Tag Archives: Jason

Papers in NeurIPS 2020

We are very happy to announce that the members of our team have had two accepted papers in NeurIPS 2020!

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Jason to present DAB

A couple of months ago Jason Ramapuram interned in Apple Machine Learning Research. Among other things, he worked with Russ Webb on a  novel method allowing for the use of simple non-differentiable functions at intermediary layers of deep neural networks. … Continue reading

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Variational saccading

Ever tried to train a deep neural network over high resolution images taken by modern smartphone cameras or smart devices? The memory and inferential costs when working with inputs of such large dimensions (e.g. 4000x3000) increase rapidly and often prohibitively. Jason Ramapuram proposes a solution in his new paper "Variational Saccading: Efficient Inference for Large Resolution Images". He will present his idea at the BMVC conference in September this year but you don't have to wait, check out the preprint!

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