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.

Set of models for classifcation of 3D volumes

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Gym environments used in the paper: "Developmental Reinforcement Learning of Control Policy of a Quadcopter UAV with Thrust Vectoring Rotors"

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code for paper -- "Seamless Satellite-image Synthesis"

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KDD CUP 2020 Automatic Graph Representation Learning: 1st Place Solution

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Custom IMDB Dataset is extracted between 2020-2021 and custom distilBERT model is trained for movie success probability prediction

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The official implementation for "FQ-ViT: Fully Quantized Vision Transformer without Retraining".

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MinkLoc++: Lidar and Monocular Image Fusion for Place Recognition

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Real-time pose estimation accelerated with NVIDIA TensorRT

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Flickr-Faces-HQ (FFHQ) is a high-quality image dataset of human faces, originally created as a benchmark for generative adversarial networks (GAN)

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(NeurIPS 2021) Realistic Evaluation of Transductive Few-Shot Learning

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Deep Learning GPU Training System

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A general, feasible, and extensible framework for classification tasks.

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