Siddharth Swaroop

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 5 papers; trusts 159 researchers and is trusted by 22 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 144 citations into it; #8,532 of 35,345 by trust here, #8,077 by citations
• Probabilistic & Bayesian Methods — 70 citations into it; #1,406 of 1,525 by trust here, #770 by citations
• Computer Vision and Pattern Recognition — 62 citations into it; #10,550 of 21,575 by trust here, #9,722 by citations
• Optimization & Training — 41 citations into it; #2,446 of 2,601 by trust here, #1,766 by citations
• Representation & Self-Supervised Learning — 40 citations into it; #2,841 of 3,145 by trust here, #2,330 by citations
• Meta & Continual Learning — 17 citations into it; #600 of 1,041 by trust here, #456 by citations
• Statistical and Nonlinear Physics — 14 citations into it
• Management Science and Operations Research — 12 citations into it

Whom Siddharth Swaroop trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Radford M. Neal
4. Alex Krizhevsky
5. Alex Graves
6. Yoshua Bengio
7. Charles Blundell
8. Yann LeCun
9. Richard E. Turner
10. Jimmy Ba

Who trusts Siddharth Swaroop overall

1. Frederik Benzing
2. Samuel Kessler
3. Fei Ye
4. Tim G. J. Rudner
5. Ta-Chu Kao
6. Pingbo Pan
7. Sanyam Kapoor
8. Cuong V Nguyen
9. Adrian G. Bors
10. Marc Masana

Papers from Siddharth Swaroop's perspective

Ordered by how strongly the researchers Siddharth Swaroop 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. 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

• Improving and Understanding Variational Continual Learning (2019) — 53 citations
• Practical Deep Learning with Bayesian Principles (2019) — 280 citations
• Continual Deep Learning by Functional Regularisation of Memorable Past (2020) — 174 citations
• Generalized Variational Continual Learning (2020) — 75 citations
• Partitioned Variational Inference: A unified framework encompassing federated and continual learning (2018) — 58 citations