Jaideep Pathak

Artificial Intelligence · 2017–2023

A researcher in the Science corpus: 7 papers; trusts 17 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 69 citations into it; #30,177 of 35,345 by trust here, #13,163 by citations
• Model Architectures — 38 citations into it
• Electrical and Electronic Engineering — 25 citations into it
• Atmospheric Science — 24 citations into it
• Global and Planetary Change — 23 citations into it
• Statistical and Nonlinear Physics — 16 citations into it; #8,420 of 10,127 by trust here, #2,503 by citations
• Optimization & Training — 11 citations into it
• Signal Processing — 10 citations into it

Whom Jaideep Pathak trusts overall

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

1. Sepp Hochreiter
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Paul J. Werbos
5. Eugenia Kalnay
6. Diederik P. Kingma
7. Alex Graves
8. Herbert Jaeger
9. Yann LeCun
10. J. Urgen Schmidhuber

Who trusts Jaideep Pathak overall

1. Pantelis R. Vlachas
2. Steven L. Brunton
3. Nicholas Geneva
4. Brian R. Hunt
5. Zong-Yi Li
6. Petros Koumoutsakos
7. Daniel J. Gauthier
8. Nicholas Zabaras
9. Tung Nguyen
10. Themistoklis P. Sapsis

Papers from Jaideep Pathak's perspective

Ordered by how strongly the researchers Jaideep Pathak 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. 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. Learning Multiple Layers of Features from Tiny Images
9. ImageNet Large Scale Visual Recognition Challenge
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere (2023) — 322 citations
• FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators (2022) — 1,246 citations
• FourCastNet: Accelerating Global High-Resolution Weather Forecasting Using Adaptive Fourier Neural Operators (2022) — 407 citations
• Backpropagation algorithms and Reservoir Computing in Recurrent Neural Networks for the forecasting of complex spatiotemporal dynamics (2019) — 503 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