Akshay Mehrotra

Computer Vision and Pattern Recognition · 2017

A researcher in the Science corpus: 1 paper; trusts 209 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 23 citations into it; #7,053 of 21,575 by trust here, #14,151 by citations
• Artificial Intelligence — 16 citations into it; #10,283 of 35,345 by trust here, #21,316 by citations
• Representation & Self-Supervised Learning — 15 citations into it
• Model Architectures — 6 citations into it
• Generative Models — 5 citations into it

Whom Akshay Mehrotra trusts overall

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

1. Ruslan Salakhutdinov
2. Kaiming He
3. Geoffrey E. Hinton
4. Sergey Ioffe
5. Yann LeCun
6. R. Caruana
7. Alex Krizhevsky
8. Yoshua Bengio
9. Diederik P. Kingma
10. Oriol Vinyals

Who trusts Akshay Mehrotra overall

1. Debasmit Das
2. Qianru Sun
3. Antreas Antoniou
4. Nathan Hilliard
5. Varun Kumar
6. C. S. Lee
7. Yaoyao Liu
8. Amos J. Storkey
9. Nathan Oken Hodas
10. Hadrien Glaude

Papers from Akshay Mehrotra's perspective

Ordered by how strongly the researchers Akshay Mehrotra 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. ImageNet Large Scale Visual Recognition Challenge
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Generative Adversarial Residual Pairwise Networks for One Shot Learning (2017) — 118 citations