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Suggested R eadings Reproducibility c omes i n s everal d ifferent f orms 1. Reproducibility o f m ethods

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Motivation An improvement* to regular Convolutional Neural Networks. Two goals: Replace max-pooling operation with something more intuitive.



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Open Review (@openreviewnet) Twitter

We gratefully acknowledge the support of the OpenReview sponsors: Google, Facebook, NSF, the University of Massachusetts Amherst Center for Data Science, and Center for Intelligent Information Retrieval, as well as the Google Cloud Platform for donating the computing and networking services on which OpenReview.net runs.

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Download How smart is today's artificial intelligence

Top Posts & Pages. Learning Deep Learning - My Top-Five List A Review on Hinton's Coursera "Neural Networks and Machine Learning" Gradient Descent For Logistic Regression

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Dear knowledge-avid readers, on next DL seminar on 15th May at 14:00 in S1, Vojtech Cermak will be presenting the following two papers: Tom B. Brown, Dandelion

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Two different ways to train 1. Using context to predict a target word (~ continuous bag-of-words) 2. Using word to predict a target context (skip-gram)

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Is there a later version of paragraph vectors (doc2vec

This recent paper ( april 2017) describes a method to create paragraph/sentence vectors that does much better than even sequence models ( e.g. LSTM) for text similarity tasks ( for sentiment analysis word order matters so this model which is word order independent can't compete with an RNN).

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Computers finally catching up with science fiction. Is there any research into using neural networks to hatch devious plans, or are we still better at it than computers?

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GitHub emited/flow Implementation of our ICLR paper

For background on Quantization - please read this link (INT8 quantization proposal) This thread only focuses on quantizing the models, i.e., representing the weights/biases from their current FP32 format to INT8 format, while controlling the drop in the accuracy introduced by the quantization.

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NeuroNet Fast and Robust Reproduction of Multiple Brain

We gratefully acknowledge the support of the OpenReview sponsors: Google, Facebook, NSF, the University of Massachusetts Amherst Center for Data Science, and Center for Intelligent Information Retrieval, as well as the Google Cloud Platform for donating the computing and networking services on which OpenReview.net runs.

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Literature on Neural Architecture Search – AutoML

hypernetworks can generate non-shared weights for LSTM. Introduction using a small network (called a hypernetwork) to generate the weights for a larger network (called a main network)

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