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

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Showing 451–500 of 641 papers

TitleStatusHype
Leveraging Automated Machine Learning for Text Classification: Evaluation of AutoML Tools and Comparison with Human Performance—0
MFES-HB: Efficient Hyperband with Multi-Fidelity Quality MeasurementsCode1
Demo Application for the AutoGOAL Framework—0
Automatic Discovery of Heterogeneous Machine Learning Pipelines: An Application to Natural Language Processing—0
Long Short Term Memory Networks for Bandwidth Forecasting in Mobile Broadband Networks under Mobility—0
Automatic selection of clustering algorithms using supervised graph embeddingCode0
Automated Model Compression by Jointly Applied Pruning and Quantization—0
Improving Machine Reading Comprehension with Single-choice Decision and Transfer Learning—0
FDNAS: Improving Data Privacy and Model Diversity in AutoML—0
VEGA: Towards an End-to-End Configurable AutoML PipelineCode1
Resource-Aware Pareto-Optimal Automated Machine Learning Platform—0
AgEBO-Tabular: Joint Neural Architecture and Hyperparameter Search with Autotuned Data-Parallel Training for Tabular Data—0
MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image AnalysisCode2
Crop and weed classification based on AutoML—0
AutoSpeech 2020: The Second Automated Machine Learning Challenge for Speech Classification—0
AutoPruning for Deep Neural Network with Dynamic Channel Masking—0
AutoML to Date and Beyond: Challenges and Opportunities—0
AutoBSS: An Efficient Algorithm for Block Stacking Style Search—0
Automatic Feasibility Study via Data Quality Analysis for ML: A Case-Study on Label NoiseCode1
AutoADR: Automatic Model Design for Ad Relevance—0
Data Readiness Report—0
Revisiting Neural Architecture Search—0
Smooth Variational Graph Embeddings for Efficient Neural Architecture SearchCode1
Cardea: An Open Automated Machine Learning Framework for Electronic Health RecordsCode1
Automating Outlier Detection via Meta-LearningCode1
Evolutionary Architecture Search for Graph Neural NetworksCode0
Automatic deep learning for trend prediction in time series data—0
An Extensive Experimental Evaluation of Automated Machine Learning Methods for Recommending Classification Algorithms (Extended Version)—0
AutoML for Multilayer Perceptron and FPGA Co-design—0
Hyperparameter Optimization via Sequential Uniform DesignsCode1
HyperTendril: Visual Analytics for User-Driven Hyperparameter Optimization of Deep Neural Networks—0
Can AutoML outperform humans? An evaluation on popular OpenML datasets using AutoML Benchmark—0
Auto-Classifier: A Robust Defect Detector Based on an AutoML HeadCode0
DARTS-: Robustly Stepping out of Performance Collapse Without IndicatorsCode1
NASirt: AutoML based learning with instance-level complexity information—0
Channel-wise Hessian Aware trace-Weighted Quantization of Neural Networks—0
Automated Machine Learning -- a brief review at the end of the early years—0
NASE: Learning Knowledge Graph Embedding for Link Prediction via Neural Architecture SearchCode0
AIPerf: Automated machine learning as an AI-HPC benchmarkCode1
Hardware-Centric AutoML for Mixed-Precision Quantization—0
Iterative Compression of End-to-End ASR Model using AutoML—0
Weight-Sharing Neural Architecture Search: A Battle to Shrink the Optimization Gap—0
Shape Adaptor: A Learnable Resizing ModuleCode1
On Hyperparameter Optimization of Machine Learning Algorithms: Theory and PracticeCode2
Practical and sample efficient zero-shot HPO—0
Loss Function Search for Face RecognitionCode1
GAMA: a General Automated Machine learning AssistantCode1
Auto-Sklearn 2.0: Hands-free AutoML via Meta-LearningCode3
AutoRec: An Automated Recommender SystemCode0
Memory-efficient Embedding for Recommendations—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