Automated Machine Learning with scikit-learn
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Updated
Sep 9, 2024 - Python
Automated Machine Learning with scikit-learn
A PyTorch Library for Meta-learning Research
Meta-Learning with Differentiable Convex Optimization (CVPR 2019 Oral)
PyTorch implementation of HyperNetworks (Ha et al., ICLR 2017) for ResNet (Residual Networks)
Faster and elegant TensorFlow Implementation of paper: Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Code for the NeurIPS19 paper "Meta-Learning Representations for Continual Learning"
Python Meta-Feature Extractor package.
This repository contains the implementation for the paper - Exploration via Hierarchical Meta Reinforcement Learning.
Tensorflow implementation of Synthetic Gradient for RNN (LSTM)
MetaTS | Time Series Forecasting using Meta Learning
Implementation of Jump-Start Reinforcement Learning (JSRL) with Stable Baselines3
DropClass and DropAdapt - repository for the paper accepted to Speaker Odyssey 2020
Generalizing to New Physical Systems via Context-Informed Dynamics Model
Implementation of SNAIL(A Simple Neural Attentive Meta-Learner) with Gluon
[NeurIPS 2021 | AIJ 2024] Multi-Objective Meta Learning
Meta-learning by applying MAML to an inner variational auto-encoder to automatically learn generative models with few examples
Model-Agnostic Meta-Learning for HDR Image Reconstruction. By learning the common structure between all LDR-to-HDR conversion tasks, our model is able to adapt it's predictions given extra exposures of a scene. This novel approach reframes LDR-to-HDR conversion as a meta-learning problem.
Meta-Padawan solution to the NeurIPS (2021) - Few-shot learning competition.
Code Repository for "Neural networks embrace diversity" paper
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