Al Shao
7 posts


@dmvaldman I still think that it had some explanations of this joke in its corpus, but at the very least it's able to extend basic variations of logic pretty well.

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Excited to present our work "Squeezeformer: An Efficient Transformer for Automatic Speech Recognition" at NeurIPS 2022 in New Orleans tomorrow Thursday December 1st!
Stop by our poster at 11AM - 1 PM (CST) in Hall J Poster #620
nips.cc/Conferences/20…
#NeurIPS2022
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@quocleix @tanmingxing This is really great work! I was wondering about a detail. I was looking at the interesting lite-R ASPP network design and was curious about the pooling layer's striding. Why is it set to output a different aspect ratio instead of being 1:1 with the input? Thanks!
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Introducing MobileNetV3: Based on MNASNet, found by architecture search, we applied additional methods to go even further (quantization friendly SqueezeExcite & Swish + NetAdapt + Compact layers). Result: 2x faster and more accurate than MobileNetV2. Link: arxiv.org/abs/1905.02244

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