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 551–575 of 641 papers

TitleStatusHype
Trust in AutoML: Exploring Information Needs for Establishing Trust in Automated Machine Learning Systems—0
Stepwise Model Selection for Sequence Prediction via Deep Kernel Learning—0
Searching to Exploit Memorization Effect in Learning with Noisy Labels—0
Evolving Machine Learning Algorithms From Scratch—0
AutoML: Exploration v.s. ExploitationCode0
AutoAIViz: Opening the Blackbox of Automated Artificial Intelligence with Conditional Parallel Coordinates—0
Fair DARTS: Eliminating Unfair Advantages in Differentiable Architecture SearchCode0
Graph Pruning for Model Compression—0
Towards Human Centered AutoML—0
Searching to Exploit Memorization Effect in Learning from Corrupted LabelsCode0
DeepLine: AutoML Tool for Pipelines Generation using Deep Reinforcement Learning and Hierarchical Actions Filtering—0
AutoML using Metadata Language EmbeddingsCode0
Analysis of an Automated Machine Learning Approach in Brain Predictive Modelling: A data-driven approach to Predict Brain Age from Cortical Anatomical Measures—0
Improved Training Speed, Accuracy, and Data Utilization via Loss Function Optimization—0
Neural Architecture Search for Class-incremental Learning—0
AutoML for Contextual Bandits—0
Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AI—0
Neuraxle - A Python Framework for Neat Machine Learning PipelinesCode0
Multi-Objective Automatic Machine Learning with AutoxgboostMC—0
A CNN toolbox for skin cancer classification—0
SCARLET-NAS: Bridging the Gap between Stability and Scalability in Weight-sharing Neural Architecture SearchCode0
Towards Automated Machine Learning: Evaluation and Comparison of AutoML Approaches and Tools—0
MoGA: Searching Beyond MobileNetV3Code0
Towards AutoML in the presence of Drift: first results—0
Techniques for Automated Machine Learning—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