Ryan Gomes

Computer Vision and Pattern Recognition · 2008–2010

A researcher in the Science corpus: 2 papers; trusts 22 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 10 citations into it; #17,114 of 21,575 by trust here, #21,103 by citations
• Artificial Intelligence — 6 citations into it; #27,910 of 35,345 by trust here, #34,213 by citations
• Probabilistic & Bayesian Methods — 5 citations into it
• Representation & Self-Supervised Learning — 5 citations into it; #1,204 of 3,145 by trust here, #1,866 by citations

Whom Ryan Gomes trusts overall

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

1. Michael I. Jordan
2. Yee Whye Teh
3. Hagai Thomas Attias
4. Francis R. Bach
5. Andrew Y. Ng
6. Geoffrey E. Hinton
7. John Shawe-Taylor
8. Tommi S. Jaakkola
9. Yoshua Bengio
10. Pietro Perona

Who trusts Ryan Gomes overall

1. Jian Liang
2. Jost Tobias Springenberg
3. Piotr Bojanowski
4. Hui Tang
5. Jason Ramapuram
6. Bradly C. Stadie
7. Rafael Müller
8. Dan Feldman
9. Dapeng Hu
10. Pieter Abbeel

Papers from Ryan Gomes's perspective

Ordered by how strongly the researchers Ryan Gomes 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: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Discriminative Clustering by Regularized Information Maximization (2010) — 401 citations
• Incremental learning of nonparametric Bayesian mixture models (2008) — 63 citations