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 301–350 of 641 papers

TitleStatusHype
Efficient Hyper-parameter Search for Knowledge Graph EmbeddingCode1
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
AutoField: Automating Feature Selection in Deep Recommender SystemsCode1
AutoMLBench: A Comprehensive Experimental Evaluation of Automated Machine Learning Frameworks—0
Efficient Architecture Search for Diverse TasksCode1
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
BigDL 2.0: Seamless Scaling of AI Pipelines from Laptops to Distributed ClusterCode5
AutoCoMet: Smart Neural Architecture Search via Co-Regulated Shaping Reinforcement—0
AutoML for Deep Recommender Systems: A Survey—0
Privacy-preserving Online AutoML for Domain-Specific Face Detection—0
Meta-Learning of NAS for Few-shot Learning in Medical Image Applications—0
Deep AutoAugmentCode1
Model-free feature selection to facilitate automatic discovery of divergent subgroups in tabular data—0
Automated Machine Learning: A Case Study on Non-Intrusive Appliance Load Monitoring—0
XAutoML: A Visual Analytics Tool for Understanding and Validating Automated Machine LearningCode1
AutoCl : A Visual Interactive System for Automatic Deep Learning Classifier Recommendation Based on Models Performance—0
Mining Robust Default Configurations for Resource-constrained AutoML—0
SapientML: Synthesizing Machine Learning Pipelines by Learning from Human-Written Solutions—0
Review of automated time series forecasting pipelines—0
NAS-Bench-Suite: NAS Evaluation is (Now) Surprisingly EasyCode0
FRAMED: An AutoML Approach for Structural Performance Prediction of Bicycle Frames—0
Online AutoML: An adaptive AutoML framework for online learning—0
Automated Reinforcement Learning (AutoRL): A Survey and Open Problems—0
Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019—0
TPAD: Identifying Effective Trajectory Predictions Under the Guidance of Trajectory Anomaly Detection Model—0
Neural Architecture Search for Inversion—0
MLOps -- Definitions, Tools and Challenges—0
AutoDES: AutoML Pipeline Generation of Classification with Dynamic Ensemble Strategy Selection—0
Transformers Can Do Bayesian InferenceCode1
Evaluating Generic Auto-ML Tools for Computational Pathology—0
Manas: Mining Software Repositories to Assist AutoMLCode0
Pipeline Combinators for Gradual AutoML—0
Naive Automated Machine LearningCode1
Fast and Informative Model Selection using Learning Curve Cross-ValidationCode0
Diagnosis of sickle cell anemia using AutoML on UV-Vis absorbance spectroscopy data—0
AutoDC: Automated data-centric processingCode1
DIVA: Dataset Derivative of a Learning Task—0
T-AutoML: Automated Machine Learning for Lesion Segmentation using Transformers in 3D Medical Imaging—0
FairAutoML: Embracing Unfairness Mitigation in AutoML—0
Towards Green Automated Machine Learning: Status Quo and Future Directions—0
Tightening the Approximation Error of Adversarial Risk with Auto Loss Function Search—0
NAS-Bench-x11 and the Power of Learning CurvesCode1
Benchmarking Multimodal AutoML for Tabular Data with Text FieldsCode3
AlphaD3M: Machine Learning Pipeline Synthesis—0
A Scalable AutoML Approach Based on Graph Neural NetworksCode0
MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classificationCode2
On Predictive Explanation of Data Anomalies—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