Jason Ramapuram

Jason Ramapuram Assistant HES, PhD student CS UNIGE


Jason Ramapuram joined the DMML team in 2015. He completed his Masters in Electrical Engineering (with a focus on Signal Processing) in 2011 from the University of California, Riverside. Until joining the DMML team Jason worked as a Software Engineer at Qualcomm Inc and later as a Machine Learning Engineer at Viasat Inc. Jason has also done academic internships at Apple AI Research and Rockwell Collins Advanced Technology Center.


Jason is actively working on Lifelong Learning for Generative Models, Variational approaches to allow CNN’s to work over ultra-high dimensional image data (eg: 4k / 8k images) and approaches to incorporate non-differentiable functions without neural network pipelines. He has also dabbled in some research attempting to find failings of neural networks in simple learning scenarios.



Ramapuram, Jason; Lavda, Frantzeska; Kalousis, Alexandros; Diephuis, Maurits

Variational Saccading: Efficient Inference for Large Resolution Images Workshop

Bayesian Deep Learning Workshop Neurips, 2018.

Abstract | Links | BibTeX

Lavda, Frantzeska; Ramapuram, Jason; Gregorova, Magda; Kalousis, Alexandros

Continual Classification Learning Using Generative Models Workshop

Continual learning Workshop NeurIPS 2018, 2018.

Links | BibTeX

Ramapuram, Jason; Webb, Russ

A New Benchmark and Progress Toward Improved Weakly Supervised Learning Journal Article

CoRR, abs/1807.00126 , 2018.

Links | BibTeX

Gregorová, Magda; Ramapuram, Jason; Kalousis, Alexandros; Marchand-Maillet, Stéphane

Large-Scale Nonlinear Variable Selection via Kernel Random Features Inproceedings

Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2018, Dublin, Ireland, September 10-14, 2018, Proceedings, Part II, pp. 177–192, 2018.

Links | BibTeX


Ramapuram, Jason; Gregorova, Magda; Kalousis, Alexandros

Lifelong Generative Modeling Journal Article

CoRR, abs/1705.09847 , 2017.

Links | BibTeX