Learning to Ponder: Memory in Deep Neural Networks with Andrea Banino - #528
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
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Advancing Neural Networks with Memory Insights
This chapter explores the integration of recurrent architectures in neural networks, emphasizing the importance of dropout and gradient noise for improved performance. It draws parallels between neural processing in deep learning and human memory functions, particularly within the context of the PonderNet framework and its challenges for enhancing generalization.
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