Vishnu Naresh Boddeti

Computer Vision and Pattern Recognition · 2013–2019

A researcher in the Science corpus: 12 papers; trusts 971 researchers and is trusted by 33 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 197 citations into it; #5,265 of 21,575 by trust here, #5,941 by citations
• Artificial Intelligence — 151 citations into it; #7,106 of 35,345 by trust here, #8,893 by citations
• Model Architectures — 34 citations into it
• Optimization & Training — 32 citations into it
• Generative Models — 31 citations into it
• Neural Architecture Search & AutoML — 24 citations into it; #236 of 283 by trust here, #217 by citations
• Signal Processing — 24 citations into it; #3,517 of 7,572 by trust here, #7,112 by citations
• Safety, Robustness & Interpretability — 23 citations into it

Whom Vishnu Naresh Boddeti trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Diederik P. Kingma
4. Geoffrey E. Hinton
5. Craig Gentry
6. Yoshua Bengio
7. Yann LeCun
8. Ilya Sutskever
9. David G. Lowe
10. Karen Simonyan

Who trusts Vishnu Naresh Boddeti overall

1. Fatemehsadat Mirshghallah
2. Praneeth Vepakomma
3. Abhishek Singh
4. Xiangxiang Chu
5. Mohammadkazem Taram
6. Sergey I. Nikolenko
7. Umang Gupta
8. Sara Beery
9. Bashir Sadeghi
10. Ramesh Raskar

Papers from Vishnu Naresh Boddeti's perspective

Ordered by how strongly the researchers Vishnu Naresh Boddeti 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. 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. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Learning scene-specific pedestrian detectors without real data (2015) — 146 citations
• Learning Scene-Specific Pedestrian Detectors without Real Data (2015)
• Maximum Margin Correlation Filter: A New Approach for Localization and Classification (2013) — 102 citations
• In Teacher We Trust: Learning Compressed Models for Pedestrian Detection (2016) — 30 citations
• NSGA-Net: neural architecture search using multi-objective genetic algorithm (2018) — 544 citations
• RankGAN: A Maximum Margin Ranking GAN for Generating Faces (2018) — 24 citations
• Privacy-Preserving Visual Learning Using Doubly Permuted Homomorphic Encryption (2017) — 70 citations
• On the Global Optima of Kernelized Adversarial Representation Learning (2019) — 36 citations
• Secure Face Matching Using Fully Homomorphic Encryption (2018) — 147 citations
• Mitigating Information Leakage in Image Representations: A Maximum Entropy Approach (2019) — 119 citations
• NSGA-NET: A Multi-Objective Genetic Algorithm for Neural Architecture Search (2018) — 88 citations
• SecureRAG: End-to-End Secure Retrieval-Augmented Generation