Less is more: Efficient pruning for reducing AI memory and computational cost

Deep learning and AI systems have made great headway in recent years, especially in their capabilities of automating complex computational tasks such as image recognition, computer vision and natural language processing. Yet, these systems consist of billions of parameters and require great memory usage as well as expensive computational cost.

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