Vinayak A. Rao

Artificial Intelligence · 2012–2019

A researcher in the Science corpus: 4 papers; trusts 47 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 53 citations into it; #10,461 of 35,345 by trust here, #14,181 by citations
• Graph Learning — 41 citations into it
• Computer Vision and Pattern Recognition — 19 citations into it
• Model Architectures — 17 citations into it
• Molecular Biology — 15 citations into it; #14,495 of 17,773 by trust here, #12,465 by citations
• Computational Theory and Mathematics — 12 citations into it
• Probabilistic & Bayesian Methods — 11 citations into it
• Representation & Self-Supervised Learning — 11 citations into it

Whom Vinayak A. Rao trusts overall

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

1. Radford M. Neal
2. Darren J. Wilkinson
3. Vanessa Didelez
4. Diederik P. Kingma
5. Andrew Golightly
6. L. Eon Bottou
7. Geoffrey E. Hinton
8. Yann LeCun
9. Yoshua Bengio
10. Alex Krizhevsky

Who trusts Vinayak A. Rao overall

1. Viktoriya Krakovna
2. Finale Doshi-Velez
3. William L. Hamilton
4. Vijay Prakash Dwivedi
5. Jiaxuan You
6. Zhengdao Chen

Papers from Vinayak A. Rao's perspective

Ordered by how strongly the researchers Vinayak A. Rao 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. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Relational Pooling for Graph Representations (2019) — 285 citations
• Janossy Pooling: Learning Deep Permutation-Invariant Functions for Variable-Size Inputs (2018) — 214 citations
• A Multitask Point Process Predictive Model (2015) — 65 citations
• Fast MCMC sampling for Markov jump processes and extensions (2012) — 119 citations