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Deeper Is Not Necessarily Better: Princeton U & Intel’s 12-Layer Parallel Networks Achieve Performance Competitive With SOTA Deep Networks

While it is generally accepted that network depth is responsible for the high performance of today’s deep learning (DL) models, adding depth also brings downsides such as increased latency and computational burdens, which can bottleneck progress in DL. Is it…

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