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A collection of extensions and data-loaders for few-shot learning & meta-learning in PyTorch
The main features of tristandeleu/pytorch-meta are: Automated Machine Learning, Meta Learning Libraries.
Projects with overlapping indexed features include: learnables/learn2learn — A PyTorch Library for Meta-learning Research. autogluon/autogluon — AutoGluon is an automated machine learning framework and multimodal library designed to automate the end-to-end… karpathy/autoresearch — Autoresearch is an autonomous machine learning research agent and architecture search framework. It employs a… google-deepmind/learning-to-learn — This project is a TensorFlow meta-learning framework and research toolkit designed to implement and train learned… ludwig-ai/ludwig — Ludwig is a multimodal machine learning platform and low-code framework designed for building, training, and deploying… bayesian-optimization/bayesianoptimization — This is a Bayesian optimization library for Python designed to find the maximum value of expensive black box…
A PyTorch Library for Meta-learning Research
AutoGluon is an automated machine learning framework and multimodal library designed to automate the end-to-end pipeline from data preprocessing to high-accuracy model training and validation. It functions as an automated model trainer for tabular, image, text, and time series data, as well as a tool for time series forecasting and foundation model finetuning. The project is distinguished by its ability to jointly process and fuse different data types, allowing for the construction of multimodal neural networks that integrate images, text, and structured tables. It supports zero-shot inferenc
Autoresearch is an autonomous machine learning research agent and architecture search framework. It employs a closed-loop system to programmatically rewrite training and architecture source code to discover optimal language model configurations. The system iteratively modifies code and evaluates performance metrics to improve model quality based on a target objective. It optimizes model performance and training efficiency by tracking validation bits per byte, which allows for a fair comparison of architectural changes independently of vocabulary size. The framework manages the full training
This project is a TensorFlow meta-learning framework and research toolkit designed to implement and train learned optimizers. It provides a library of tools for developing neural networks that learn how to optimize other models, replacing traditional gradient-based optimization algorithms. The framework includes a problem ensemble manager that allows multiple distinct optimization tasks to be combined into a single weighted loss function for simultaneous training. It uses a factory pattern for network instantiation and supports the definition of custom objective functions and loss graphs as t