AutoML

233 papers with code • 2 benchmarks • 7 datasets

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

Libraries

Use these libraries to find AutoML models and implementations
14 papers
137
5 papers
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4 papers
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Latest papers with no code

Neural Architecture Search for Sentence Classification with BERT

no code yet • 27 Mar 2024

Pre training of language models on large text corpora is common practice in Natural Language Processing.

Deciphering AutoML Ensembles: cattleia's Assistance in Decision-Making

no code yet • 19 Mar 2024

In many applications, model ensembling proves to be better than a single predictive model.

Automated Contrastive Learning Strategy Search for Time Series

no code yet • 19 Mar 2024

In recent years, Contrastive Learning (CL) has become a predominant representation learning paradigm for time series.

Automated data processing and feature engineering for deep learning and big data applications: a survey

no code yet • 18 Mar 2024

In addition to automating specific data processing tasks, we discuss the use of AutoML methods and tools to simultaneously optimize all stages of the machine learning pipeline.

LLM Guided Evolution - The Automation of Models Advancing Models

no code yet • 18 Mar 2024

GE leverages LLMs for a more intelligent, supervised evolutionary process, guiding mutations and crossovers.

Data augmentation with automated machine learning: approaches and performance comparison with classical data augmentation methods

no code yet • 13 Mar 2024

Finally, we carried out an extensive comparison and analysis of the performance of automated data augmentation techniques and state-of-the-art methods based on classical augmentation approaches.

Evolving machine learning workflows through interactive AutoML

no code yet • 28 Feb 2024

In this paper we present \ourmethod, an interactive G3P algorithm that allows users to dynamically modify the grammar to prune the search space and focus on their regions of interest.

Automated Machine Learning for Multi-Label Classification

no code yet • 28 Feb 2024

Automated machine learning (AutoML) aims to select and configure machine learning algorithms and combine them into machine learning pipelines tailored to a dataset at hand.

AutoMMLab: Automatically Generating Deployable Models from Language Instructions for Computer Vision Tasks

no code yet • 23 Feb 2024

Automated machine learning (AutoML) is a collection of techniques designed to automate the machine learning development process.

Data Pipeline Training: Integrating AutoML to Optimize the Data Flow of Machine Learning Models

no code yet • 20 Feb 2024

With the increasing diversification and complexity of Data sources, as well as the rapid growth of data volumes, building an efficient Data Pipeline has become crucial for improving work efficiency and solving complex problems.