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 551–600 of 641 papers

TitleStatusHype
AutoML using Metadata Language EmbeddingsCode0
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
Once-for-All: Train One Network and Specialize it for Efficient DeploymentCode1
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
AutoML: A Survey of the State-of-the-ArtCode1
Towards AutoML in the presence of Drift: first results—0
MixConv: Mixed Depthwise Convolutional KernelsCode1
Techniques for Automated Machine Learning—0
Automated Machine Learning in Practice: State of the Art and Recent Results—0
ShrinkML: End-to-End ASR Model Compression Using Reinforcement Learning—0
Visus: An Interactive System for Automatic Machine Learning Model Building and Curation—0
Transfer Learning for Risk Classification of Social Media Posts: Model Evaluation StudyCode0
Encoding high-cardinality string categorical variablesCode0
AutoML Strategy Based on Grammatical Evolution: A Case Study about Knowledge Discovery from Text—0
Single-Path Mobile AutoML: Efficient ConvNet Design and NAS Hyperparameter OptimizationCode0
Two-stage Optimization for Machine Learning WorkflowCode0
An Open Source AutoML Benchmark—0
Efficient Neural Interaction Function Search for Collaborative FilteringCode0
Meta-learning of textual representationsCode0
Automated Machine Learning: State-of-The-Art and Open ChallengesCode0
Approximation capability of neural networks on spaces of probability measures and tree-structured domains—0
Automated Machine Learning with Monte-Carlo Tree SearchCode0
Transferable AutoML by Model Sharing Over Grouped Datasets—0
Cascaded Algorithm-Selection and Hyper-Parameter Optimization with Extreme-Region Upper Confidence Bound Bandit—0
DDPNAS: Efficient Neural Architecture Search via Dynamic Distribution PruningCode0
Improved Training Speed, Accuracy, and Data Utilization Through Loss Function OptimizationCode0
Automatic Machine Learning by Pipeline Synthesis using Model-Based Reinforcement Learning and a Grammar—0
The Machine Learning Bazaar: Harnessing the ML Ecosystem for Effective System DevelopmentCode0
Analysis of the AutoML Challenge Series 2015–2018—0
Towards Automatically-Tuned Deep Neural NetworksCode2
AutoDispNet: Improving Disparity Estimation With AutoMLCode0
AM-LFS: AutoML for Loss Function SearchCode0
An ADMM Based Framework for AutoML Pipeline Configuration—0
Approximation capability of neural networks on sets of probability measures and tree-structured data—0
Benchmark and Survey of Automated Machine Learning FrameworksCode0
AutoSF: Searching Scoring Functions for Knowledge Graph EmbeddingCode1
DeepFreak: Learning Crystallography Diffraction Patterns with Automated Machine LearningCode0
CascadeML: An Automatic Neural Network Architecture Evolution and Training Algorithm for Multi-label Classification—0
Neural Architecture Search for Deep Face Recognition—0
Adaptive Bayesian Linear Regression for Automated Machine Learning—0
MetaPruning: Meta Learning for Automatic Neural Network Channel PruningCode1
Regularize, Expand and Compress: Multi-task based Lifelong Learning via NonExpansive AutoML—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