Tag Archives: Jason Ramapuram

PhD Thesis defense of Jason Ramapuram

Jason Ramapuram will defend on Wednesday the 15th of September 2021 at 13:00 his PhD thesis entitled:  "Finding signals in the void: Improving deep latent variable generative models via supervisory signals present within data."  co-directed by Prof. Stephane Marchand-Maillet and … Continue reading

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Kanerva++ at ICLR21

The ICLR21 conference is still a few weeks away but to wet your appetite already, we are glad to let you know that Jason Ramapuram will be presenting there his new paper Kanerva++: Extending the Kanerva Machine With Differentiable, Locally Block Allocated Latent Memory. The paper is a result of a successful collaboration with Yan Wu from Deepmind.

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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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