Rahul G. Krishnan

Artificial Intelligence · 2015–2022

A researcher in the Science corpus: 6 papers; trusts 279 researchers and is trusted by 81 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 115 citations into it; #3,532 of 35,345 by trust here, #6,157 by citations
• Computer Vision and Pattern Recognition — 72 citations into it; #3,541 of 21,575 by trust here, #4,001 by citations
• Probabilistic & Bayesian Methods — 51 citations into it; #275 of 1,525 by trust here, #236 by citations
• Generative Models — 33 citations into it; #449 of 1,956 by trust here, #922 by citations
• Representation & Self-Supervised Learning — 30 citations into it; #2,242 of 3,145 by trust here, #2,691 by citations
• Information Systems — 29 citations into it; #3,335 of 8,771 by trust here, #2,667 by citations
• Recommender Systems — 28 citations into it; #138 of 233 by trust here, #135 by citations
• Signal Processing — 24 citations into it; #1,345 of 7,572 by trust here, #1,055 by citations

Whom Rahul G. Krishnan trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Ruslan Salakhutdinov
4. Yoshua Bengio
5. Danilo Jimenez Rezende
6. Alex Krizhevsky
7. Ilya Sutskever
8. Yann LeCun
9. Radford M. Neal
10. Hugo Larochelle

Who trusts Rahul G. Krishnan overall

1. Hao Wang
2. Bernie Wang
3. Bryan Lim
4. Chaochao Lu
5. Danijar Hafner
6. Marco Fraccaro
7. Yoon Kim
8. Danielle C. Maddix
9. Sameer Khurana
10. Stephan Mandt

Papers from Rahul G. Krishnan's perspective

Ordered by how strongly the researchers Rahul G. Krishnan 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. 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Deep Kalman Filters (2015) — 435 citations
• Structured Inference Networks for Nonlinear State Space Models (2016) — 537 citations
• On the challenges of learning with inference networks on sparse, high-dimensional data (2017) — 90 citations
• Variational Autoencoders for Collaborative Filtering (2018) — 1,520 citations
• Barrier Frank-Wolfe for Marginal Inference (2015) — 40 citations
• Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning (2022) — 716 citations