Jason Tyler Rolfe

Artificial Intelligence · 2013–2019

A researcher in the Science corpus: 5 papers; trusts 480 researchers and is trusted by 395 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 77 citations into it; #2,248 of 35,345 by trust here, #7,858 by citations
• Computer Vision and Pattern Recognition — 66 citations into it; #2,091 of 21,575 by trust here, #5,680 by citations
• Generative Models — 35 citations into it; #227 of 1,956 by trust here, #1,054 by citations
• Probabilistic & Bayesian Methods — 35 citations into it; #240 of 1,525 by trust here, #700 by citations
• Computational Theory and Mathematics — 31 citations into it
• Materials Chemistry — 28 citations into it
• Model Architectures — 25 citations into it; #1,239 of 2,278 by trust here, #1,665 by citations
• Representation & Self-Supervised Learning — 25 citations into it

Whom Jason Tyler Rolfe trusts overall

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

1. Geoffrey E. Hinton
2. Ruslan Salakhutdinov
3. Yoshua Bengio
4. Diederik P. Kingma
5. Radford M. Neal
6. Yann LeCun
7. Alex Krizhevsky
8. Ronald J. Williams
9. Danilo Jimenez Rezende
10. Tim Salimans

Who trusts Jason Tyler Rolfe overall

1. Arash Vahdat
2. Evgeny Andriyash
3. Lars Maaløe
4. Xuezhe Ma
5. William G. Macready
6. Christos Louizos
7. Diederik P. Kingma
8. Renrui Zhang
9. Hossein Sadeghi
10. Tsu-Jui Fu

Papers from Jason Tyler Rolfe's perspective

Ordered by how strongly the researchers Jason Tyler Rolfe 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. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
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

• Discrete Variational Autoencoders (2016) — 291 citations
• Understanding Deep Architectures using a Recursive Convolutional Network (2013) — 148 citations
• Discriminative Recurrent Sparse Auto-Encoders (2013) — 74 citations
• All SMILES Variational Autoencoder (2019) — 43 citations
• Quantum Boltzmann Machine (2016) — 599 citations