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Sunday, August 18, 2019

Deep Learning Papers that you must know!

Word embedding: Mikolov et al., 2013b; Pennington et al., 2014 word level embedding. One embedding per language versus LASER one embedding for all languages.
Sentence embedding: Conneau et al., 2017; Peters et al., 2018; Devlin et al., 2018 sentence level embedding. One embedding per language versus LASER one embedding for all languages.

NLP
Word2vec

Paper on data augmentation. Data augmentation scaling, rotations, mirroring, and/or cropping. The Effectiveness of Data Augmentation in Image Classification using Deep Learning Jason Wang Stanford University Luis Perez Google
http://cs231n.stanford.edu/reports/2017/pdfs/300.pdf

AlexNet (wikipedia) : "AlexNet is the name of a convolutional neural network, designed by Alex Krizhevsky,[1] and published with Ilya Sutskever and Krizhevsky's PhD advisor Geoffrey Hinton,[2][3] who was originally resistant to the idea of his student.[1][4]

AlexNet competed in the ImageNet Large Scale Visual Recognition Challenge[5] on September 30, 2012. The network achieved a top-5 error of 15.3%, more than 10.8 percentage points lower than that of the runner up. The original paper's primary result was that the depth of the model was essential for its high performance, which was computationally expensive, but made feasible due to the utilization of graphics processing units (GPUs) during training.[5]" AlexNet is similar to modern CNN architecture made significant improvement in ImageNet competition performance!

1 comment:

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