Summary of A Monte Carlo Framework For Calibrated Uncertainty Estimation in Sequence Prediction, by Qidong Yang et al.
A Monte Carlo Framework for Calibrated Uncertainty Estimation in Sequence Prediction
by Qidong Yang, Weicheng Zhu, Joseph Keslin, Laure Zanna, Tim G. J. Rudner, Carlos Fernandez-Granda
First submitted to arxiv on: 30 Oct 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 The proposed Monte Carlo framework estimates probabilities and confidence intervals associated with the distribution of a discrete sequence, conditioned on an image input. The framework uses an autoregressively trained neural network as a Monte Carlo simulator to sample sequences, which are then used to estimate the probabilities and confidence intervals. The approach is tested on synthetic and real data, demonstrating accurate discriminative predictions but potentially suffering from miscalibration. To address this issue, a time-dependent regularization method is proposed, shown to produce calibrated predictions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way of predicting sequences from images has been developed. Instead of just guessing the most likely sequence, this method calculates the probability and uncertainty associated with each possible sequence. The approach uses a special type of neural network that can generate many different sequences based on an image input. This helps estimate the likelihood of each sequence being correct or not. The results show that this method works well for predicting sequences but sometimes makes mistakes. To fix this, a new technique is introduced to make sure the predictions are accurate and reliable. |
Keywords
» Artificial intelligence » Likelihood » Neural network » Probability » Regularization
