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20202023
most citedBenchmarking Differential Privacy and Federated Learning for BERT Models

7 citations · 24 across the 11 of their papers we have counts for

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7 papers · 1 filter

cs.LG2021★ 1 cited

Why is Pruning at Initialization Immune to Reinitializing and Shuffling?

Sahib Singh, Rosanne Liu

Recent studies assessing the efficacy of pruning neural networks methods uncovered a surprising finding: when conducting ablation studies on existing pruning-at-initialization meth…

cs.LG2021

WeightScale: Interpreting Weight Change in Neural Networks

Ayush Manish Agrawal, Atharva Tendle, Harshvardhan Sikka +1

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 a…

cs.LG2021★ 1 cited

Using Anomaly Feature Vectors for Detecting, Classifying and Warning of Outlier Adversarial Examples

Nelson Manohar-Alers, Ryan Feng, Sahib Singh +2

We present DeClaW, a system for detecting, classifying, and warning of adversarial inputs presented to a classification neural network. In contrast to current state-of-the-art meth…

cs.LG2021★ 5 cited

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

Archit Uniyal, Rakshit Naidu, Sasikanth Kotti +4

Recent advances in differentially private deep learning have demonstrated that application of differential privacy, specifically the DP-SGD algorithm, has a disparate impact on dif…

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.LG2020★ 7 cited

Neither Private Nor Fair: Impact of Data Imbalance on Utility and Fairness in Differential Privacy

Tom Farrand, Fatemehsadat Mireshghallah, Sahib Singh +1

Deployment of deep learning in different fields and industries is growing day by day due to its performance, which relies on the availability of data and compute. Data is often cro…

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-…