Gustavo Kunde Rohde
Computer Vision and Pattern Recognition · 2003–2019
A researcher in the Science corpus: 5 papers; trusts 201 researchers and is trusted by 8 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 41 citations into it; #10,612 of 21,575 by trust here, #11,950 by citations
- Artificial Intelligence — 18 citations into it; #12,614 of 35,345 by trust here, #20,836 by citations
- Applied Mathematics — 16 citations into it
- Computational Mechanics — 13 citations into it
- Optimization & Training — 13 citations into it
- Computational Theory and Mathematics — 10 citations into it; #11,604 of 12,182 by trust here, #6,165 by citations
- Generative Models — 10 citations into it
- Mathematical Optimization — 9 citations into it
Whom Gustavo Kunde Rohde trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Marco Cuturi
- Diederik P. Kingma
- Julien Rabin
- Soheil Kolouri
- Geoffrey E. Hinton
- Yann LeCun
- Nicolas Bonneel
- Daniel Rueckert
- Yoshua Bengio
- Alex Krizhevsky
Who trusts Gustavo Kunde Rohde overall
- Mohammad Rostami
- Soheil Kolouri
- Juan Eugenio Iglesias
- Aristeidis Sotiras
- Stefan Klein
- A. Gholipour
- Mert Rory Sabuncu
- Christos Davatzikos
- D. Kühn
- G. Peyr'e
Papers from Gustavo Kunde Rohde's perspective
Ordered by how strongly the researchers Gustavo Kunde Rohde 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
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Optimal Mass Transport: Signal processing and machine-learning applications (2017) — 477 citations
- Generalized Sliced Wasserstein Distances (2019) — 389 citations
- Sliced Wasserstein Distance for Learning Gaussian Mixture Models (2017) — 143 citations
- Sliced Wasserstein Kernels for Probability Distributions (2015) — 182 citations
- The adaptive bases algorithm for intensity-based nonrigid image registration (2003) — 465 citations