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 351–400 of 641 papers

TitleStatusHype
Mithridates: Auditing and Boosting Backdoor Resistance of Machine Learning PipelinesCode0
DivBO: Diversity-aware CASH for Ensemble Learning—0
RF+clust for Leave-One-Problem-Out Performance Prediction—0
Improvement of Computational Performance of Evolutionary AutoML in a Heterogeneous EnvironmentCode0
POPNASv3: a Pareto-Optimal Neural Architecture Search Solution for Image and Time Series Classification—0
Examining marginal properness in the external validation of survival models with squared and logarithmic lossesCode0
AutoPINN: When AutoML Meets Physics-Informed Neural Networks—0
Benchmarking AutoML algorithms on a collection of synthetic classification problemsCode0
Towards Automated Design of Bayesian Optimization via Exploratory Landscape AnalysisCode0
AutoML-based Almond Yield Prediction and Projection in California—0
The Technological Emergence of AutoML: A Survey of Performant Software and Applications in the Context of Industry—0
Automated Imbalanced LearningCode0
Neural Architectural Backdoors—0
Efficient Automatic Machine Learning via Design GraphsCode0
Multi-Agent Automated Machine Learning—0
NAS-Bench-Suite-Zero: Accelerating Research on Zero Cost Proxies—0
TC-SKNet with GridMask for Low-complexity Classification of Acoustic scene—0
Efficient Non-Parametric Optimizer Search for Diverse TasksCode0
Automatic and effective discovery of quantum kernelsCode0
Industrial Data Science for Batch Manufacturing Processes—0
MDE for Machine Learning-Enabled Software Systems: A Case Study and Comparison of MontiAnna & ML-Quadrat—0
A Deep Neural Networks ensemble workflow from hyperparameter search to inference leveraging GPU clusters—0
An Empirical Study on the Usage of Automated Machine Learning ToolsCode0
Task Selection for AutoML System Evaluation—0
Survey on Evolutionary Deep Learning: Principles, Algorithms, Applications and Open Issues—0
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
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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