Robik Shrestha

Computer Vision and Pattern Recognition · 2019

A researcher in the Science corpus: 4 papers; trusts 939 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 196 citations into it; #8,125 of 21,575 by trust here, #7,176 by citations
• Artificial Intelligence — 192 citations into it; #13,267 of 35,345 by trust here, #10,667 by citations
• Multimodal Learning — 71 citations into it
• Natural Language Processing — 58 citations into it
• Evaluation & Benchmarks — 26 citations into it
• Question Answering & Reading Comprehension — 25 citations into it
• Representation & Self-Supervised Learning — 23 citations into it
• Meta & Continual Learning — 18 citations into it; #601 of 1,041 by trust here, #456 by citations

Whom Robik Shrestha trusts overall

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

1. Ross B. Girshick
2. Kaiming He
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Yoshua Bengio
6. Diederik P. Kingma
7. Kushal Kafle
8. Ilya Sutskever
9. Yann LeCun
10. Karen Simonyan

Who trusts Robik Shrestha overall

1. Kushal Kafle
2. Drew A. Hudson
3. Tyler L. Hayes
4. Christopher Kanan
5. Rémi Cadène
6. Christopher D. Manning
7. Manoj Acharya
8. Xialei Liu
9. Corentin Dancette
10. Xinzhe Han

Papers from Robik Shrestha's perspective

Ordered by how strongly the researchers Robik Shrestha 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. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• REMIND Your Neural Network to Prevent Catastrophic Forgetting (2019) — 349 citations
• Answer Them All! Toward Universal Visual Question Answering Models (2019) — 87 citations
• Challenges and Prospects in Vision and Language Research (2019) — 43 citations
• Answering Questions about Data Visualizations using Efficient Bimodal Fusion (2019) — 67 citations