Siddhartha Mishra
Artificial Intelligence · 2005–2023
A researcher in the Science corpus: 8 papers; trusts 50 researchers and is trusted by 12 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 70 citations into it
- Statistical and Nonlinear Physics — 50 citations into it; #3,568 of 10,127 by trust here, #4,633 by citations
- Computational Mechanics — 24 citations into it; #5,886 of 10,001 by trust here, #8,258 by citations
- Model Architectures — 24 citations into it
- Computer Vision and Pattern Recognition — 23 citations into it
- Applied Mathematics — 17 citations into it
- Computational Theory and Mathematics — 16 citations into it
- Graph Learning — 13 citations into it
Whom Siddhartha Mishra trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Maizar Raissi
- G. Em Karniadakis
- Ameya Dilip Jagtap
- Diederik P. Kingma
- Roger Fletcher
- Alex Krizhevsky
- Geoffrey E. Hinton
- Andrew R. Barron
- Yoshua Bengio
- Yann LeCun
Who trusts Siddhartha Mishra overall
- Nikola B. Kovachki
- Salvatore Cuomo
- Praveen Chandrashekar
- Lu Lu
- Travis C. Fisher
- Shengze Cai
- Vincenzo Schiano Di Cola
- Zong-Yi Li
- Mark H. Carpenter
- Adimurthi
Papers from Siddhartha Mishra's perspective
Ordered by how strongly the researchers Siddhartha Mishra trusts endorse each paper. Same corpus, a different order for every researcher.
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- 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)
- Going deeper with convolutions
Their papers
- A Survey on Oversmoothing in Graph Neural Networks (2023) — 371 citations
- Error estimates for DeepOnets: A deep learning framework in infinite dimensions (2021) — 334 citations
- Long Expressive Memory for Sequence Modeling (2021) — 60 citations
- On universal approximation and error bounds for Fourier Neural Operators (2021) — 383 citations
- Estimates on the generalization error of physics-informed neural networks for approximating a class of inverse problems for PDEs (2020) — 357 citations
- Statistical Solutions of Hyperbolic Conservation Laws: Foundations (2016) — 52 citations
- Arbitrarily High-order Accurate Entropy Stable Essentially Nonoscillatory Schemes for Systems of Conservation Laws (2012) — 294 citations
- OPTIMAL ENTROPY SOLUTIONS FOR CONSERVATION LAWS WITH DISCONTINUOUS FLUX-FUNCTIONS (2005) — 187 citations