Probabilistic Numeric CNNs with Roberto Bondesan - #482
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
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Probabilistic Approaches in CNNs
This chapter explores the integration of probabilistic numerical methods within convolutional neural networks (CNNs), focusing on their application to error correction in wireless signals and the modeling of non-uniformly sampled data. It discusses the incorporation of partial differential equations (PDEs) to enhance training processes while managing uncertainties in inputs. Additionally, the chapter highlights the future potential of connecting these methodologies with quantum computation, emphasizing their significance in data processing and advanced AI applications.
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