Brian R. Hunt

Artificial Intelligence · 2004–2019

A researcher in the Science corpus: 9 papers; trusts 31 researchers and is trusted by 45 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 62 citations into it; #19,566 of 35,345 by trust here, #13,353 by citations
• Atmospheric Science — 28 citations into it; #181 of 4,804 by trust here, #1,374 by citations
• Electrical and Electronic Engineering — 28 citations into it
• Global and Planetary Change — 28 citations into it; #374 of 10,225 by trust here, #3,398 by citations
• Model Architectures — 25 citations into it
• Optimization & Training — 13 citations into it
• Signal Processing — 11 citations into it
• Statistical and Nonlinear Physics — 11 citations into it; #4,995 of 10,127 by trust here, #2,316 by citations

Whom Brian R. Hunt trusts overall

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

1. Geir Evensen
2. Jeffrey L. Anderson
3. Milija Zupanski
4. J. Whitaker
5. Tamer Basar
6. Eugenia Kalnay
7. Ichiro Fukumori
8. Thomas M. Hamill
9. P. Houtekamer
10. Sepp Hochreiter

Who trusts Brian R. Hunt overall

1. Takemasa Miyoshi
2. Marc Bocquet
3. Stephen G. Penny
4. Pavel Sakov
5. Hong Li
6. Shunji Kotsuki
7. Eugenia Kalnay
8. Yan Chen
9. Laurent Bertino
10. Julia Tödter

Papers from Brian R. Hunt's perspective

Ordered by how strongly the researchers Brian R. Hunt trusts endorse each paper. Same corpus, a different order for every researcher.

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. ImageNet classification with deep convolutional neural networks
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Reducing storage requirements for biological sequence comparison (2004) — 445 citations
• Backpropagation algorithms and Reservoir Computing in Recurrent Neural Networks for the forecasting of complex spatiotemporal dynamics (2019) — 503 citations
• Attractor reconstruction by machine learning. (2018) — 403 citations
• Model-Free Prediction of Large Spatiotemporally Chaotic Systems from Data: A Reservoir Computing Approach. (2018) — 1,176 citations
• Hybrid Forecasting of Chaotic Processes: Using Machine Learning in Conjunction with a Knowledge-Based Model (2018) — 350 citations
• Using machine learning to replicate chaotic attractors and calculate Lyapunov exponents from data. (2017) — 674 citations
• Approximating invariant densities of metastable systems (2009) — 49 citations
• A comparative study of 4D-VAR and a 4D Ensemble Kalman Filter: perfect model simulations with Lorenz-96 (2007) — 140 citations
• Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter (2005) — 1,617 citations