Kayhan Batmanghelich

Computer Vision and Pattern Recognition · 2010–2019

A researcher in the Science corpus: 6 papers; trusts 406 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 80 citations into it; #11,652 of 21,575 by trust here, #6,873 by citations
• Artificial Intelligence — 57 citations into it; #34,852 of 35,345 by trust here, #26,682 by citations
• Representation & Self-Supervised Learning — 36 citations into it
• Generative Models — 13 citations into it
• Media Technology — 13 citations into it; #1,812 of 3,621 by trust here, #612 by citations
• Signal Processing — 11 citations into it
• Causal Inference & Discovery — 10 citations into it
• Probabilistic & Bayesian Methods — 9 citations into it

Whom Kayhan Batmanghelich trusts overall

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

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

Who trusts Kayhan Batmanghelich overall

1. Heung-Il Suk
2. Rui Shu
3. Dinggang Shen
4. Bilwaj Gaonkar
5. Daoqiang Zhang
6. Shariq Farooq Bhat

Papers from Kayhan Batmanghelich's perspective

Ordered by how strongly the researchers Kayhan Batmanghelich 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Deep Ordinal Regression Network for Monocular Depth Estimation (2018) — 2,009 citations
• Weakly Supervised Disentanglement by Pairwise Similarities (2019) — 58 citations
• Transfer Learning with Label Noise (2017) — 32 citations
• Causal Generative Domain Adaptation Networks (2018) — 22 citations
• Causal Discovery with Linear Non-Gaussian Models under Measurement Error: Structural Identifiability Results (2018) — 27 citations
• Prediction of MCI to AD conversion, via MRI, CSF biomarkers, pattern classification (2010) — 614 citations