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 451–475 of 641 papers

TitleStatusHype
Designing Machine Learning Pipeline Toolkit for AutoML Surrogate Modeling Optimization—0
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
A multi-objective perspective on jointly tuning hardware and hyperparameters—0
MONCAE: Multi-Objective Neuroevolution of Convolutional Autoencoders—0
Tabular Data: Deep Learning is Not All You NeedCode0
Incorporating domain knowledge into neural-guided search via in situ priors and constraints—0
Sequential Automated Machine Learning: Bandits-driven Exploration using a Collaborative Filtering Representation—0
Towards Model Selection using Learning Curve Cross-ValidationCode0
AutoML Adoption in ML Software—0
Replacing the Ex-Def Baseline in AutoML by Naive AutoML—0
Automating Data Science: Prospects and Challenges—0
Chameleon: A Semi-AutoML framework targeting quick and scalable development and deployment of production-ready ML systems for SMEs—0
Model LineUpper: Supporting Interactive Model Comparison at Multiple Levels for AutoML—0
How Powerful are Performance Predictors in Neural Architecture Search?—0
Bit-Mixer: Mixed-precision networks with runtime bit-width selection—0
Naive Automated Machine Learning -- A Late Baseline for AutoML—0
Metalearning Using Structure-rich Pipeline Representations for Better AutoML—0
Neural Architecture Search based on Cartesian Genetic Programming Coding Method—0
An Automated Machine Learning (AutoML) Method for Driving Distraction Detection Based on Lane-Keeping Performance—0
Improving Neural Networks for Time Series Forecasting using Data Augmentation and AutoMLCode0
Automated Creative Optimization for E-Commerce AdvertisingCode0
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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