Summary of Creinns: Credal-set Interval Neural Networks For Uncertainty Estimation in Classification Tasks, by Kaizheng Wang et al.
CreINNs: Credal-Set Interval Neural Networks for Uncertainty Estimation in Classification Tasks
by Kaizheng Wang, Keivan Shariatmadar, Shireen Kudukkil Manchingal, Fabio Cuzzolin, David Moens, Hans Hallez
First submitted to arxiv on: 10 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 CreINNs, a novel approach for classification, retain traditional Interval Neural Networks’ structure while capturing weight uncertainty through deterministic intervals. Unlike single probability values, CreINNs predict upper and lower probability bounds per class, defining credal sets that facilitate estimating various types of uncertainties. Experimental results on multiclass and binary tasks show that CreINNs achieve superior or comparable quality of uncertainty estimation to variational Bayesian Neural Networks (BNNs) and Deep Ensembles. Additionally, CreINNs significantly reduce the computational complexity of variational BNNs during inference. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CreINNs is a new way to predict what something might be. Instead of just saying “it’s probably this,” it gives you two numbers: one says “it might not be that” and the other says “it’s definitely not that.” This helps us understand how sure we are about our predictions. It’s like having a range of possibilities instead of just one answer. CreINNs works well on certain types of problems, even better than some other methods. Plus, it doesn’t take as long to figure out the answers. |
Keywords
* Artificial intelligence * Classification * Inference * Probability
