An index of algorithms for few-shot learning/meta-learning on graphs
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Updated
Jun 4, 2024
An index of algorithms for few-shot learning/meta-learning on graphs
定时获取谷歌学术和arxiv论文的相关更新 (代码只有一个py文件,较简单有注释)
🎓 Automatically Update Some Fields Papers Daily using Github Actions (Update Every 12th hours)
Manipulating Python Programs
NAACL '24 (Demo) / MlSys @ NeurIPS '23 - RedCoast: A Lightweight Tool to Automate Distributed Training and Inference
A framework for composing Neural Processes in Python
Artificial Intelligence > Machine Learning > Deep Learning
Datasets collection and preprocessings framework for NLP extreme multitask learning
A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learning. arXiv:2307.09218.
Knowledge-Aware machine LEarning (KALE): accessible machine learning from multiple sources for interdisciplinary research, part of the 🔥PyTorch ecosystem. ⭐ Star to support our work!
The ORBIT dataset is a collection of videos of objects in clean and cluttered scenes recorded by people who are blind/low-vision on a mobile phone. The dataset is presented with a teachable object recognition benchmark task which aims to drive few-shot learning on challenging real-world data.
Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习
This is a meta-model distilled from LLMs for information extraction. This is an intermediate checkpoint that can be well-transferred to all kinds of downstream information extraction tasks.
TorchOpt is an efficient library for differentiable optimization built upon PyTorch.
A dataset of datasets for learning to learn from few examples
A new comprehensive and diverse few-shot acoustic classification benchmark.
Reproducible material for Meta-Processing: A robust framework for multi-tasks seismic processing
The official source code of the paper "Unsupervised Episode Generation for Graph Meta-learning" (to be presented in ICML 2024)
Framework for meta-optimisation in AutoML tasks
Abstract thinking patterns and problem decomposition / solving strategies
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