Driverless car, Data set, Data, Operations research, Learning, Machine learning

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On Nov 1, 2020
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RT @ak92501: Permute, Quantize, and Fine-tune: Efficient Compression of Neural Networks pdf: https://t.co/duV3vXPyab abs: https://t.co/yFdbj3J7hz github: https://t.co/ok35vdKXi6 https://t.co/AuVxJZxpaV
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In this context, vector quantization is an ap- pealing framework that expresses multiple parameters us- ing a single code, and has recently achieved state-of-the- art network compression on a range of core vision and natural language processing tasks. In par- ticular, we fix the codes and ...

arxiv.org
On Nov 1, 2020
@petewarden shared
RT @ak92501: Permute, Quantize, and Fine-tune: Efficient Compression of Neural Networks pdf: https://t.co/duV3vXPyab abs: https://t.co/yFdbj3J7hz github: https://t.co/ok35vdKXi6 https://t.co/AuVxJZxpaV
Open

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In this context, vector quantization is an ap- pealing framework that expresses multiple parameters us- ing a single code, and has recently achieved state-of-the- art network compression on ...

Deep Learning in Real Time – Inference Acceleration and Continuous Training

Deep Learning in Real Time – Inference Acceleration and Continuous Training

In this report, we will touch on some of the recent technologies, trends and studies on deep neural network inference acceleration and continuous training in the context of production ...

Gradient descent for wide two-layer neural networks – I : Global convergence

Gradient descent for wide two-layer neural networks – I : Global convergence

This is still not exactly what is used in practice, but, as explained in last month post, this is a good approximation of gradient descent (if using the empirical risk, then leading to ...

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One highly successful approach to automatically deriving features based on this idea is clustering; for example, the Brown et al.4 clustering algorithm automatically organized words into ...

Revisiting Self-Supervised Visual Representation Learning

Revisiting Self-Supervised Visual Representation Learning

As a result, the intermediate layers of convolutional neural networks (CNNs) trained for solving these pretext tasks encode high-level semantic visual representations that are useful for ...