Model-Agnostic Meta-Learning in PyTorch
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Updated
Jul 31, 2020 - Jupyter Notebook
Model-Agnostic Meta-Learning in PyTorch
Using LIME (Local Interpretable Model-Agnostic Explanations) for text data
An implementation of Model Agnostic Meta Learning (MAML) for few shot supervised image classification.
The code for magnification generalization for the histopathology image embedding
This repository stores scripts used to run COMASure and its extensions. The models are studied as part of the requirements for the MSc Data Science and Machine Learning dissertation at UCL.
Code for "Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks"
Code for the project "Exploring transferability and model agnostic meta learning across NLP Tasks". CS330 Deep Multi-Task and Meta Learning, Stanford University.
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