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 391400 of 641 papers

TitleStatusHype
ChaCha for Online AutoMLCode2
MONCAE: Multi-Objective Neuroevolution of Convolutional Autoencoders0
Tabular Data: Deep Learning is Not All You NeedCode0
You Only Compress Once: Towards Effective and Elastic BERT Compression via Exploit-Explore Stochastic Nature GradientCode1
Replacing the Ex-Def Baseline in AutoML by Naive AutoML0
Sequential Automated Machine Learning: Bandits-driven Exploration using a Collaborative Filtering Representation0
Towards Model Selection using Learning Curve Cross-ValidationCode0
Multimodal AutoML on Structured Tables with Text FieldsCode1
AutoML Adoption in ML Software0
Incorporating domain knowledge into neural-guided search via in situ priors and constraints0
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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