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 526–550 of 641 papers

TitleStatusHype
Evolving Machine Learning Algorithms From Scratch—0
Evolving machine learning workflows through interactive AutoML—0
Explainable Automated Machine Learning for Credit Decisions: Enhancing Human Artificial Intelligence Collaboration in Financial Engineering—0
Exploring Visual Complaints through a test battery in Acquired Brain Injury Patients: A Detailed Analysis of the DiaNAH Dataset—0
Extreme AutoML: Analysis of Classification, Regression, and NLP Performance—0
FairAutoML: Embracing Unfairness Mitigation in AutoML—0
Fair AutoML Through Multi-objective Optimization—0
Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation—0
Fast Data Aware Neural Architecture Search via Supernet Accelerated Evaluation—0
FDNAS: Improving Data Privacy and Model Diversity in AutoML—0
Federated Whole Prostate Segmentation in MRI with Personalized Neural Architectures—0
Fits and Starts: Enterprise Use of AutoML and the Role of Humans in the Loop—0
Floralens: a Deep Learning Model for the Portuguese Native Flora—0
FRAMED: An AutoML Approach for Structural Performance Prediction of Bicycle Frames—0
Generating Diverse Synthetic Datasets for Evaluation of Real-life Recommender Systems—0
Gradients: When Markets Meet Fine-tuning -- A Distributed Approach to Model Optimisation—0
Grad-Instructor: Universal Backpropagation with Explainable Evaluation Neural Networks for Meta-learning and AutoML—0
Grammar-based evolutionary approach for automated workflow composition with domain-specific operators and ensemble diversity—0
Graph Pruning for Model Compression—0
Gravitational wave surrogates through automated machine learning—0
Guided Evolution with Binary Discriminators for ML Program Search—0
FEATHERS: Federated Architecture and Hyperparameter Search—0
Hardware-Centric AutoML for Mixed-Precision Quantization—0
High-throughput Cotton Phenotyping Big Data Pipeline Lambda Architecture Computer Vision Deep Neural Networks—0
How Much Automation Does a Data Scientist Want?—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