The post provides an overview of how the RM2 Network employs unsupervised learning to process Natural Language using reference visual inputs along with the object label, just as humans do. We believe that in order to deliver effective machine-human communication, we need to integrate visual cues with language that will provide the ability to learn, reason, explain abstracts and understand the sentiment in a given conversation and help maintain context at all times.
In order to explain how the language processing works, we present an overview of the entire network and how unsupervised learning is conducted for autonomous learning
The RM2 Network is a hybrid model for Unsupervised Learning that combines aspects of Kohonen’s Self-Organizing Map (SOM) and Recurrent Networks like Hopfields Network. Click here to read more on all the existing models that influence the hybrid.