Janardhan Kulkarni

Artificial Intelligence · 2016–2021

A researcher in the Science corpus: 6 papers; trusts 35 researchers and is trusted by 38 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 126 citations into it; #5,435 of 35,345 by trust here, #8,007 by citations
• Privacy & Security — 65 citations into it; #141 of 709 by trust here, #214 by citations
• Computer Networks and Communications — 21 citations into it
• Safety, Robustness & Interpretability — 21 citations into it
• Statistics and Probability — 19 citations into it
• Computer Science Applications — 14 citations into it; #93 of 1,855 by trust here, #232 by citations
• Electrical and Electronic Engineering — 14 citations into it
• Algorithms & Complexity — 13 citations into it

Whom Janardhan Kulkarni trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Cynthia Dwork
2. John C. Duchi
3. Raef Bassily
4. Ú. Erlingsson
5. Adam D. Smith
6. Kobbi Nissim
7. Martin J. Wainwright
8. Maxim Raginsky
9. Michael I. Jordan
10. Frank McSherry

Who trusts Janardhan Kulkarni overall

1. Tianhao Wang
2. Jayadev Acharya
3. Badih Ghazi
4. Borja Balle
5. Teng Wang
6. Yatharth Dubey
7. Vitaly Feldman
8. Jun Zhao
9. Peter Kairouz
10. Graham Cormode

Papers from Janardhan Kulkarni's perspective

Ordered by how strongly the researchers Janardhan Kulkarni trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Attention is All you Need (Neural Information Processing Systems)
4. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. Learning Multiple Layers of Features from Tiny Images
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. ImageNet: A large-scale hierarchical image database
10. Calibrating Noise to Sensitivity in Private Data Analysis

Their papers

• Differentially Private Fine-tuning of Language Models (2021) — 517 citations
• Differentially Private Set Union (2020) — 38 citations
• Collecting Telemetry Data Privately (2017) — 791 citations
• Locally Private Gaussian Estimation (2018) — 39 citations
• An Algorithmic Framework For Differentially Private Data Analysis on Trusted Processors (2018) — 35 citations
• ProjecToR: Agile Reconfigurable Data Center Interconnect (2016) — 348 citations