Publications

Benchmarking Differentially Private Residual Networks for Medical Imagery

S Singh, H Sikka, S Kotti, A Trask (2020): 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-SGD and benchmark their performance when analyzing medical imagery records...
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WeightScale: Interpreting Weight Change in Neural Networks

AM Agrawal, A Tendle, H Sikka, S Singh (2021): Interpreting the learning dynamics of neural networks can provide useful insights into how networks learn and the development of better training and design approaches. We present an approach to interpret learning in neural networks by measuring relative weight change on a per layer basis and dynamically aggregating emerging trends through combination of dimensionality reduction and clustering which allows us to scale to very deep networks...
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Investigating Learning in Deep Neural Networks using Layer-Wise Weight Change

AM Agrawal, A Tendle, H Sikka, S Singh, A Kayid (2021): 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 better training regimens. We investigate learning in Deep Convolutional Neural Networks (CNNs) by measuring the relative weight change of layers while training...
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A Genetic Algorithm Based Approach for Satellite Autonomy

S Sikka, H Sikka (2021): Autonomous spacecraft maneuver planning using an evolutionary algorithmic approach is investigated. Simulated spacecraft were placed into four different initial orbits. Each was allowed a string of thirty delta-v impulse maneuvers in six cartesian directions, the positive and negative x, y and z directions. The goal of the spacecraft maneuver string was to, starting from some non-polar starting orbit, place the spacecraft into a polar, low eccentricity orbit...
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Multimodal Modular Meta-Learning

H Sikka, A Tendle, A Kayid (2020): Many real world prediction problems involve structured tasks across multiple modalities. We propose to extend previous work in modular meta learning to the multimodal setting. Specifically, we present an algorithmic approach to apply task aware modulation to a modular meta learning system that decomposes structured multimodal problems into a set of modules that can be reassembled to learn new tasks...
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Creating, Managing, and Understanding Large, Sparse, Multitask Neural Networks

H Sikka (2020): One of the popular directions in Deep Learning (DL) research has been to build larger and more complex deep networks that can perform well on several different learning tasks, commonly known as multitask learning. This work is usually done within specific domains, e.g. multitask models that perform captioning, translation, and text classification tasks...
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