Kumar Shridhar

Artificial Intelligence · with Laumann · 2018–2019

A researcher in the Science corpus: 4 papers; trusts 309 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 68 citations into it; #25,255 of 35,345 by trust here, #31,361 by citations
• Computer Vision and Pattern Recognition — 50 citations into it; #15,512 of 21,575 by trust here, #19,766 by citations
• Probabilistic & Bayesian Methods — 48 citations into it; #1,258 of 1,525 by trust here, #1,415 by citations
• Optimization & Training — 38 citations into it
• Model Architectures — 23 citations into it; #1,690 of 2,278 by trust here, #2,192 by citations
• Efficiency & Compression — 14 citations into it
• Reinforcement Learning — 9 citations into it
• Computer Networks and Communications — 8 citations into it

Whom Kumar Shridhar trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Alex Graves
4. Diederik P. Kingma
5. Yann LeCun
6. David John Cameron MacKay
7. Radford M. Neal
8. Yoshua Bengio
9. Ilya Sutskever
10. Sepp Hochreiter

Who trusts Kumar Shridhar overall

1. Felix Laumann
2. Sayna Ebrahimi
3. Mohamed Elhoseiny
4. Marcus Liwicki

Papers from Kumar Shridhar's perspective

Ordered by how strongly the researchers Kumar Shridhar 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. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
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. Going deeper with convolutions

Their papers

• A Comprehensive guide to Bayesian Convolutional Neural Network with Variational Inference (2019) — 225 citations
• Bayesian Convolutional Neural Networks (2018) — 23 citations
• Uncertainty Estimations by Softplus normalization in Bayesian Convolutional Neural Networks with Variational Inference (2018) — 71 citations
• Bayesian Convolutional Neural Networks with Variational Inference (2018) — 19 citations