Nitika Verma

Computer Vision and Pattern Recognition · 2017

A researcher in the Science corpus: 2 papers; trusts 267 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 42 citations into it; #10,218 of 21,575 by trust here, #11,950 by citations
• Computational Mechanics — 25 citations into it; #4,214 of 10,001 by trust here, #3,763 by citations
• Artificial Intelligence — 24 citations into it
• Graph Learning — 19 citations into it
• Model Architectures — 12 citations into it
• Representation & Self-Supervised Learning — 8 citations into it
• Geology — 7 citations into it
• Aerospace Engineering — 5 citations into it

Whom Nitika Verma trusts overall

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

1. Yann LeCun
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Kaiming He
5. Davide Boscaini
6. Jonathan Masci
7. Yoshua Bengio
8. Alex Krizhevsky
9. Joan Bruna
10. Max Welling

Who trusts Nitika Verma overall

1. L. Zampieri
2. Or Litany
3. Hongsuk Choi
4. Nikos Kolotouros
5. Kiran Koshy Thekumparampil
6. Liuhao Ge
7. Gyeongsik Moon
8. Georgios Pavlakos
9. A. Bronstein
10. Michael M. Bronstein

Papers from Nitika Verma's perspective

Ordered by how strongly the researchers Nitika Verma 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. 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. Going deeper with convolutions
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• FeaStNet: Feature-Steered Graph Convolutions for 3D Shape Analysis (2017) — 363 citations
• Dynamic Filters in Graph Convolutional Networks (2017) — 26 citations