Tag Archives: Variational saccading

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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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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