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Awesome-machine-unlearning Alternatives
Similar projects and alternatives to awesome-machine-unlearning based on common topics and language
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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continual-pretraining-nlp-vision
Code to reproduce experiments from the paper "Continual Pre-Training Mitigates Forgetting in Language and Vision" https://arxiv.org/abs/2205.09357
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NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better awesome-machine-unlearning alternative or higher similarity.
awesome-machine-unlearning reviews and mentions
Posts with mentions or reviews of awesome-machine-unlearning.
We have used some of these posts to build our list of alternatives
and similar projects.
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[P] [R] Machine Unlearning Summary
Github Repo: https://github.com/tamlhp/awesome-machine-unlearning 📚 Notebook: https://www.kaggle.com/code/tamlhp/machine-unlearning-the-right-to-be-forgotten/
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[R] A Survey of Machine Unlearning
Today, computer systems hold large amounts of personal data. Yet while such an abundance of data allows breakthroughs in artificial intelligence, and especially machine learning (ML), its existence can be a threat to user privacy, and it can weaken the bonds of trust between humans and AI. Recent regulations now require that, on request, private information about a user must be removed from both computer systems and from ML models, i.e. ``the right to be forgotten''). While removing data from back-end databases should be straightforward, it is not sufficient in the AI context as ML models often `remember' the old data. Contemporary adversarial attacks on trained models have proven that we can learn whether an instance or an attribute belonged to the training data. This phenomenon calls for a new paradigm, namely machine unlearning, to make ML models forget about particular data. It turns out that recent works on machine unlearning have not been able to completely solve the problem due to the lack of common frameworks and resources. Therefore, this paper aspires to present a comprehensive examination of machine unlearning's concepts, scenarios, methods, and applications. Specifically, as a category collection of cutting-edge studies, the intention behind this article is to serve as a comprehensive resource for researchers and practitioners seeking an introduction to machine unlearning and its formulations, design criteria, removal requests, algorithms, and applications. In addition, we aim to highlight the key findings, current trends, and new research areas that have not yet featured the use of machine unlearning but could benefit greatly from it. We hope this survey serves as a valuable resource for ML researchers and those seeking to innovate privacy technologies. Our resources are publicly available at this https URL.
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Welcome!
Welcome to Machine unlearning, You can post all kinds of stuff about Machine unlearning here . Here is a great resource to get you started https://github.com/tamlhp/awesome-machine-unlearning
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[P] [R] [D] Can Machine Actually Forget Your Data?
We also have a Github repo for this topic, please consider star if this topic piques your curiosity.
- [P] Awesome Machine Unlearning
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A note from our sponsor - SaaSHub
www.saashub.com | 2 May 2024
Stats
Basic awesome-machine-unlearning repo stats
5
602
7.9
9 days ago
tamlhp/awesome-machine-unlearning is an open source project licensed under MIT License which is an OSI approved license.
The primary programming language of awesome-machine-unlearning is Jupyter Notebook.
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