Matthew Christopher Stamm

Computer Vision and Pattern Recognition · 2010–2018

A researcher in the Science corpus: 4 papers; trusts 159 researchers and is trusted by 44 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 71 citations into it; #3,764 of 21,575 by trust here, #7,249 by citations
• Artificial Intelligence — 17 citations into it
• Model Architectures — 13 citations into it
• Media Technology — 9 citations into it; #256 of 3,621 by trust here, #693 by citations
• Optimization & Training — 8 citations into it
• Biophysics — 6 citations into it
• Signal Processing — 6 citations into it

Whom Matthew Christopher Stamm trusts overall

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

1. Jessica J. Fridrich
2. Hany Farid
3. Alin C. Popescu
4. Tomás Pevný
5. Jan Lukás
6. Micah K. Johnson
7. David G. Lowe
8. Alex Krizhevsky
9. Geoffrey E. Hinton
10. Yann LeCun

Who trusts Matthew Christopher Stamm overall

1. Belhassen Bayar
2. Davide Cozzolino
3. Luisa Verdoliva
4. Gajanan K. Birajdar
5. Andreas Rössler
6. Ru Zhang
7. Xuefeng Hu
8. Yue Wu
9. Huy Hoang Le Nguyen
10. Sohail Ahmed Khan

Papers from Matthew Christopher Stamm's perspective

Ordered by how strongly the researchers Matthew Christopher Stamm 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. Gradient-based learning applied to document recognition
5. ImageNet Large Scale Visual Recognition Challenge
6. Attention is All you Need (Neural Information Processing Systems)
7. Going deeper with convolutions
8. Long Short-Term Memory
9. ImageNet: A large-scale hierarchical image database
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• A Deep Learning Approach to Universal Image Manipulation Detection Using a New Convolutional Layer (2016) — 817 citations
• Constrained Convolutional Neural Networks: A New Approach Towards General Purpose Image Manipulation Detection (2018) — 534 citations
• Information Forensics: An Overview of the First Decade (2013) — 378 citations
• Forensic detection of image manipulation using statistical intrinsic fingerprints (2010) — 359 citations