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 376–400 of 641 papers

TitleStatusHype
Benchmarking AutoML Frameworks for Disease Prediction Using Medical Claims—0
Incorporating domain knowledge into neural-guided search—0
VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space DecompositionCode1
Federated Whole Prostate Segmentation in MRI with Personalized Neural Architectures—0
The Power of Proxy Data and Proxy Networks for Hyper-Parameter Optimization in Medical Image Segmentation—0
Designing Machine Learning Pipeline Toolkit for AutoML Surrogate Modeling Optimization—0
Bilinear Scoring Function Search for Knowledge Graph LearningCode1
AutoFormer: Searching Transformers for Visual RecognitionCode2
Exploring Robust Architectures for Deep Artificial Neural NetworksCode0
Privileged Zero-Shot AutoML—0
Evaluation of Representation Models for Text Classification with AutoML Tools—0
Comparison of Automated Machine Learning Tools for SMS Spam Message FilteringCode0
Efficient Data-specific Model Search for Collaborative Filtering—0
Automated Machine Learning Techniques for Data StreamsCode1
A multi-objective perspective on jointly tuning hardware and hyperparameters—0
ChaCha for Online AutoMLCode2
MONCAE: Multi-Objective Neuroevolution of Convolutional Autoencoders—0
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 AutoML—0
Sequential Automated Machine Learning: Bandits-driven Exploration using a Collaborative Filtering Representation—0
Towards Model Selection using Learning Curve Cross-ValidationCode0
Multimodal AutoML on Structured Tables with Text FieldsCode1
AutoML Adoption in ML Software—0
Incorporating domain knowledge into neural-guided search via in situ priors and constraints—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