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 451–500 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
Interpret-able feedback for AutoML systems—0
An AutoML-based Approach to Multimodal Image Sentiment Analysis—0
Leveraging Benchmarking Data for Informed One-Shot Dynamic Algorithm Selection—0
Data Analytics and Machine Learning Methods, Techniques and Tool for Model-Driven Engineering of Smart IoT Services—0
Dancing along Battery: Enabling Transformer with Run-time Reconfigurability on Mobile Devices—0
Incremental Search Space Construction for Machine Learning Pipeline Synthesis—0
PyGlove: Symbolic Programming for Automated Machine Learning—0
JITuNE: Just-In-Time Hyperparameter Tuning for Network Embedding Algorithms—0
Robusta: Robust AutoML for Feature Selection via Reinforcement Learning—0
A Neophyte With AutoML: Evaluating the Promises of Automatic Machine Learning ToolsCode0
AutoDS: Towards Human-Centered Automation of Data Science—0
Whither AutoML? Understanding the Role of Automation in Machine Learning Workflows—0
Fits and Starts: Enterprise Use of AutoML and the Role of Humans in the Loop—0
Application of an automated machine learning-genetic algorithm (AutoML-GA) coupled with computational fluid dynamics simulations for rapid engine design optimization—0
How Much Automation Does a Data Scientist Want?—0
Hyperboost: Hyperparameter Optimization by Gradient Boosting surrogate models—0
ECONOMIC HYPERPARAMETER OPTIMIZATION WITH BLENDED SEARCH STRATEGY—0
Real-Time AutoML—0
DiffAutoML: Differentiable Joint Optimization for Efficient End-to-End Automated Machine Learning—0
Joint Search of Data Augmentation Policies and Network Architectures—0
Amazon SageMaker Autopilot: a white box AutoML solution at scale—0
Ensemble Squared: A Meta AutoML System—0
Efficient Automatic CASH via Rising Bandits—0
Leveraging Automated Machine Learning for Text Classification: Evaluation of AutoML Tools and Comparison with Human Performance—0
Automatic Discovery of Heterogeneous Machine Learning Pipelines: An Application to Natural Language Processing—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