Meta-meta-learning with evolution and plasticity

Overview

Meta-meta-learning with evolution and plasticity

This is the code for the arxiv preprint Learning to acquire novel cognitive tasks with evolution, plasticity and meta-meta-learning.

We evolve plastic networks to be able to automatically acquire novel cognitive (meta-learning) tasks, that were not seen during training.

The code is in the form of Jupyter notebooks that can be run on Google Colab. It is strongly recommended to consult the Simple notebook, which contains a simplified version of the code that should be easier to read through, while still producing the same results. The other notebook contains the full code that was actually used to run the experiments.

RODD: A Self-Supervised Approach for Robust Out-of-Distribution Detection

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An example of semantic segmentation using tensorflow in eager execution.

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Sandbox for training deep learning networks

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Compositional and Parameter-Efficient Representations for Large Knowledge Graphs

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Representing Long-Range Context for Graph Neural Networks with Global Attention

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Official Implementation of "LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks"

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