Search Results for author: Rakshit Naidu

Found 9 papers, 5 papers with code

Efficient Hyperparameter Optimization for Differentially Private Deep Learning

1 code implementation9 Aug 2021 Aman Priyanshu, Rakshit Naidu, FatemehSadat Mireshghallah, Mohammad Malekzadeh

Tuning the hyperparameters in the differentially private stochastic gradient descent (DPSGD) is a fundamental challenge.

Hyperparameter Optimization

Towards Quantifying the Carbon Emissions of Differentially Private Machine Learning

no code implementations14 Jul 2021 Rakshit Naidu, Harshita Diddee, Ajinkya Mulay, Aleti Vardhan, Krithika Ramesh, Ahmed Zamzam

In recent years, machine learning techniques utilizing large-scale datasets have achieved remarkable performance.

Benchmarking Differential Privacy and Federated Learning for BERT Models

2 code implementations26 Jun 2021 Priyam Basu, Tiasa Singha Roy, Rakshit Naidu, Zumrut Muftuoglu, Sahib Singh, FatemehSadat Mireshghallah

Natural Language Processing (NLP) techniques can be applied to help with the diagnosis of medical conditions such as depression, using a collection of a person's utterances.

Federated Learning

When Differential Privacy Meets Interpretability: A Case Study

no code implementations24 Jun 2021 Rakshit Naidu, Aman Priyanshu, Aadith Kumar, Sasikanth Kotti, Haofan Wang, FatemehSadat Mireshghallah

Given the increase in the use of personal data for training Deep Neural Networks (DNNs) in tasks such as medical imaging and diagnosis, differentially private training of DNNs is surging in importance and there is a large body of work focusing on providing better privacy-utility trade-off.

DP-SGD vs PATE: Which Has Less Disparate Impact on Model Accuracy?

1 code implementation22 Jun 2021 Archit Uniyal, Rakshit Naidu, Sasikanth Kotti, Sahib Singh, Patrik Joslin Kenfack, FatemehSadat Mireshghallah, Andrew Trask

Recent advances in differentially private deep learning have demonstrated that application of differential privacy, specifically the DP-SGD algorithm, has a disparate impact on different sub-groups in the population, which leads to a significantly high drop-in model utility for sub-populations that are under-represented (minorities), compared to well-represented ones.


FedPandemic: A Cross-Device Federated Learning Approach Towards Elementary Prognosis of Diseases During a Pandemic

no code implementations5 Apr 2021 Aman Priyanshu, Rakshit Naidu

The amount of data, manpower and capital required to understand, evaluate and agree on a group of symptoms for the elementary prognosis of pandemic diseases is enormous.

Federated Learning

IS-CAM: Integrated Score-CAM for axiomatic-based explanations

1 code implementation6 Oct 2020 Rakshit Naidu, Ankita Ghosh, Yash Maurya, Shamanth R Nayak K, Soumya Snigdha Kundu

Convolutional Neural Networks have been known as black-box models as humans cannot interpret their inner functionalities.

SS-CAM: Smoothed Score-CAM for Sharper Visual Feature Localization

2 code implementations25 Jun 2020 Haofan Wang, Rakshit Naidu, Joy Michael, Soumya Snigdha Kundu

Interpretation of the underlying mechanisms of Deep Convolutional Neural Networks has become an important aspect of research in the field of deep learning due to their applications in high-risk environments.

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