AutoML
235 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
Benchmarks
These leaderboards are used to track progress in AutoML
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Libraries
Use these libraries to find AutoML models and implementations
NAS-Bench-101
MedMNIST
Chalearn-AutoML-1
Latest papers
Integrating Hyperparameter Search into Model-Free AutoML with Context-Free Grammars
This is very important for the practice of machine learning, as it allows building strong baselines quickly, improving the efficiency of the data scientists, and reducing the time to production.
Neural Architecture Search for Sentence Classification with BERT
themody/nasforsentenceembeddingheads
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Pre training of language models on large text corpora is common practice in Natural Language Processing.
Robustifying and Boosting Training-Free Neural Architecture Search
hzf1174/robot
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Nevertheless, the estimation ability of these metrics typically varies across different tasks, making it challenging to achieve robust and consistently good search performance on diverse tasks with only a single training-free metric.
Principled Architecture-aware Scaling of Hyperparameters
vita-group/principled_scaling_lr_init
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However, most designs or optimization methods are agnostic to the choice of network structures, and thus largely ignore the impact of neural architectures on hyperparameters.
HyperFast: Instant Classification for Tabular Data
Training deep learning models and performing hyperparameter tuning can be computationally demanding and time-consuming.
MobileVLM V2: Faster and Stronger Baseline for Vision Language Model
We introduce MobileVLM V2, a family of significantly improved vision language models upon MobileVLM, which proves that a delicate orchestration of novel architectural design, an improved training scheme tailored for mobile VLMs, and rich high-quality dataset curation can substantially benefit VLMs' performance.
The Potential of AutoML for Recommender Systems
We found that AutoML and AutoRecSys libraries performed best.
MobileVLM : A Fast, Strong and Open Vision Language Assistant for Mobile Devices
We present MobileVLM, a competent multimodal vision language model (MMVLM) targeted to run on mobile devices.
AutoXPCR: Automated Multi-Objective Model Selection for Time Series Forecasting
Our method clearly outperforms other model selection approaches - on average, it only requires 20% of computation costs for recommending models with 90% of the best-possible quality.
auto-sktime: Automated Time Series Forecasting
The framework employs Bayesian optimization, to automatically construct pipelines from statistical, machine learning (ML) and deep neural network (DNN) models.
