Summary of Quanda: An Interpretability Toolkit For Training Data Attribution Evaluation and Beyond, by Dilyara Bareeva et al.
Quanda: An Interpretability Toolkit for Training Data Attribution Evaluation and Beyond
by Dilyara Bareeva, Galip Ümit Yolcu, Anna Hedström, Niklas Schmolenski, Thomas Wiegand, Wojciech Samek, Sebastian Lapuschkin
First submitted to arxiv on: 9 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A recent surge in research on training data attribution (TDA) methods for neural networks has led to the development of several standalone evaluation metrics. However, a unified framework is lacking, hindering trust and widespread adoption. To address this gap, we introduce Quanda, a Python toolkit designed to facilitate TDA method evaluation. It offers a comprehensive set of evaluation metrics and a uniform interface for seamless integration with existing implementations across different repositories, enabling systematic benchmarking. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TDA methods are used to make neural networks more interpretable. Right now, there aren’t any good ways to compare how well these methods work in different situations. To fix this problem, we created Quanda, a toolkit that helps you evaluate TDA methods. It has many evaluation metrics and makes it easy to use existing TDA implementations. |
