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Learning Hierarchical Information Flow With Recurrent Neural Modules by legendmichael5553 is a document available to read on EtoBox.
The document presents ThalNet, a deep learning model inspired by thalamic communication in the neocortex, which utilizes recurrent neural modules to route information hierarchically. This model allows for flexible feature sharing over time and has been shown to outperform traditional recurrent neural networks on various sequential tasks, including image classification and language modeling. The paper details the architecture, learning mechanisms, and experimental results demonstrating the model
- Author
- legendmichael5553
- Language
- EN