Summary of Torchcp: a Python Library For Conformal Prediction, by Jianguo Huang et al.
TorchCP: A Python Library for Conformal Prediction
by Jianguo Huang, Jianqing Song, Xuanning Zhou, Bingyi Jing, Hongxin Wei
First submitted to arxiv on: 20 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Statistics Theory (math.ST)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces TorchCP, a comprehensive toolkit for deep learning models that enables the application of conformal prediction (CP) algorithms. CP provides strict theoretical guarantees but has been limited by challenges in applicability and efficiency when applied to deep learning models. TorchCP addresses these limitations by implementing various post-hoc and training methods for machine learning tasks such as classification, regression, graph neural networks, and language models. The toolkit also features user-friendly interfaces and extensive evaluations for easy integration into specific tasks. TorchCP is built on PyTorch, enabling high-performance GPU acceleration and mini-batch computation on large-scale datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it easier to use special prediction tools called conformal prediction (CP) with deep learning models. Deep learning is a type of artificial intelligence that’s good at learning from big amounts of data. CP helps us understand how well these models will work in the future, but there are some challenges to making this happen. The paper creates a tool called TorchCP that makes it easier to use CP with deep learning models. This tool lets you choose which type of prediction you want to do and provides extra help and testing so you can easily use CP for your own projects. |
Keywords
* Artificial intelligence * Classification * Deep learning * Machine learning * Regression
