Summary of Average-over-time Spiking Neural Networks For Uncertainty Estimation in Regression, by Tao Sun et al.
Average-Over-Time Spiking Neural Networks for Uncertainty Estimation in Regression
by Tao Sun, Sander Bohté
First submitted to arxiv on: 29 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
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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 two novel methods for uncertainty estimation in spiking neural networks (SNNs), particularly for regression models. The Average-Over-Time Spiking Neural Network (AOT-SNN) framework is adapted to regression tasks, enabling efficient uncertainty estimation in event-driven models. The first method uses the heteroscedastic Gaussian approach, predicting both mean and variance at each time step, while the second method employs the Regression-as-Classification (RAC) approach, reformulating regression as a classification problem. The proposed AOT-SNN models demonstrate comparable or better performance to state-of-the-art deep neural networks in uncertainty estimation, making them an efficient and biologically inspired alternative for applications requiring both accuracy and energy efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Uncertainty estimation is important for reliable predictions from deep learning models. This paper shows how to do this for spiking neural networks (SNNs), which are like the brain and use less energy than regular computers. SNNs can be used for regression, or predicting numbers. The two methods in this paper help make SNNs better at estimating uncertainty, making them useful for real-world applications. |
Keywords
» Artificial intelligence » Classification » Deep learning » Neural network » Regression
