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Machine learning with less than one example
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Amine Moulay Ramdane
Oct 25, 2020, 1:58:11 PM10/25/20
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Machine learning with less than one example
A math idea that may dramatically reduce the dataset size needed to train AI systems
MNIST is a database of images of handwritten digits often used in training and testing machine learning models. Sucholutsky and his colleague Matthias Schonlau managed to achieve above-90 percent accuracy on MNIST with just five synthetic examples on the convolutional neural network LeNet.
“That result really surprised me, and it’s what got me thinking more broadly about this LO-shot learning setting,” Sucholutsky said.
Read more here:
https://bdtechtalks.com/2020/10/01/less-than-one-shot-machine-learning/
Thank you,
Amine Moulay Ramdane.
Machine learning with less than one example
A math idea that may dramatically reduce the dataset size needed to train AI systems
MNIST is a database of images of handwritten digits often used in training and testing machine learning models. Sucholutsky and his colleague Matthias Schonlau managed to achieve above-90 percent accuracy on MNIST with just five synthetic examples on the convolutional neural network LeNet.
“That result really surprised me, and it’s what got me thinking more broadly about this LO-shot learning setting,” Sucholutsky said.
Read more here:
https://bdtechtalks.com/2020/10/01/less-than-one-shot-machine-learning/
Thank you,
Amine Moulay Ramdane.
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