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TRADI: Tracking deep neural network weight distributions -- work with G. Franchi https://arxiv.org/abs/1912.11316 We’re proposing a cheap method for getting ensembles of networks from a single network training 1/pic.twitter.com/J31E9aaiKL
Weights of a DNN are optimized from a random init towards an optimum value minimizing the loss. Only this final state of the weights is typically kept for testing, while the wealth of information on the geometry of the weight space, accumulated over the descent is discarded. 2/
Here, we track the trajectory of the weights during optimization using Kalman filters, allowing us to compute distributions of the weights. We can then sample an ensemble of networks for estimating model uncertainty. 3/pic.twitter.com/8vdJrjYwQY
I love it when kalman filters and neural networks team up
can listen to paper here: https://youtu.be/NvEayHR-2nU . Am thinking about adding Kalman to http://CoronavirusChart.com/
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