Niru Maheswaranathan

Artificial Intelligence · 2015–2022

A researcher in the Science corpus: 5 papers; trusts 310 researchers and is trusted by 270 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 122 citations into it; #1,257 of 35,345 by trust here, #1,662 by citations
• Optimization & Training — 77 citations into it; #1,623 of 2,601 by trust here, #1,083 by citations
• Computer Vision and Pattern Recognition — 74 citations into it; #10,158 of 21,575 by trust here, #7,249 by citations
• Probabilistic & Bayesian Methods — 34 citations into it; #209 of 1,525 by trust here, #72 by citations
• Model Architectures — 23 citations into it
• Representation & Self-Supervised Learning — 23 citations into it
• Reinforcement Learning — 21 citations into it
• Generative Models — 18 citations into it; #165 of 1,956 by trust here, #173 by citations

Whom Niru Maheswaranathan trusts overall

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

1. Yoshua Bengio
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. J. Urgen Schmidhuber
6. Yann LeCun
7. Ronald J. Williams
8. Radford M. Neal
9. Kaiming He
10. Ilya Sutskever

Who trusts Niru Maheswaranathan overall

1. Yang Song
2. Luke Metz
3. Jonathan Ho
4. Diederik P. Kingma
5. Yoshua Bengio
6. Liyang Jiang
7. Arash Vahdat
8. Yilun Du
9. Sam Schoenholz
10. Stefano Ermon

Papers from Niru Maheswaranathan's perspective

Ordered by how strongly the researchers Niru Maheswaranathan 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
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

• Deep Unsupervised Learning using Nonequilibrium Thermodynamics (2015) — 10,395 citations
• Learned Optimizers that Scale and Generalize (2017) — 308 citations
• Meta-Learning Update Rules for Unsupervised Representation Learning (2018) — 132 citations
• Understanding and correcting pathologies in the training of learned optimizers (2018) — 168 citations
• Practical tradeoffs between memory, compute, and performance in learned optimizers (2022) — 42 citations