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 251–300 of 641 papers

TitleStatusHype
Gravitational wave surrogates through automated machine learning—0
Hardware-Centric AutoML for Mixed-Precision Quantization—0
Human-Centered AI for Data Science: A Systematic Approach—0
Improved Training Speed, Accuracy, and Data Utilization via Loss Function Optimization—0
A Very Brief and Critical Discussion on AutoML—0
10 Years of Fair Representations: Challenges and Opportunities—0
AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Models—0
A New Deep Neural Architecture Search Pipeline for Face Recognition—0
Generating Diverse Synthetic Datasets for Evaluation of Real-life Recommender Systems—0
Auto-survey Challenge—0
Autostacker: an Automatic Evolutionary Hierarchical Machine Learning System—0
Autostacker: A Compositional Evolutionary Learning System—0
AutoSpeech 2020: The Second Automated Machine Learning Challenge for Speech Classification—0
An AutoML-based Approach to Multimodal Image Sentiment Analysis—0
AI-based Classification of Customer Support Tickets: State of the Art and Implementation with AutoML—0
FRAMED: An AutoML Approach for Structural Performance Prediction of Bicycle Frames—0
AutoRAG-HP: Automatic Online Hyper-Parameter Tuning for Retrieval-Augmented Generation—0
AutoEn: An AutoML method based on ensembles of predefined Machine Learning pipelines for supervised Traffic Forecasting—0
Gradients: When Markets Meet Fine-tuning -- A Distributed Approach to Model Optimisation—0
AutoQ: Automated Kernel-Wise Neural Network Quantization—0
AutoEmb: Automated Embedding Dimensionality Search in Streaming Recommendations—0
AutoPruning for Deep Neural Network with Dynamic Channel Masking—0
AutoDS: Towards Human-Centered Automation of Data Science—0
An AutoML-based approach for Network Intrusion Detection—0
AutoPINN: When AutoML Meets Physics-Informed Neural Networks—0
AutoPDL: Automatic Prompt Optimization for LLM Agents—0
AutoDOViz: Human-Centered Automation for Decision Optimization—0
Application of an automated machine learning-genetic algorithm (AutoML-GA) coupled with computational fluid dynamics simulations for rapid engine design optimization—0
AutoCP: Automated Pipelines for Accurate Prediction Intervals—0
A General Recipe for Automated Machine Learning in Practice—0
AgEBO-Tabular: Joint Neural Architecture and Hyperparameter Search with Autotuned Data-Parallel Training for Tabular Data—0
AutoDES: AutoML Pipeline Generation of Classification with Dynamic Ensemble Strategy Selection—0
An Approach for Efficient Neural Architecture Search Space Definition—0
An Automated Machine Learning (AutoML) Method for Driving Distraction Detection Based on Lane-Keeping Performance—0
Federated Whole Prostate Segmentation in MRI with Personalized Neural Architectures—0
AutoML to generate ensembles of deep neural networks—0
AutoML Systems For Medical Imaging—0
AutoCompete: A Framework for Machine Learning Competitions—0
AutoML Strategy Based on Grammatical Evolution: A Case Study about Knowledge Discovery from Text—0
AutoCoMet: Smart Neural Architecture Search via Co-Regulated Shaping Reinforcement—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
Grad-Instructor: Universal Backpropagation with Explainable Evaluation Neural Networks for Meta-learning and AutoML—0
AutoML @ NeurIPS 2018 challenge: Design and Results—0
AutoCl : A Visual Interactive System for Automatic Deep Learning Classifier Recommendation Based on Models Performance—0
A Framework for the Automated Parameterization of a Sensorless Bearing Fault Detection Pipeline—0
AutoML in The Wild: Obstacles, Workarounds, and Expectations—0
AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks—0
Analysis of the AutoML Challenge Series 2015–2018—0
Fast Data Aware Neural Architecture Search via Supernet Accelerated Evaluation—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