Deep Fried Convnets · HackerPapers discussions

Deep Fried Convnets — mentioned by stochastic_monk

Mon, 12 Feb 2018 15:50:26 GMT

This reminds me of ACDC, a Deep-fried Convnets-like[0] approach by some NVIDIA employees. [1] See section 1.1, where they state they could perform the operation in analog. [0]: https://arxiv.org/abs/1412.7149 [1]: https://arxiv.org/pdf/1511.05946

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Deep Fried Convnets — mentioned by stochastic_monk

Sun, 29 Oct 2017 16:59:34 GMT

Very interesting; I've ignored random forests more than I should. Thank you! I won't comment on your methods except to say that comparing classification over linear PCA against a kernel PCA in linearly unseparable data isn't exactly fair, and I think that providing an SVM performed on a different kernel PCA decomposition or a kernel SVM itself would be more illustrative. (Is your code available?) I generally think of neural networks as enormous meta-kernels. (Composites of <composites of...> kernels) This generally leads me to think of ways that kernels can be turned into neural network…

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Deep Fried Convnets — mentioned by curuinor

Sun, 02 Oct 2016 22:27:03 GMT

Rather hilariously, deep frying is already a term of art in ML, of course in a radically different setting. Deep fried convnets (https://arxiv.org/abs/1412.7149).

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Deep Fried Convnets — mentioned by jhartmann

Sun, 14 Jun 2015 14:39:47 GMT

Actually, state of the art speech recognition has switched over to having a Recursive Neural Network directly run over the audio input. Take a look at the paper at http://arxiv.org/abs/1412.5567 and http://usa.baidu.com/deep-speech-lessons-from-deep-learning/ However combining learning features with other systems is a very powerful approach and combining SVM's on top of the learned features of a Neural Network I would say is common. I personally am more interested in approaches like Deep Fried Convnets (http://arxiv.org/abs/1412.7149) that combine kernel methods as part of the Neural Networks…

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