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.

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A Distributional Approach To Controlled Text Generation

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The official code for PRIMER: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization

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Supplementary code for TISMIR paper "Sliding-Window Pitch-Class Histograms as a Means of Modeling Musical Form"

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Final report with code for KAIST Course KSE 801.

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MOpt-AFL provided by the paper "MOPT: Optimized Mutation Scheduling for Fuzzers"

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PyTorch code accompanying the paper "Landmark-Guided Subgoal Generation in Hierarchical Reinforcement Learning" (NeurIPS 2021).

HIGL This is a PyTorch implementation for our paper: Landmark-Guided Subgoal Generation in Hierarchical Reinforcement Learning (NeurIPS 2021). Our cod

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PyTorch code accompanying the paper "Landmark-Guided Subgoal Generation in Hierarchical Reinforcement Learning" (NeurIPS 2021).

HIGL This is a PyTorch implementation for our paper: Landmark-Guided Subgoal Generation in Hierarchical Reinforcement Learning (NeurIPS 2021). Our cod

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