#
maml
Here are 43 public repositories matching this topic...
Learning to Learn using One-Shot Learning, MAML, Reptile, Meta-SGD and more with Tensorflow
reinforcement-learning
tensorflow
keras
one-shot-learning
reptile
maml
mann
zero-shot-learning
ntm
shot-learning
siamese-network
relation-network
metalearning
few-shot-learning
prototypical-networks
meta-sgd
matching-networks
deep-meta-learning
meta-imitation-learning
prototypical-network
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Apr 17, 2020 - Jupyter Notebook
Repository for few-shot learning machine learning projects
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Updated
Nov 25, 2019 - Python
A dataset of datasets for learning to learn from few examples
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Aug 29, 2020 - Python
Personalizing Dialogue Agents via Meta-Learning
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Oct 6, 2019 - Jupyter Notebook
Memory efficient MAML using gradient checkpointing
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Dec 30, 2019 - Jupyter Notebook
TensorFlow 2.0 implementation of MAML.
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Jul 12, 2019 - Jupyter Notebook
Tools for building raster processing and display services
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Aug 21, 2020 - Scala
Source code for KDD 2020 paper "Meta-learning on Heterogeneous Information Networks for Cold-start Recommendation"
cold-start
recommender-systems
maml
meta-learning
heterogeneous-information-networks
heterogeneous-graph
kdd2020
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Jul 28, 2020 - Jupyter Notebook
Meta learning with BERT as a learner
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Jan 15, 2020 - Python
A collection of Gradient-Based Meta-Learning Algorithms with pytorch
pytorch
reptile
maml
meta-learning
few-shot-learning
meta-learning-algorithms
gradient-based-meta-learning
cavia
implicit-maml
neumann-approximation
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Dec 9, 2019 - Python
This repository contains the implementation for the paper - Exploration via Hierarchical Meta Reinforcement Learning.
machine-learning
algorithm
meta
reinforcement-learning
deep-learning
robotics
deep-reinforcement-learning
openai-gym
pytorch
openai
gym
exploration
rl
hierarchical
maml
mujoco
mujoco-py
metalearning
maml-rl
rlkit
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May 6, 2019 - Python
My notes and assignment solutions for Stanford CS330 (Fall 2019) Deep Multi-Task and Meta Learning
deep-learning
tensorflow
pytorch
stanford
maml
mann
meta-learning
protonet
multitask-learning
cs330
deep-multi-task
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Aug 10, 2020 - Jupyter Notebook
This repository implements the paper, Model-Agnostic Meta-Leanring for Fast Adaptation of Deep Networks.
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Nov 3, 2017 - Python
Source code for CVPR 2020 paper "Learning to Forget for Meta-Learning"
machine-learning
reinforcement-learning
computer-vision
deep-learning
pytorch
maml
meta-learning
few-shot-learning
cvpr2020
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Jul 7, 2020 - Python
Deepest Season 6 Meta-Learning study papers plus alpha
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Updated
Mar 4, 2020
Meta-learning model agnostic (MAML) implementation for cross-accented ASR
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Aug 12, 2020 - Python
Implementation of Model-Agnostic Meta-Learning (MAML) applied on Reinforcement Learning problems in TensorFlow 2.
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Dec 20, 2019 - Python
Few Shot Regression of Periodic and Basic Polynomial Functions using MAML and Reptile Method
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Dec 15, 2019 - Python
Code for meta-learning initializations for image segmentation
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Jun 18, 2020 - Python
Meta-learning by applying MAML to an inner variational auto-encoder to automatically learn generative models with few examples
machine-learning
deep-learning
neural-network
tensorflow
vae
one-shot-learning
maml
generative-models
metalearning
few-shot-learning
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Jan 16, 2019 - Python
Meta-Learning for Generalized Zero-Shot Learning
generative-adversarial-network
gans
maml
zero-shot-learning
domain-adaptation
meta-learning
few-shot-learning
zsl
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Jul 1, 2020 - Jupyter Notebook
Homoiconic C - a universal data format for computation
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Jul 24, 2020 - TypeScript
Robot Navigation in Crowds via Meta-learning
robotics
navigation
deep-reinforcement-learning
maml
trajectory-prediction
social-awareness
pedestrian-behavior
maml-rl
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Mar 24, 2019 - C++
This repo contains implementations of the challenges from fellowship.ai. For more, visit here.
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Jul 11, 2019 - Jupyter Notebook
CommServer software family - management of the migration to open source.
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May 2, 2020
A MAML-format help generator for binary PowerShell modules.
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Dec 27, 2017 - C#
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Dec 4, 2019 - Python
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As suggested on Reddit, it would be nice to have more tutorials.
A simple idea is to base them on our existing examples. The tutorial could explain how each implemented method works