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 601–641 of 641 papers

TitleStatusHype
Model LineUpper: Supporting Interactive Model Comparison at Multiple Levels for AutoML—0
MONCAE: Multi-Objective Neuroevolution of Convolutional Autoencoders—0
Multi-Agent Automated Machine Learning—0
Multi-Microgrid Collaborative Optimization Scheduling Using an Improved Multi-Agent Soft Actor-Critic Algorithm—0
Multimodal Representation Learning and Fusion—0
Multi-Objective Automatic Machine Learning with AutoxgboostMC—0
Multi-objective Evolutionary Search of Variable-length Composite Semantic Perturbations—0
Naive Automated Machine Learning -- A Late Baseline for AutoML—0
NAS-Bench-Suite: NAS Evaluation is (Now) Surprisingly Easy—0
NAS-Bench-Suite-Zero: Accelerating Research on Zero Cost Proxies—0
NASirt: AutoML based learning with instance-level complexity information—0
Network-Aware AutoML Framework for Software-Defined Sensor Networks—0
Neural Architecture Codesign for Fast Bragg Peak Analysis—0
Neural Architecture Search based on Cartesian Genetic Programming Coding Method—0
Neural Architecture Search for Class-incremental Learning—0
Neural Architecture Search for Deep Face Recognition—0
Neural Architecture Search for Inversion—0
OmniForce: On Human-Centered, Large Model Empowered and Cloud-Edge Collaborative AutoML System—0
Online AutoML: An adaptive AutoML framework for online learning—0
Online Meta-learning for AutoML in Real-time (OnMAR)—0
On Predictive Explanation of Data Anomalies—0
On the Hyperparameter Loss Landscapes of Machine Learning Models: An Exploratory Study—0
Optimising 4th-Order Runge-Kutta Methods: A Dynamic Heuristic Approach for Efficiency and Low Storage—0
Physical Informed-Inspired Deep Reinforcement Learning Based Bi-Level Programming for Microgrid Scheduling—0
Pipeline Combinators for Gradual AutoML—0
POPNASv3: a Pareto-Optimal Neural Architecture Search Solution for Image and Time Series Classification—0
Position: A Call to Action for a Human-Centered AutoML Paradigm—0
Practical and sample efficient zero-shot HPO—0
Privacy-preserving Online AutoML for Domain-Specific Face Detection—0
Privileged Zero-Shot AutoML—0
Problem-oriented AutoML in Clustering—0
Put CASH on Bandits: A Max K-Armed Problem for Automated Machine Learning—0
PyGlove: Symbolic Programming for Automated Machine Learning—0
Q(D)O-ES: Population-based Quality (Diversity) Optimisation for Post Hoc Ensemble Selection in AutoML—0
RandomNet: Towards Fully Automatic Neural Architecture Design for Multimodal Learning—0
Real-Time AutoML—0
Regularize, Expand and Compress: Multi-task based Lifelong Learning via NonExpansive AutoML—0
Replacing the Ex-Def Baseline in AutoML by Naive AutoML—0
Resource-Aware Pareto-Optimal Automated Machine Learning Platform—0
Reusing Trained Layers of Convolutional Neural Networks to Shorten Hyperparameters Tuning Time—0
Review of automated time series forecasting pipelines—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