Burigede Liu
Statistical and Nonlinear Physics · 2020–2023
A researcher in the Science corpus: 7 papers; trusts 38 researchers and is trusted by 19 researchers here, as inferred from the citation graph.
Subfields
- Statistical and Nonlinear Physics — 73 citations into it; #1,497 of 10,127 by trust here, #954 by citations
- Artificial Intelligence — 69 citations into it; #14,013 of 35,345 by trust here, #11,327 by citations
- Computer Vision and Pattern Recognition — 40 citations into it
- Computational Mechanics — 38 citations into it; #7,184 of 10,001 by trust here, #2,574 by citations
- Model Architectures — 17 citations into it
- Statistics and Probability — 17 citations into it
- Mathematical Physics — 16 citations into it
- Atmospheric Science — 14 citations into it; #2,919 of 4,804 by trust here, #1,475 by citations
Whom Burigede Liu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Zong-Yi Li
- Nikola B. Kovachki
- Kaiming He
- Maizar Raissi
- Andrew M. Stuart
- Geoffrey E. Hinton
- Yann LeCun
- Yoshua Bengio
- Diederik P. Kingma
- Lu Lu
Who trusts Burigede Liu overall
- Zong-Yi Li
- Nikola B. Kovachki
- Shuhao Cao
- Alasdair Tran
- Zijie Li
- Haixu Wu
- Johannes Brandstetter
- Gaurav Gupta
- Lu Lu
- Alexander Patrick Mathews
Papers from Burigede Liu's perspective
Ordered by how strongly the researchers Burigede Liu trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Learning Multiple Layers of Features from Tiny Images
- ImageNet: A large-scale hierarchical image database
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Physics-Informed Neural Operator for Learning Partial Differential Equations (2021) — 856 citations
- Multipole Graph Neural Operator for Parametric Partial Differential Equations (2020) — 539 citations
- Neural Operator: Learning Maps Between Function Spaces With Applications to PDEs (2023) — 1,356 citations
- Fourier Neural Operator with Learned Deformations for PDEs on General Geometries (2022) — 645 citations
- Neural Operator: Learning Maps Between Function Spaces (2021) — 645 citations
- Fourier Neural Operator for Parametric Partial Differential Equations (2020) — 4,467 citations
- Neural Operator: Graph Kernel Network for Partial Differential Equations (2020) — 1,099 citations