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 376–400 of 641 papers

TitleStatusHype
On Taking Advantage of Opportunistic Meta-knowledge to Reduce Configuration Spaces for Automated Machine LearningCode0
Meta-learning from Learning Curves Challenge: Lessons learned from the First Round and Design of the Second Round—0
Auto Machine Learning for Medical Image Analysis by Unifying the Search on Data Augmentation and Neural Architecture—0
EVE: Environmental Adaptive Neural Network Models for Low-power Energy Harvesting System—0
The Impact of Feature Quantity on Recommendation Algorithm Performance: A Movielens-100K Case StudyCode0
FEATHERS: Federated Architecture and Hyperparameter Search—0
Exploring the Intersection between Neural Architecture Search and Continual Learning—0
AutoML-Based Drought Forecast with Meteorological Variables—0
SubStrat: A Subset-Based Strategy for Faster AutoMLCode0
Concurrent Neural Tree and Data Preprocessing AutoML for Image Classification—0
Automated machine learning: AI-driven decision making in business analytics—0
A Scalable Workflow to Build Machine Learning Classifiers with Clinician-in-the-Loop to Identify Patients in Specific Diseases—0
Warm-starting DARTS using meta-learning—0
Deep Architecture Connectivity Matters for Its Convergence: A Fine-Grained AnalysisCode0
Serving and Optimizing Machine Learning Workflows on Heterogeneous Infrastructures—0
The Roles and Modes of Human Interactions with Automated Machine Learning Systems—0
E8-IJS@LT-EDI-ACL2022 - BERT, AutoML and Knowledge-graph backed Detection of Depression—0
Enable Deep Learning on Mobile Devices: Methods, Systems, and Applications—0
Search-based Methods for Multi-Cloud Configuration—0
AutoMLBench: A Comprehensive Experimental Evaluation of Automated Machine Learning Frameworks—0
Bridging the Gap of AutoGraph between Academia and Industry: Analysing AutoGraph Challenge at KDD Cup 2020Code0
A Comprehensive Survey on Automated Machine Learning for Recommendations—0
AutoCoMet: Smart Neural Architecture Search via Co-Regulated Shaping Reinforcement—0
AutoML for Deep Recommender Systems: A Survey—0
Meta-Learning of NAS for Few-shot Learning in Medical Image Applications—0
Show:102550
← PrevPage 16 of 26Next →
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