Manjunath Kudlur

Computer Vision and Pattern Recognition · 2015–2018

A researcher in the Science corpus: 6 papers; trusts 286 researchers and is trusted by 1,456 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 92 citations into it; #1,100 of 21,575 by trust here, #471 by citations
• Artificial Intelligence — 86 citations into it; #3,229 of 35,345 by trust here, #636 by citations
• Model Architectures — 42 citations into it; #458 of 2,278 by trust here, #625 by citations
• Natural Language Processing — 32 citations into it
• Optimization & Training — 25 citations into it
• Generative Models — 17 citations into it
• Computer Graphics and Computer-Aided Design — 16 citations into it
• Representation & Self-Supervised Learning — 12 citations into it; #389 of 3,145 by trust here, #693 by citations

Whom Manjunath Kudlur trusts overall

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

1. Leon A. Gatys
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Alexei A. Efros
5. Karen Simonyan
6. Ilya Sutskever
7. Alex Krizhevsky
8. Yoshua Bengio
9. Sepp Hochreiter
10. Andrew Zisserman

Who trusts Manjunath Kudlur overall

1. Charles Ruizhongtai Qi
2. Xun Huang
3. Yoshua Bengio
4. Jeff Donahue
5. Mohit Bansal
6. Takeru Miyato
7. William L. Hamilton
8. Ming-Yu Liu
9. Kaiyang Zhou
10. Yang Song

Papers from Manjunath Kudlur's perspective

Ordered by how strongly the researchers Manjunath Kudlur trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. ImageNet: A large-scale hierarchical image database
6. Gradient-based learning applied to document recognition
7. ImageNet Large Scale Visual Recognition Challenge
8. Attention is All you Need (Neural Information Processing Systems)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• TensorFlow: A system for large-scale machine learning (2016) — 19,838 citations
• A Learned Representation For Artistic Style (2016) — 1,271 citations
• Order Matters: Sequence to sequence for sets (2015) — 1,043 citations
• Exploring the structure of a real-time, arbitrary neural artistic stylization network (2017) — 312 citations
• Dynamic control flow in large-scale machine learning (2018) — 112 citations
• TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems (2016) — 11,798 citations