Automated Machine Learning with scikit-learn
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Updated
Sep 3, 2024 - Python
Automated Machine Learning with scikit-learn
A PyTorch Library for Meta-learning Research
A curated list of Meta Learning papers, code, books, blogs, videos, datasets and other resources.
Learning to Learn using One-Shot Learning, MAML, Reptile, Meta-SGD and more with Tensorflow
Meta-Learning with Differentiable Convex Optimization (CVPR 2019 Oral)
PyTorch implementation of HyperNetworks (Ha et al., ICLR 2017) for ResNet (Residual Networks)
A PyTorch implementation of OpenAI's REPTILE algorithm
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"
Personalizing Dialogue Agents via Meta-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
Meta learning is a subfield of machine learning where automatic learning algorithms are applied on metadata about machine learning experiments.
Implementation of Jump-Start Reinforcement Learning (JSRL) with Stable Baselines3
DropClass and DropAdapt - repository for the paper accepted to Speaker Odyssey 2020
Taking causal inference to the extreme!
Generalizing to New Physical Systems via Context-Informed Dynamics Model
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