Praneeth Vepakomma

Artificial Intelligence · 2016–2020

A researcher in the Science corpus: 14 papers; trusts 279 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 790 citations into it; #8,137 of 35,345 by trust here, #2,472 by citations
• Privacy & Security — 311 citations into it; #285 of 709 by trust here, #273 by citations
• Federated & Distributed Learning — 207 citations into it; #61 of 319 by trust here, #22 by citations
• Computer Vision and Pattern Recognition — 175 citations into it; #17,568 of 21,575 by trust here, #20,756 by citations
• Safety, Robustness & Interpretability — 142 citations into it
• Optimization & Training — 127 citations into it; #2,257 of 2,601 by trust here, #2,519 by citations
• Computer Networks and Communications — 89 citations into it
• Computational Theory and Mathematics — 65 citations into it

Whom Praneeth Vepakomma trusts overall

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

1. Otkrist Gupta
2. Cynthia Dwork
3. Shun-ichi Amari
4. G'abor J. Sz'ekely
5. Geoffrey E. Hinton
6. Ramesh Raskar
7. Alex Krizhevsky
8. Yann LeCun
9. Yoshua Bengio
10. Kenneth L. Lange

Who trusts Praneeth Vepakomma overall

1. Vivek Sharma
2. Chaoyang He
3. Abhishek Singh
4. Ramesh Raskar
5. Jihong Park
6. Iker Ceballos
7. Tristan Swedish
8. Otkrist Gupta
9. Chandra Thapa
10. Kenglun Chang

Papers from Praneeth Vepakomma's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Learning Multiple Layers of Features from Tiny Images
7. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Attention is All you Need (Neural Information Processing Systems)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Advances and Open Problems in Federated Learning (2019) — 9,019 citations
• Detailed comparison of communication efficiency of split learning and federated learning (2019) — 222 citations
• A Review of Homomorphic Encryption Libraries for Secure Computation (2018) — 49 citations
• Supervised Dimensionality Reduction via Distance Correlation Maximization (2016) — 42 citations
• ExpertMatcher: Automating ML Model Selection for Clients using Hidden Representations (2019) — 11 citations
• DISCO: Dynamic and Invariant Sensitive Channel Obfuscation for deep neural networks (2020) — 63 citations
• SplitNN-driven Vertical Partitioning (2020) — 91 citations
• Privacy in Deep Learning: A Survey (2020) — 153 citations
• FedML: A Research Library and Benchmark for Federated Machine Learning (2020) — 716 citations
• NoPeek: Information leakage reduction to share activations in distributed deep learning (2020) — 119 citations
• ExpertMatcher: Automating ML Model Selection for Users in Resource Constrained Countries (2019) — 7 citations
• R EDUCING LEAKAGE IN DISTRIBUTED DEEP LEARNING FOR SENSITIVE HEALTH DATA (2019)
• Split learning for health: Distributed deep learning without sharing raw patient data (2018) — 964 citations
• No Peek: A Survey of private distributed deep learning (2018) — 113 citations