Topic: meta-learning Goto Github
Some thing interesting about meta-learning
Some thing interesting about meta-learning
meta-learning,Automated Machine Learning with scikit-learn
Organization: automl
Home Page: https://automl.github.io/auto-sklearn
meta-learning,Official pytorch implementation of the paper "Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels" (NeurIPS 2020)
Organization: bayeswatch
Home Page: https://arxiv.org/abs/1910.05199
meta-learning,[CVPR'17] Training a Correlation Filter end-to-end allows lightweight networks of 2 layers (600 kB) to high performance at fast speed..
User: bertinetto
meta-learning,Meta Self-learning for Multi-Source Domain Adaptation: A Benchmark
User: bupt-ai-cz
meta-learning,Implementations of many meta-learning algorithms to solve the few-shot learning problem in Pytorch
User: cnguyen10
meta-learning,Collection for Few-shot Learning
User: duan-jm
meta-learning,A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learning. arXiv:2307.09218.
User: ennengyang
Home Page: https://arxiv.org/abs/2307.09218
meta-learning,PyTorch code for CVPR 2018 paper: Learning to Compare: Relation Network for Few-Shot Learning (Few-Shot Learning part)
User: floodsung
meta-learning,Meta Learning / Learning to Learn / One Shot Learning / Few Shot Learning
User: floodsung
meta-learning,A PyTorch implementation of Model Agnostic Meta-Learning (MAML) that faithfully reproduces the results from the original paper.
User: fmu2
meta-learning,This repo provides pytorch code which replicates the results of the Matching Networks for One Shot Learning paper on the Omniglot and MiniImageNet dataset
User: gitabcworld
meta-learning,A dataset of datasets for learning to learn from few examples
Organization: google-research
meta-learning,Manipulating Python Programs
Organization: google
meta-learning,A list of high-quality (newest) AutoML works and lightweight models including 1.) Neural Architecture Search, 2.) Lightweight Structures, 3.) Model Compression, Quantization and Acceleration, 4.) Hyperparameter Optimization, 5.) Automated Feature Engineering.
User: guan-yuan
meta-learning,A list of recent papers about Meta / few-shot learning methods applied in NLP areas.
User: ha-lins
meta-learning,Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformation (ICLR 2020 spotlight)
User: hytseng0509
meta-learning,Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习
User: jindongwang
Home Page: http://transferlearning.xyz/
meta-learning,Implementation of Proximal Meta-Policy Search (ProMP) as well as related Meta-RL algorithm. Includes a useful experiment framework for Meta-RL.
User: jonasrothfuss
Home Page: https://sites.google.com/view/pro-mp
meta-learning,Meta-Transfer Learning for Zero-Shot Super-Resolution (CVPR, 2020)
User: jwsoh
meta-learning,Official implementation of Meta-StyleSpeech and StyleSpeech
User: kevinmin95
meta-learning,PyTorch Implementations For A Series Of Deep Learning-Based Recommendation Models
User: khanhnamle1994
meta-learning,Meta-Learning with Differentiable Convex Optimization (CVPR 2019 Oral)
User: kjunelee
meta-learning,A PyTorch Library for Meta-learning Research
Organization: learnables
Home Page: http://learn2learn.net
meta-learning,Betty: an automatic differentiation library for generalized meta-learning and multilevel optimization
Organization: leopard-ai
Home Page: https://leopard-ai.github.io/betty/
meta-learning,Implementation of papers in 100 lines of code.
User: maximevandegar
meta-learning,TorchOpt is an efficient library for differentiable optimization built upon PyTorch.
Organization: metaopt
Home Page: https://torchopt.readthedocs.io
meta-learning,Awesome Multitask Learning Resources
Organization: mlsys-mbs0221
meta-learning,A resource list for causality in statistics, data science and physics
User: msuzen
meta-learning,Reinforcement Learning Tutorial with Demo: DP (Policy and Value Iteration), Monte Carlo, TD Learning (SARSA, QLearning), Function Approximation, Policy Gradient, DQN, Imitation, Meta Learning, Papers, Courses, etc..
User: omerbsezer
meta-learning,A classified list of meta learning papers based on realm.
User: onehuster
meta-learning,Code for the paper "Evolved Policy Gradients"
Organization: openai
Home Page: https://arxiv.org/abs/1802.04821
meta-learning,Python module to interface with OpenML
Organization: openml
Home Page: https://openml.github.io/openml-python/main/
meta-learning,Repository for few-shot learning machine learning projects
User: oscarknagg
meta-learning,Collection of Reinforcement Learning / Meta Reinforcement Learning Environments.
Organization: paddlepaddle
meta-learning,Knowledge-Aware machine LEarning (KALE): accessible machine learning from multiple sources for interdisciplinary research, part of the 🔥PyTorch ecosystem. ⭐ Star to support our work!
Organization: pykale
Home Page: https://pykale.github.io/
meta-learning,LibFewShot: A Comprehensive Library for Few-shot Learning. TPAMI 2023.
Organization: rl-vig
meta-learning,PyTorch code for Learning Deep Time-index Models for Time Series Forecasting (ICML 2023)
Organization: salesforce
meta-learning,The code repository for "Few-Shot Learning via Embedding Adaptation with Set-to-Set Functions"
Organization: sha-lab
meta-learning,Ready-to-use code and tutorial notebooks to boost your way into few-shot learning for image classification.
Organization: sicara
meta-learning,FSL-Mate: A collection of resources for few-shot learning (FSL).
User: tata1661
meta-learning,Code for "One-Shot Visual Imitation Learning via Meta-Learning"
User: tianheyu927
Home Page: https://sites.google.com/view/one-shot-imitation
meta-learning,A collection of extensions and data-loaders for few-shot learning & meta-learning in PyTorch
User: tristandeleu
Home Page: https://tristandeleu.github.io/pytorch-meta/
meta-learning,Great resources for making Reinforcement Learning work in Real Life situations. Papers,projects and more.
User: ugurkanates
meta-learning,Open-L2O: A Comprehensive and Reproducible Benchmark for Learning to Optimize Algorithms
Organization: vita-group
meta-learning,All materials you need for Federated Learning: blogs, videos, papers, and softwares, etc.
User: weimingwill
Home Page: https://github.com/EasyFL-AI/EasyFL
meta-learning,NeurIPS'19: Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting (Pytorch implementation for noisy labels).
User: xjtushujun
meta-learning,Code for the Neural Processes website and replication of 4 papers on NPs. Pytorch implementation.
User: yanndubs
Home Page: https://yanndubs.github.io/Neural-Process-Family/
meta-learning,TensorFlow and PyTorch implementation of "Meta-Transfer Learning for Few-Shot Learning" (CVPR2019)
User: yaoyao-liu
Home Page: https://lyy.mpi-inf.mpg.de/mtl/
meta-learning,Tools for generating mini-ImageNet dataset and processing batches
User: yaoyao-liu
Home Page: https://mtl.yyliu.net/datasets/
meta-learning,[T-PAMI 2022] Meta-DETR for Few-Shot Object Detection: Official PyTorch Implementation
User: zhanggongjie
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.