Tim K. Marks

Computer Vision and Pattern Recognition · 2008–2019

A researcher in the Science corpus: 7 papers; trusts 660 researchers and is trusted by 276 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 137 citations into it; #2,335 of 21,575 by trust here, #2,661 by citations
• Artificial Intelligence — 94 citations into it; #35,216 of 35,345 by trust here, #13,544 by citations
• Natural Language Processing — 47 citations into it
• Evaluation & Benchmarks — 31 citations into it
• Model Architectures — 21 citations into it; #2,267 of 2,278 by trust here, #1,378 by citations
• Cognitive Neuroscience — 15 citations into it; #5,635 of 10,852 by trust here, #3,051 by citations
• Biomedical Engineering — 11 citations into it; #319 of 10,734 by trust here, #2,804 by citations
• Representation & Self-Supervised Learning — 8 citations into it

Whom Tim K. Marks trusts overall

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

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

Who trusts Tim K. Marks overall

1. Liang Zheng
2. Tong Xiao
3. Zhedong Zheng
4. Tao A. Xiang
5. Rahul Rama Varior
6. Chi Su
7. Shuang Li
8. Ming-Ming Cheng
9. Wenguan Wang
10. Yi Yang

Papers from Tim K. Marks's perspective

Ordered by how strongly the researchers Tim K. Marks 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. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. Attention is All you Need (Neural Information Processing Systems)
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

• An improved deep learning architecture for person re-identification (2015) — 1,262 citations
• A Multi-stream Bi-directional Recurrent Neural Network for Fine-Grained Action Detection (2016) — 493 citations
• Attention-Based Multimodal Fusion for Video Description (2017) — 397 citations
• Audio Visual Scene-Aware Dialog (2019) — 230 citations
• SUN: A Bayesian framework for saliency using natural statistics. (2008) — 1,319 citations
• End-to-end Audio Visual Scene-aware Dialog Using Multimodal Attention-based Video Features (2018) — 132 citations
• Dialog System Technology Challenge 7 (2019) — 38 citations