Antti Rasmus

Computer Vision and Pattern Recognition · 2015–2017

A researcher in the Science corpus: 3 papers; trusts 61 researchers and is trusted by 308 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 40 citations into it; #2,309 of 21,575 by trust here, #2,872 by citations
• Artificial Intelligence — 39 citations into it; #5,285 of 35,345 by trust here, #3,937 by citations
• Representation & Self-Supervised Learning — 19 citations into it; #1,113 of 3,145 by trust here, #613 by citations
• Model Architectures — 16 citations into it
• Optimization & Training — 15 citations into it
• Probabilistic & Bayesian Methods — 6 citations into it

Whom Antti Rasmus trusts overall

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

1. Yoshua Bengio
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Ian J. Goodfellow
5. J. Urgen Schmidhuber
6. Yann LeCun
7. Alex Krizhevsky
8. Jimmy Ba
9. Pascal Vincent
10. Radford M. Neal

Who trusts Antti Rasmus overall

1. Klaus Greff
2. J. Urgen Schmidhuber
3. Casper Kaae Sønderby
4. Lu Liu
5. Yassine Ouali
6. Sjoerd van Steenkiste
7. Jost Tobias Springenberg
8. Mehdi S. M. Sajjadi
9. Philip Bachman
10. Yoshua Bengio

Papers from Antti Rasmus's perspective

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

Their papers

• Semi-supervised Learning with Ladder Networks (2015) — 1,426 citations
• Tagger: Deep Unsupervised Perceptual Grouping (2016) — 169 citations
• Recurrent Ladder Networks (2017) — 41 citations