Architecture search using unsupervised learning with symmetric auto-encoders and QLearning in PyTorch
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Updated
Feb 24, 2018 - Python
Architecture search using unsupervised learning with symmetric auto-encoders and QLearning in PyTorch
Learning Dynamic Treatment Regime (DTR) via meta-learners
The objective of this report is to conduct a study on the images of dogs and classify the emotions of each dog using deep learning algorithms, and to provide an new approach to solve this particular classification problem
Image classification with very few data sample (n=25 per class)
Towards Improved Meta-Learned Optimizers: Investigating the effect of L2-regularization on learned meta-optimizers. Research done for the AML course in Fall 2021/2022.
A repository that applies the Meta-SGD algorithm to High Energy Physics in Signal vs Background classification and studies the applicability of such methods.
Slime Mold swarm optimization - efficient derivative free biologically inspired solver
Elegant PyTorch implementation of paper Model-Agnostic Meta-Learning (MAML)
Latent graphs (reward landscapes). Can humans learn them? How?
A repository contains research papers related to RL, NLP, CV, ML, DL, Meta Learning, Incremental, etc.
A project implementing better evaluation scenarios for community models for malicious content detection, and meta-learning GNNs to achieve better downstream adaptation.
This project consists of the classification of brain's signals by using AI (meta learning) in order to developing brain- computer interface
Version 2 of Reconstruction-Style
Experimenting with different ways of calculating metafeatures and embedding datasets
XCS224U - Winter 2021 - Syed/Aiswarya - Intent Classification - Few Shot - Code and Dataset
A simple implementation for creating a Meta-learning rule using spatial statistical parameters of a dataset
Uncertainty-Guided Online Test-time Adaptation via Meta-Learning
This code is for the honour thesis developed by Dannong Xu. It includes CTNet (developed algorithm in the thesis), Siamese Network, MAML, and Reptile.
Code for "Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks"
I'm now interested in Learning to Adapt to Domain Shift
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