Niladri S. Chatterji

Artificial Intelligence · 2017–2024

A researcher in the Science corpus: 8 papers; trusts 85 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 124 citations into it
• Natural Language Processing — 44 citations into it
• Computer Vision and Pattern Recognition — 35 citations into it
• Reinforcement Learning — 21 citations into it
• Management Science and Operations Research — 16 citations into it
• Statistics and Probability — 15 citations into it; #11,437 of 12,932 by trust here, #9,233 by citations
• Model Architectures — 12 citations into it
• Scaling & Learning Theory — 12 citations into it

Whom Niladri S. Chatterji trusts overall

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

1. Diederik P. Kingma
2. Behnam Neyshabur
3. Kaiming He
4. David A. McAllester
5. Geoffrey E. Hinton
6. Karen Simonyan
7. Philip M. Long
8. Alex Krizhevsky
9. Jimmy Ba
10. Yoshua Bengio

Who trusts Niladri S. Chatterji overall

1. Aldo Pacchiano
2. Christopher Nemeth
3. Michael Arbel
4. Wenlong Mou
5. Aadirupa Saha
6. Raaz Dwivedi

Papers from Niladri S. Chatterji's perspective

Ordered by how strongly the researchers Niladri S. Chatterji 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• On the Opportunities and Risks of Foundation Models (2021) — 6,923 citations
• Holistic Evaluation of Language Models (2023) — 1,863 citations
• On the Theory of Reinforcement Learning with Once-per-Episode Feedback (2021) — 37 citations
• Underdamped Langevin MCMC: A non-asymptotic analysis (2017) — 342 citations
• The Llama 3 Herd of Models (2024) — 17,085 citations
• When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards (2024) — 153 citations
• Proving Test Set Contamination in Black Box Language Models (2023) — 251 citations
• The intriguing role of module criticality in the generalization of deep networks (2019) — 60 citations