Sudeep Pillai
Computer Vision and Pattern Recognition · 2015–2019
A researcher in the Science corpus: 3 papers; trusts 722 researchers and is trusted by 16 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 102 citations into it; #7,983 of 21,575 by trust here, #9,187 by citations
- Aerospace Engineering — 33 citations into it; #3,694 of 6,421 by trust here, #4,945 by citations
- Artificial Intelligence — 20 citations into it
- Media Technology — 18 citations into it; #1,325 of 3,621 by trust here, #1,079 by citations
- Evaluation & Benchmarks — 16 citations into it
- Model Architectures — 9 citations into it
- Geology — 7 citations into it
- Optimization & Training — 7 citations into it
Whom Sudeep Pillai trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Diederik P. Kingma
- Andrew Zisserman
- Geoffrey E. Hinton
- Alex Krizhevsky
- Ross B. Girshick
- David G. Lowe
- Karen Simonyan
- Yann LeCun
- Yoshua Bengio
Who trusts Sudeep Pillai overall
- Spyros Gidaris
- Jamie Watson
- Niko Sünderhauf
- Clément Godard
- John McCormac
- Matteo Poggi
- Xiaoyang Lyu
- R. Clark
- Gabriel J. Brostow
- Oisin Mac Aodha
Papers from Sudeep Pillai's perspective
Ordered by how strongly the researchers Sudeep Pillai trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
Their papers
- 3D Packing for Self-Supervised Monocular Depth Estimation (2019) — 818 citations
- SuperDepth: Self-Supervised, Super-Resolved Monocular Depth Estimation (2018) — 229 citations
- Monocular SLAM Supported Object Recognition (2015) — 140 citations