Summary of Revisiting Essential and Nonessential Settings Of Evidential Deep Learning, by Mengyuan Chen et al.
Revisiting Essential and Nonessential Settings of Evidential Deep Learning
by Mengyuan Chen, Junyu Gao, Changsheng Xu
First submitted to arxiv on: 1 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 This paper proposes a revised version of Evidential Deep Learning (EDL), called Re-EDL, to improve uncertainty estimation in deep learning models. EDL provides reliable predictive uncertainty by constructing a Dirichlet probability density function (PDF) from neural networks. However, the original EDL method incorporates several nonessential settings that can impact its performance. Specifically, it fixes the prior weight parameter, uses variance-minimizing optimization, and includes KL-divergence regularization. The authors argue that these settings can exacerbate overconfidence in the predictive scores. To address this, Re-EDL relaxes these nonessential settings by treating the prior weight as an adjustable hyperparameter and directly optimizing the expectation of the Dirichlet PDF. Extensive experiments demonstrate the effectiveness of Re-EDL, which achieves state-of-the-art performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about improving how deep learning models can predict uncertainty in their results. The current method, called Evidential Deep Learning (EDL), is good at doing this, but it has some flaws that make the predictions less reliable. The authors propose a new version of EDL, called Re-EDL, that fixes these problems. They show that Re-EDL works better than the original EDL and achieves the best results so far. This means that we can use Re-EDL to get more accurate predictions with uncertainty estimates. |
Keywords
» Artificial intelligence » Deep learning » Hyperparameter » Optimization » Probability » Regularization
