Generative Adversarial Networks (GANs)[Goodfellow et al. 2014] convergence in a high-
resolution setting with a computational constrain of GPU memory capacity has been beset …

The topic of this Ph. D. thesis lies on the borderline between signal processing, statistics and
computer science. It mainly focuses on compressive learning, a paradigm for large-scale …

Kernel methods provide a flexible and theoretically grounded approach to nonlinear and
nonparametric learning. While memory and run-time requirements hinder their applicability …

Dr. of Crosswise proposes a new architecture to reduce over-parametrization in Neural
Networks. It introduces an operand for rapid computation in the framework of Deep Learning …

We introduce a framework to use kernel approximates in the mini-batch setting with
Stochastic Gradient Descent (SGD) as an alternative to Deep Learning. Based on Random …

Generative Adversarial Networks (GANs) have had tremendous applications in Computer
Vision. Yet, in the context of space science and planetary exploration the door is open for …

We propose a novel approach to address the problem of Simultaneous Detection and
Segmentation introduced in [Hariharan et al 2014]. Using the hierarchical structures first …