Ryan L. Murphy

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 2 papers; trusts 36 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 47 citations into it; #11,909 of 35,345 by trust here, #14,438 by citations
• Graph Learning — 41 citations into it
• Computer Vision and Pattern Recognition — 19 citations into it
• Model Architectures — 17 citations into it
• Molecular Biology — 13 citations into it; #14,496 of 17,773 by trust here, #12,465 by citations
• Computational Theory and Mathematics — 12 citations into it
• Representation & Self-Supervised Learning — 11 citations into it
• Statistical and Nonlinear Physics — 11 citations into it

Whom Ryan L. Murphy trusts overall

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

1. Diederik P. Kingma
2. L. Eon Bottou
3. Geoffrey E. Hinton
4. Yann LeCun
5. Sepp Hochreiter
6. Yoshua Bengio
7. Persi Diaconis
8. Alex Krizhevsky
9. Jimmy Ba
10. Oriol Vinyals

Who trusts Ryan L. Murphy overall

1. William L. Hamilton
2. Vijay Prakash Dwivedi
3. Jiaxuan You
4. Zhengdao Chen
5. Pan Li
6. Yan Zhang
7. Balasubramaniam Srinivasan
8. Jure Leskovec
9. Rex Ying
10. Lei Chen

Papers from Ryan L. Murphy's perspective

Ordered by how strongly the researchers Ryan L. Murphy 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. 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

• Relational Pooling for Graph Representations (2019) — 285 citations
• Janossy Pooling: Learning Deep Permutation-Invariant Functions for Variable-Size Inputs (2018) — 214 citations