Ambedkar Dukkipati

Computer Vision and Pattern Recognition · 2014–2017

A researcher in the Science corpus: 4 papers; trusts 427 researchers and is trusted by 22 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 59 citations into it; #5,912 of 21,575 by trust here, #8,831 by citations
• Artificial Intelligence — 56 citations into it; #10,281 of 35,345 by trust here, #19,439 by citations
• Representation & Self-Supervised Learning — 32 citations into it
• Natural Language Processing — 23 citations into it
• Model Architectures — 19 citations into it; #1,498 of 2,278 by trust here, #1,643 by citations
• Generative Models — 9 citations into it
• Optimization & Training — 9 citations into it
• Signal Processing — 7 citations into it

Whom Ambedkar Dukkipati trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. David G. Lowe
4. Yann LeCun
5. Ruslan Salakhutdinov
6. Diederik P. Kingma
7. Alex Krizhevsky
8. Alex Graves
9. Kaiming He
10. J. Urgen Schmidhuber

Who trusts Ambedkar Dukkipati overall

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

Papers from Ambedkar Dukkipati's perspective

Ordered by how strongly the researchers Ambedkar Dukkipati 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. 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. ImageNet Large Scale Visual Recognition Challenge
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Attentive Recurrent Comparators (2017) — 116 citations
• Generative Adversarial Residual Pairwise Networks for One Shot Learning (2017) — 118 citations
• A Neural Architecture Mimicking Humans End-to-End for Natural Language Inference (2016) — 13 citations
• Learning by Stretching Deep Networks (2014) — 23 citations