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 501–525 of 641 papers

TitleStatusHype
Demo Application for the AutoGOAL Framework—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
FDNAS: Improving Data Privacy and Model Diversity in AutoML—0
Improving Machine Reading Comprehension with Single-choice Decision and Transfer Learning—0
AgEBO-Tabular: Joint Neural Architecture and Hyperparameter Search with Autotuned Data-Parallel Training for Tabular Data—0
Resource-Aware Pareto-Optimal Automated Machine Learning Platform—0
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
Data Readiness Report—0
AutoADR: Automatic Model Design for Ad Relevance—0
Revisiting Neural Architecture Search—0
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
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
NASirt: AutoML based learning with instance-level complexity information—0
Automated Machine Learning -- a brief review at the end of the early years—0
Show:102550
← PrevPage 21 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