Benjamin Paul Chamberlain

Artificial Intelligence · 2017–2021

A researcher in the Science corpus: 5 papers; trusts 96 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 148 citations into it; #23,349 of 35,345 by trust here, #16,771 by citations
• Graph Learning — 87 citations into it
• Statistical and Nonlinear Physics — 63 citations into it; #6,349 of 10,127 by trust here, #5,894 by citations
• Computer Vision and Pattern Recognition — 61 citations into it
• Representation & Self-Supervised Learning — 30 citations into it
• Model Architectures — 26 citations into it
• Molecular Biology — 22 citations into it
• Information Systems — 20 citations into it

Whom Benjamin Paul Chamberlain trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Andriy Mnih
4. Diederik P. Kingma
5. Alex Graves
6. Ilya Sutskever
7. Yann LeCun
8. Alex Krizhevsky
9. Kaiming He
10. Max Welling

Who trusts Benjamin Paul Chamberlain overall

1. William L. Hamilton
2. Ines Chami
3. Frederic Sala
4. Rex Ying
5. Christopher R'e
6. Jure Leskovec
7. Christopher De Sa
8. Sami Abu-El-Haija
9. Joakim Skarding
10. Michelle M. Li

Papers from Benjamin Paul Chamberlain's perspective

Ordered by how strongly the researchers Benjamin Paul Chamberlain trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Going deeper with convolutions
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Neural Embeddings of Graphs in Hyperbolic Space (2017) — 188 citations
• SIGN: Scalable Inception Graph Neural Networks (2020) — 475 citations
• Understanding over-squashing and bottlenecks on graphs via curvature (2021) — 679 citations
• GRAND: Graph Neural Diffusion (2021) — 391 citations
• Temporal Graph Networks for Deep Learning on Dynamic Graphs (2020) — 1,099 citations