-
Archives
- September 2021
- August 2021
- April 2021
- March 2021
- February 2021
- December 2020
- November 2020
- October 2020
- September 2020
- August 2020
- July 2020
- May 2020
- February 2020
- January 2020
- December 2019
- November 2019
- October 2019
- September 2019
- August 2019
- July 2019
- May 2019
- April 2019
- November 2018
- October 2018
- August 2018
- June 2018
- May 2018
- February 2018
- January 2018
- November 2017
-
Meta
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
Posted in news
Tagged Alexandros Kalousis, deep leaning, generative modeling, Jason, Jason Ramapuram, Kanerva++, phd, PhD Thesis, presentation, VAE, Variational saccading
Comments Off on PhD Thesis defense of Jason Ramapuram
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
Comments Off on Kanerva++ at ICLR21
Papers in NeurIPS 2020
We are very happy to announce that the members of our team have had two accepted papers in NeurIPS 2020!
Posted in news
Tagged Alexandros Kalousis, Amina, Amina Mollaysa, internships, Jason, Jason Ramapuram, NeurIPS, NIPS, NIPS conference, papers
Comments Off on Papers in NeurIPS 2020
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
Posted in news
Tagged collaboration, conference, deep leaning, Jason, Jason Ramapuram, paper, presentation
Comments Off on Jason to present DAB
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!
Posted in news
Tagged BMVC conference, conference, Jason, Jason Ramapuram, paper, Variational saccading
Comments Off on Variational saccading