Artificial Intelligence > Machine Learning > Deep Learning

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A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learning. arXiv:2307.09218.

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)

This is a meta-model distilled from ChatGPT-3.5-turbo for information extraction. This is an intermediate checkpoint that can be well-transferred to all kinds of downstream information extraction tasks.

Python module to interface with OpenML

TorchOpt is an efficient library for differentiable optimization built upon PyTorch.

The official source code of the paper "Unsupervised Episode Generation for Graph Meta-learning" (to be presented in ICML 2024)

Meta-Transformer: A meta-learning framework for scalable automatic modulation classification

Ready-to-use code and tutorial notebooks to boost your way into few-shot learning for image classification.

FSL-Mate: A collection of resources for few-shot learning (FSL).

Presented at the 2022 IEEE Region 10 Conference (TENCON 2022). Our main contribution is twofold: (1) the construction of a meta-learning model for recommending a distance metric for k-means clustering and (2) a fine-grained analysis of the importance and effects of the meta-features on the model's output

LibFewShot: A Comprehensive Library for Few-shot Learning. TPAMI 2023.

Implementation of papers in 100 lines of code.

PyTorch-Lightning implementation of Meta Temporal Point Processes

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.

Datasets collection and preprocessings framework for NLP extreme multitask learning

[KDD 2023] Deep Pipeline Embeddings for AutoML

Reproducible material for Meta-Processing: A robust framework for multi-tasks seismic processing

Manipulating Python Programs