Tom Schumm
Computer Vision and Pattern Recognition · 2017
A researcher in the Science corpus: 1 paper; trusts 29 researchers and is trusted by 117 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 14 citations into it; #3,114 of 21,575 by trust here, #3,558 by citations
- Generative Models — 13 citations into it; #256 of 1,956 by trust here, #389 by citations
- Artificial Intelligence — 10 citations into it
- Model Architectures — 5 citations into it
- Optimization & Training — 5 citations into it
Whom Tom Schumm trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yoshua Bengio
- Diederik P. Kingma
- Geoffrey E. Hinton
- Individualized Treat
- Ian J. Goodfellow
- Yann LeCun
- Alex Krizhevsky
- J. Urgen Schmidhuber
- Kaiming He
- Tim Salimans
Who trusts Tom Schumm overall
- Jiapeng Zhu
- Ngoc-Trung Tran
- Sergey I. Nikolenko
- Jamie Hayes
- Yujun Shen
- Jingjing Xu
- Sitao Xiang
- Garrett Wilson
- Paulina Grnarova
- Zizhao Zhang
Papers from Tom Schumm's perspective
Ordered by how strongly the researchers Tom Schumm trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- GENERATIVE ADVERSARIAL NETS
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- BEGAN: Boundary Equilibrium Generative Adversarial Networks (2017) — 1,207 citations