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Tag Archives: Kanerva++
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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Tagged Alexandros Kalousis, deep leaning, generative modeling, Jason, Jason Ramapuram, Kanerva++, phd, PhD Thesis, presentation, VAE, Variational saccading
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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.
Posted in news
Tagged conference, ICLR, Jason, Jason Ramapuram, Kanerva++, paper
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