This repository contains the code for the paper: Utilizing the Untapped Potential of Indirect Encoding for Neural Networks with Meta Learning
An implementation of Model Agnostic Meta Learning (MAML) for few shot supervised image classification.
Prototypical Networks on Omniglot Dataset for Few-Shot Classification
Bio-inspired neural architectures research: differentiable local plasticity, sparse coding, and few-shot learning. Empirical study toward post-LLM cognitive architectures (Omniglot benchmark). Workshop paper in preparation.
Final Project from the course "Deep Learning" @ Data Science & Scientific Computing, University of Trieste, year 2020/2021, written in Python using PyTorch.
Few-shot learning of deep convolutional models
To associate your repository with the omniglot-dataset topic, visit your repo's landing page and select "manage topics."