most citedA Closer Look at Disentangling in -VAE

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2020

Investigating Learning in Deep Neural Networks using Layer-Wise Weight Change

Ayush Manish Agrawal, Atharva Tendle, Harshvardhan Sikka +2

Understanding the per-layer learning dynamics of deep neural networks is of significant interest as it may provide insights into how neural networks learn and the potential for bet…

cs.NE2020

A Genetic Algorithm Based Approach for Satellite Autonomy

Sidhdharth Sikka, Harshvardhan Sikka

Autonomous spacecraft maneuver planning using an evolutionary algorithmic approach is investigated. Simulated spacecraft were placed into four different initial orbits. Each was al…

cs.LG2020

Benchmarking Differentially Private Residual Networks for Medical Imagery

Sahib Singh, Harshvardhan Sikka, Sasikanth Kotti +1

In this paper we measure the effectiveness of -Differential Privacy (DP) when applied to medical imaging. We compare two robust differential privacy mechanisms: Local-DP and DP-…

cs.LG2020

A Deeper Look at the Unsupervised Learning of Disentangled Representations in -VAE from the Perspective of Core Object Recognition

Harshvardhan Sikka

The ability to recognize objects despite there being differences in appearance, known as Core Object Recognition, forms a critical part of human perception. While it is understood…

stat.ML20191 cited

A Closer Look at Disentangling in -VAE

Harshvardhan Sikka, Weishun Zhong, Jun Yin +1

In many data analysis tasks, it is beneficial to learn representations where each dimension is statistically independent and thus disentangled from the others. If data generating f…