T. Mundhenk

Computer Vision and Pattern Recognition · 2016–2017

A researcher in the Science corpus: 2 papers; trusts 487 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 51 citations into it; #10,295 of 21,575 by trust here, #7,938 by citations
• Artificial Intelligence — 28 citations into it; #18,434 of 35,345 by trust here, #16,771 by citations
• Representation & Self-Supervised Learning — 18 citations into it; #1,911 of 3,145 by trust here, #2,042 by citations
• Model Architectures — 13 citations into it
• Optimization & Training — 5 citations into it

Whom T. Mundhenk trusts overall

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

1. Alex Krizhevsky
2. Ross B. Girshick
3. Christian Szegedy
4. Kaiming He
5. Geoffrey E. Hinton
6. Sergey Ioffe
7. Ilya Sutskever
8. Andrew Zisserman
9. Yann LeCun
10. Nitish Srivastava

Who trusts T. Mundhenk overall

1. Shubhra Aich
2. Longlong Jing
3. Jun Shu
4. Zeyu Feng
5. M. Hsieh
6. Puneet Mangla
7. Adam Van Etten
8. Jie Gui
9. Jakaria Rabbi
10. Daniel E. Ho

Papers from T. Mundhenk's perspective

Ordered by how strongly the researchers T. Mundhenk 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. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Improvements to Context Based Self-Supervised Learning (2017) — 125 citations
• A Large Contextual Dataset for Classification, Detection and Counting of Cars with Deep Learning (2016) — 392 citations