Suvranu De
Artificial Intelligence · 2000–2019
A researcher in the Science corpus: 8 papers; trusts 20 researchers and is trusted by 28 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 45 citations into it
- Statistical and Nonlinear Physics — 38 citations into it
- Computational Mechanics — 21 citations into it; #9,956 of 10,001 by trust here, #8,258 by citations
- AI for Physical Sciences — 19 citations into it
- Computer Vision and Pattern Recognition — 17 citations into it
- Electrical and Electronic Engineering — 13 citations into it
- Mechanics of Materials — 12 citations into it; #432 of 6,291 by trust here, #1,610 by citations
- Numerical Methods & Scientific Computing — 7 citations into it
Whom Suvranu De trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Klaus-Jürgen Bathe
- Gregory J. Wagner
- Maizar Raissi
- Wing Kam Liu
- Diederik P. Kingma
- Paris Perdikaris
- Stanley J. Osher
- G. Em Karniadakis
- F. Sahli Costabal
- Mengyu Chu
Who trusts Suvranu De overall
- Guirong Liu
- Elyas Shivanian
- Jiun-Shyan Chen
- Jens Markus Melenk
- Yuantong T. Gu
- Michael Hillman
- Klaus-Jürgen Bathe
- Ivo Babuska
- Sergio R. Idelsohn
- J. Xiao
Papers from Suvranu De's perspective
Ordered by how strongly the researchers Suvranu De trusts endorse each paper. Same corpus, a different order for every researcher.
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Dropout: a simple way to prevent neural networks from overfitting
- Intriguing properties of neural networks
- Attention is All you Need (Neural Information Processing Systems)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Mechanical properties of the hexagonal boron nitride monolayer: Ab initio study (2011) — 405 citations
- Mechanical stabilities of silicene (2013) — 179 citations
- A deep learning-based hybrid approach for the solution of multiphysics problems in electrosurgery (2019) — 21 citations
- Displacement/pressure mixed interpolation in the method of finite spheres (2001) — 61 citations
- Integrating machine learning and multiscale modeling—perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences (2019) — 551 citations
- Multiscale modeling meets machine learning: What can we learn? (2019) — 341 citations
- Towards an efficient meshless computational technique: the method of finite spheres (2001) — 78 citations
- The method of finite spheres (2000) — 319 citations