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How to Train MAML(Model-Agnostic Meta-Learning)
Author(s): Sherwin Chen Originally published on Towards AI. An elaborate explanation for MAML and more Top highlight Source: Pixabay Model-Agnostic Meta-Learning(MAML) has been growing more and more popular in the field of meta-learning since it’s first introduced by Finn et al. in …
PEARL: Probabilistic Embeddings for Actor-Critic RL
Author(s): Sherwin Chen Originally published on Towards AI. A sample-efficient meta reinforcement learning method Top highlight Source: Unsplash Meta reinforcement learning could be particularly challenging because the agent has to not only adapt to the new incoming data but also find an …
Meta-Learning in NLP Classification
Author(s): Edward Ma Originally published on Towards AI. Learning to learn Unlike well-known dataset, our real life problem domain always only have small labeled dataset while we may not able to train a good model under this scenario. Data augmentation is one …
Meta-Learning in Dialog Generation
Author(s): Edward Ma Originally published on Towards AI. Learning to learn Unlike a well-known dataset, our real life problem domain always only have small labeled dataset while we may not able to train a good model under this scenario. Data augmentation is …
A Gentle Introduction to Meta-Learning
Author(s): Edward Ma Originally published on Towards AI. Learning to learn Photo by Edward Ma on Unsplash Human begins can learn new things in a few examples, while deep learning thus far is data-hungry. To have a good performance model, millions, or …
Introduction
Author(s): Sherwin Chen Originally published on Towards AI. A climbing snail trying to see the outside world U+007C Source: Pinterest Diving Into SNAIL U+007C Towards AI A Simple Neural Attentive Meta-Learner — SNAIL Traditional reinforcement learning algorithms train an agent to solve …
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