Maja R. Rudolph

Artificial Intelligence · 2016–2020

A researcher in the Science corpus: 4 papers; trusts 61 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 105 citations into it; #19,409 of 35,345 by trust here, #9,465 by citations
• Natural Language Processing — 69 citations into it
• Probabilistic & Bayesian Methods — 44 citations into it; #1,121 of 1,525 by trust here, #700 by citations
• Computer Vision and Pattern Recognition — 33 citations into it; #19,755 of 21,575 by trust here, #17,757 by citations
• Representation & Self-Supervised Learning — 27 citations into it
• Statistics and Probability — 22 citations into it
• Model Architectures — 21 citations into it
• Evaluation & Benchmarks — 10 citations into it

Whom Maja R. Rudolph trusts overall

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

1. Tomas Mikolov
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Diederik P. Kingma
5. Ilya Sutskever
6. Andriy Mnih
7. Radford M. Neal
8. Alex Krizhevsky
9. David E. Rumelhart
10. Michael I. Jordan

Who trusts Maja R. Rudolph overall

1. Francisco J. R. Ruiz
2. Dustin Tran
3. Austin C. Kozlowski
4. David M. Blei
5. Matt Taddy
6. Susan Athey
7. Rajesh Ranganath

Papers from Maja R. Rudolph's perspective

Ordered by how strongly the researchers Maja R. Rudolph 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. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Extending Machine Language Models toward Human-Level Language Understanding (2019) — 37 citations
• Edward: A library for probabilistic modeling, inference, and criticism (2016) — 306 citations
• Placing language in an integrated understanding system: Next steps toward human-level performance in neural language models (2020) — 96 citations
• Exponential Family Embeddings (2016) — 110 citations