Arjun Nitin Bhagoji

Artificial Intelligence · 2017–2019

A researcher in the Science corpus: 8 papers; trusts 184 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 584 citations into it; #17,710 of 35,345 by trust here, #2,652 by citations
• Safety, Robustness & Interpretability — 255 citations into it; #1,404 of 2,251 by trust here, #856 by citations
• Computer Vision and Pattern Recognition — 128 citations into it
• Federated & Distributed Learning — 125 citations into it; #189 of 319 by trust here, #26 by citations
• Optimization & Training — 103 citations into it
• Computer Networks and Communications — 87 citations into it
• Signal Processing — 86 citations into it; #6,607 of 7,572 by trust here, #4,560 by citations
• Information Systems — 38 citations into it

Whom Arjun Nitin Bhagoji trusts overall

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

1. Yann LeCun
2. Alex Krizhevsky
3. Jonathon Shlens
4. Ian J. Goodfellow
5. Christian Szegedy
6. Geoffrey E. Hinton
7. Yoshua Bengio
8. Kaiming He
9. Ilya Sutskever
10. Diederik P. Kingma

Who trusts Arjun Nitin Bhagoji overall

1. Nicholas Carlini
2. Virat Shejwalkar
3. Naveed Akhtar
4. David Stutz
5. David Enthoven
6. Ke He
7. Xiaoyu Cao
8. Zihao Liu
9. Weilin Xu
10. Chien-Lun Chen

Papers from Arjun Nitin Bhagoji's perspective

Ordered by how strongly the researchers Arjun Nitin Bhagoji 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. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Advances and Open Problems in Federated Learning (2019) — 9,019 citations
• Enhancing robustness of machine learning systems via data transformations (2017) — 246 citations
• Analyzing Federated Learning through an Adversarial Lens (2018) — 1,351 citations
• Supplementary : Analyzing Federated Learning through an Adversarial Lens (2019)
• Better the Devil you Know: An Analysis of Evasion Attacks using Out-of-Distribution Adversarial Examples (2019) — 27 citations
• Rogue Signs: Deceiving Traffic Sign Recognition with Malicious Ads and Logos (2018) — 70 citations
• Dimensionality Reduction as a Defense against Evasion Attacks on Machine Learning Classifiers (2017) — 164 citations
• Exploring the Space of Black-box Attacks on Deep Neural Networks (2017) — 5 citations