Edward Ott

Artificial Intelligence · 2000–2019

A researcher in the Science corpus: 8 papers; trusts 11 researchers and is trusted by 22 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 59 citations into it; #19,565 of 35,345 by trust here, #13,353 by citations
• Electrical and Electronic Engineering — 28 citations into it
• Model Architectures — 25 citations into it
• Optimization & Training — 12 citations into it
• Signal Processing — 10 citations into it
• Statistical and Nonlinear Physics — 9 citations into it; #4,994 of 10,127 by trust here, #2,263 by citations
• Time-Series & Forecasting — 5 citations into it

Whom Edward Ott trusts overall

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

1. Peter V. E. McClintock
2. Steven H. Strogatz
3. Sepp Hochreiter
4. Paul J. Werbos
5. Yoshua Bengio
6. Geoffrey E. Hinton
7. Herbert Jaeger
8. Alex Graves
9. Diederik P. Kingma
10. Jaideep Pathak

Who trusts Edward Ott overall

1. Oleh E. Omel'chenko
2. Hayato Chiba
3. Carlo R. Laing
4. D. Paz'o
5. Ernest Montbri'o
6. Florian Dörfler
7. Per Sebastian Skardal
8. Francisco Aparecido Rodrigues
9. Stefano Boccaletti
10. Federico Battiston

Papers from Edward Ott's perspective

Ordered by how strongly the researchers Edward Ott 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. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
7. Human-level control through deep reinforcement learning
8. Attention is All you Need (Neural Information Processing Systems)
9. Understanding the difficulty of training deep feedforward neural networks
10. Deep Learning

Their papers

• Theoretical mechanics: Crowd synchrony on the Millennium Bridge (2005) — 617 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
• Low dimensional behavior of large systems of globally coupled oscillators. (2008) — 1,055 citations
• Explosions of chaotic sets (2000) — 78 citations