Abstract: Besides the spatial contents from current sensory inputs, the relevant contexts from past frames are very important for temporal processing tasks (e.g., speech recognition, video analysis, and natural language processing). Our Developmental Network (DN) has demonstrated the ability to learn any emergent Turing Machine (TM), it can learn feature patterns from current and attended past natural inputs as their states. We have shown the dense actions can serve as natural sources of contexts, and the DN can autonomously generate actions as contexts ...
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Topics: 
Artificial intelligence
Speech recognition
Natural language processing