Summary of Continual Learning with the Neural Tangent Ensemble, by Ari S. Benjamin et al.
Continual learning with the neural tangent ensemble
by Ari S. Benjamin, Christian Pehle, Kyle Daruwalla
First submitted to arxiv on: 30 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 A natural approach to continual learning involves combining the predictions from multiple Bayesian models. This idea is inspired by the concept of ensembling fixed functions. To realize this potential, we treat a neural network as an ensemble of N classifiers, where each classifier outputs valid probability distributions over labels. We then derive the likelihood and posterior probabilities of these classifiers given past data. Interestingly, the updates to these posteriors are equivalent to stochastic gradient descent (SGD) over the network weights. This framework provides a new understanding of neural networks as Bayesian ensembles of experts, which can be used to mitigate catastrophic forgetting in continual learning settings. Our findings offer a principled approach to continual learning and its application in various tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper explores how we can make machines learn better over time without forgetting what they learned earlier. The idea is to combine multiple predictions from different models. We treat a neural network as a group of many small classifiers, each producing a probability for the correct answer. By combining these probabilities, we get a more accurate result. This helps us understand how neural networks work and how they can learn new things without forgetting old ones. The findings in this paper provide a new way to think about how machines learn and can help us make better artificial intelligence. |
Keywords
» Artificial intelligence » Continual learning » Likelihood » Neural network » Probability » Stochastic gradient descent
