Geoffrey Roeder

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 3 papers; trusts 89 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 52 citations into it; #10,610 of 35,345 by trust here, #11,192 by citations
• Computer Vision and Pattern Recognition — 20 citations into it
• Probabilistic & Bayesian Methods — 16 citations into it; #1,084 of 1,525 by trust here, #1,113 by citations
• Representation & Self-Supervised Learning — 16 citations into it
• Generative Models — 15 citations into it
• Reinforcement Learning — 15 citations into it
• Optimization & Training — 11 citations into it; #1,734 of 2,601 by trust here, #1,385 by citations
• Natural Language Processing — 8 citations into it

Whom Geoffrey Roeder trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Yoshua Bengio
5. Radford M. Neal
6. Andriy Mnih
7. Aapo Hyvärinen
8. Danilo Jimenez Rezende
9. Sepp Hochreiter
10. Ilya Sutskever

Who trusts Geoffrey Roeder overall

1. Arash Vahdat
2. Diederik P. Kingma
3. George Tucker
4. Guy Lorberbom
5. Yang Song
6. William S. Peebles
7. Ming Xu
8. Max Welling
9. Evgeny Andriyash
10. Sébastien Lachapelle

Papers from Geoffrey Roeder's perspective

Ordered by how strongly the researchers Geoffrey Roeder 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
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

• Backpropagation through the Void: Optimizing control variates for black-box gradient estimation (2017) — 317 citations
• Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference (2017) — 225 citations
• On Linear Identifiability of Learned Representations (2020) — 115 citations