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

Showing 421430 of 641 papers

TitleStatusHype
Task-Adaptive Neural Network Search with Meta-Contrastive LearningCode1
Multi-Objective Evolutionary Design of Composite Data-Driven ModelsCode1
Automated Machine Learning on Graphs: A SurveyCode1
Automated Creative Optimization for E-Commerce AdvertisingCode0
Interpret-able feedback for AutoML systems0
Conditional Positional Encodings for Vision TransformersCode1
An AutoML-based Approach to Multimodal Image Sentiment Analysis0
CATE: Computation-aware Neural Architecture Encoding with TransformersCode1
Dancing along Battery: Enabling Transformer with Run-time Reconfigurability on Mobile Devices0
Leveraging Benchmarking Data for Informed One-Shot Dynamic Algorithm Selection0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1marc.boulleRank (AutoML5)6.4Unverified
2reference_mbRank (AutoML5)5.2Unverified
3postech.mlg_exbrainRank (AutoML5)5.2Unverified
4abhishek4Rank (AutoML5)4.6Unverified
5referenceRank (AutoML5)4.4Unverified
6reference_lsRank (AutoML5)4Unverified
7djajeticRank (AutoML5)3Unverified
8aad_freiburgRank (AutoML5)1.6Unverified
#ModelMetricClaimedVerifiedStatus
1Logistic RegressionAccuracy97.02Unverified
#ModelMetricClaimedVerifiedStatus
1Zero-shot-BERT-SORT1:1 Accuracy55Unverified
#ModelMetricClaimedVerifiedStatus
1Logistic Regressionaccuracy98.33Unverified