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.

Dilated Convolution with Learnable Spacings PyTorch

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This is a JAX implementation of Neural Radiance Fields for learning purposes.

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Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising

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Code and training data for our ECCV 2016 paper on Unsupervised Learning

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The official implementation of Equalization Loss for Long-Tailed Object Recognition (CVPR 2020) based on Detectron2

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Little tool in python to watch anime from the terminal (the better way to watch anime)

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Code release of paper "Deep Multi-View Stereo gone wild"

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