Vaishnavh Nagarajan

Artificial Intelligence · 2017–2023

A researcher in the Science corpus: 7 papers; trusts 251 researchers and is trusted by 54 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 119 citations into it; #7,074 of 35,345 by trust here, #12,761 by citations
• Computer Vision and Pattern Recognition — 63 citations into it; #3,903 of 21,575 by trust here, #6,954 by citations
• Scaling & Learning Theory — 40 citations into it; #247 of 428 by trust here, #288 by citations
• Generative Models — 31 citations into it
• Optimization & Training — 26 citations into it; #586 of 2,601 by trust here, #1,034 by citations
• Safety, Robustness & Interpretability — 24 citations into it
• Statistical Learning Theory — 20 citations into it; #353 of 1,038 by trust here, #478 by citations
• Natural Language Processing — 16 citations into it

Whom Vaishnavh Nagarajan trusts overall

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

1. Behnam Neyshabur
2. David A. McAllester
3. Geoffrey E. Hinton
4. Yann LeCun
5. J. Langford
6. Yoshua Bengio
7. Alex Krizhevsky
8. Luke Metz
9. Srinadh Bhojanapalli
10. Nati Srebro

Who trusts Vaishnavh Nagarajan overall

1. Colin Wei
2. Gauthier Gidel
3. Niladri S. Chatterji
4. Lars M. Mescheder
5. Behnam Neyshabur
6. N. Bynagari
7. Martin Heusel
8. Tengyu Ma
9. Florian Graf
10. Yuanzhi Li

Papers from Vaishnavh Nagarajan's perspective

Ordered by how strongly the researchers Vaishnavh Nagarajan 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. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
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

• Gradient descent GAN optimization is locally stable (2017) — 375 citations
• Generalization in Deep Networks: The Role of Distance from Initialization (2019) — 97 citations
• Deterministic PAC-Bayesian generalization bounds for deep networks via generalizing noise-resilience (2019) — 105 citations
• Uniform convergence may be unable to explain generalization in deep learning (2019) — 365 citations
• Think before you speak: Training Language Models With Pause Tokens (2023) — 302 citations
• Theoretical Insights into Memorization in GANs (2019)
• Revisiting Adversarial Risk (2018) — 20 citations