeyondCode1
Deep Pipeline Embeddings for AutoMLCode1
DriveML: An R Package for Driverless Machine LearningCode1
AIPerf: Automated machine learning as an AI-HPC benchmarkCode1
Cross-Modal Fine-Tuning: Align then RefineCode1
CliMB: An AI-enabled Partner for Clinical Predictive ModelingCode1
Construction of Hierarchical Neural Architecture Search Spaces based on Context-free GrammarsCode1
DARTS-: Robustly Stepping out of Performance Collapse Without IndicatorsCode1
Cardea: An Open Automated Machine Learning Framework for Electronic Health RecordsCode1
CATE: Computation-aware Neural Architecture Encoding with TransformersCode1
Bi-level Alignment for Cross-Domain Crowd CountingCode1
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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