Model-Agnostic Meta-Learning in PyTorch

Proximal Policy Optimization with Model-Agnostic Meta-Learning for Battery Energy Storage System Management in a Multi-Microgrid

Project for Deep Learning And Applied AI course at the University of "La Sapienza" in Master in Computer Science A.A. 2021/2022

Clean implementation of "Model-Agnostic Meta-Learning" in PyTorch using Facebook's Higher.

Fake News detection based on the FA-KES dataset

Model Agnostic Meta Learning in TensorFlow 2.1

Determine feasible grasp positions and orientations using a spherically transformed dataset.

Estimación no invasiva de presión arterial con CNNs y Meta-Learning (MAML) sobre señales PPG/ECG — Proyecto Final de Bioingeniería, UNER.

MAML model finished by Tensorflow 2.2

Machine learning model for aviation accident risk prediction using ACAS and air traffic complexity data. Includes ONNX model export for deployment.

A production-ready adaptive meta-learning framework for continuous self-improvement. airbornehrs (MirrorMind) is a lightweight PyTorch framework that turns standard deep learning models into self-improving systems.

This is pedagogical implementation of MAML Algorithm.

Higher-order gradients in PyTorch, Parallelized

An implementation of Model Agnostic Meta Learning (MAML) algorithm using pytorch

A CNN-based MAML retains the standard principles of Model-Agnostic Meta-Learning (MAML) but integrates convolutional neural networks (CNNs) as the base architecture for feature extraction and adaptation.

Deep Learning Midterm Project