Chandan Yeshwanth
Computer Vision and Pattern Recognition · 2020–2023
A researcher in the Science corpus: 2 papers; trusts 130 researchers and is trusted by 3 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 43 citations into it; #16,257 of 21,575 by trust here, #15,529 by citations
- Computational Mechanics — 22 citations into it; #7,484 of 10,001 by trust here, #7,884 by citations
- Computer Graphics and Computer-Aided Design — 18 citations into it; #1,182 of 1,584 by trust here, #1,271 by citations
- Aerospace Engineering — 16 citations into it
- 3D Vision & Pose — 12 citations into it
- Geology — 11 citations into it
- Artificial Intelligence — 10 citations into it
- Natural Language Processing — 5 citations into it
Whom Chandan Yeshwanth trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Johannes L. Schönberger
- Charles Ruizhongtai Qi
- M. Kazhdan
- Kaiming He
- Angel X. Chang
- Geoffrey E. Hinton
- Alex Krizhevsky
- Diederik P. Kingma
- Yoshua Bengio
- Ross B. Girshick
Who trusts Chandan Yeshwanth overall
Papers from Chandan Yeshwanth's perspective
Ordered by how strongly the researchers Chandan Yeshwanth trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- ImageNet Large Scale Visual Recognition Challenge
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- SceneFormer: Indoor Scene Generation with Transformers (2020) — 216 citations
- ScanNet++: A High-Fidelity Dataset of 3D Indoor Scenes (2023) — 795 citations