Implementation of Meta-RL A3C algorithm
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Updated
Feb 22, 2017 - Jupyter Notebook
Implementation of Meta-RL A3C algorithm
My personal toolkit for PyTorch development.
MfeatExtractor is an automated code for meta-feature extraction, useful for meta-learning projects.
This repository implements the paper, Model-Agnostic Meta-Leanring for Fast Adaptation of Deep Networks.
AI masterthesis. Iterative learning. Meta learning. Stochastic approach to adaptive computation
implementation of relationNet naive version
We built an optimization technique that, at each learning step, automatically learns which best learning rate to use for gradient descent.
What if you could make another You?
Pytorch code for Arxiv Paper: Learning to learn: Meta-Critic Networks for Sample-Efficient Learning
Code used for experiments in the ICPR 2018 paper "Classifier Recommendation Using Data Complexity Measures"
[CVPR'17] Training a Correlation Filter end-to-end allows lightweight networks of 2 layers (600 kB) to high performance at fast speed..
Literature on learning from small amount of labeled data
Experiments with Meta-Learning in search for beautiful minima
Resample, parameter tuning, meta-learning, clustering, and mining algorithms for the purpose of data mining and machine learning.
Implementation of MetaQNN (https://arxiv.org/abs/1611.02167, https://github.com/bowenbaker/metaqnn.git) with Additions and Modifications in PyTorch for Image Classification
Implementation of MetaQNN (https://arxiv.org/abs/1611.02167, https://github.com/bowenbaker/metaqnn.git) with Additions and Modifications in PyTorch for Image Generation with GAN where the Discriminator network is fixed and same as that in the infoGAN paper (https://arxiv.org/abs/1606.03657)
Implementation of MetaQNN (https://arxiv.org/abs/1611.02167, https://github.com/bowenbaker/metaqnn.git) with Additions and Modifications in PyTorch for Image Generation with Asymmetric Variational Autoencoders
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