Probabilistic Numeric CNNs
Roberto discusses the innovative application of deep learning to non-uniformly sampled signals, emphasizing the importance of understanding these signals in their continuous form. He explains how probabilistic numerics quantifies uncertainty in numerical computations, particularly through Bayesian inference, allowing models to return probability distributions rather than single values. This approach not only enhances the accuracy of numerical integrals but also opens new avenues for machine learning applications.In this clip
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The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Probabilistic Numeric CNNs with Roberto Bondesan - #482
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