Nicholas Geneva

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 2 papers; trusts 66 researchers and is trusted by 19 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 25 citations into it
• Statistical and Nonlinear Physics — 20 citations into it; #1,542 of 10,127 by trust here, #4,281 by citations
• Computational Mechanics — 19 citations into it; #1,840 of 10,001 by trust here, #4,450 by citations
• Statistics and Probability — 17 citations into it; #11,610 of 12,932 by trust here, #10,275 by citations
• Probabilistic & Bayesian Methods — 16 citations into it
• Optimization & Training — 15 citations into it
• Computer Vision and Pattern Recognition — 13 citations into it
• Model Architectures — 10 citations into it

Whom Nicholas Geneva trusts overall

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

1. Radford M. Neal
2. Qiang Liu
3. Yinhao Zhu
4. Diederik P. Kingma
5. Yarin Gal
6. Yann LeCun
7. David John Cameron MacKay
8. Geoffrey E. Hinton
9. Nicholas Zabaras
10. Yoshua Bengio

Who trusts Nicholas Geneva overall

1. Jared D. Willard
2. Sifan Wang
3. Han Gao
4. Benjamin Moseley
5. Pavel Izmailov
6. Luning Sun
7. Xiaowei Jia
8. Salvatore Cuomo
9. Nicholas Zabaras
10. Jian-Xun Wang

Papers from Nicholas Geneva's perspective

Ordered by how strongly the researchers Nicholas Geneva 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
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

• Quantifying model form uncertainty in Reynolds-averaged turbulence models with Bayesian deep neural networks (2018) — 141 citations
• Modeling the Dynamics of PDE Systems with Physics-Constrained Deep Auto-Regressive Networks (2019) — 349 citations