David I. Shuman
Computer Vision and Pattern Recognition · 2011–2016
A researcher in the Science corpus: 6 papers; trusts 30 researchers and is trusted by 47 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 42 citations into it; #10,145 of 21,575 by trust here, #13,815 by citations
- Artificial Intelligence — 30 citations into it; #4,567 of 35,345 by trust here, #4,722 by citations
- Computational Theory and Mathematics — 27 citations into it; #2,669 of 12,182 by trust here, #3,562 by citations
- Computational Mechanics — 24 citations into it
- Graph Learning — 21 citations into it; #123 of 693 by trust here, #129 by citations
- Statistical and Nonlinear Physics — 17 citations into it; #747 of 10,127 by trust here, #484 by citations
- Media Technology — 16 citations into it
- Applied Mathematics — 15 citations into it
Whom David I. Shuman trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- David K. Hammond
- Ulrike von Luxburg
- Stéphane Lafon
- Ronald R. Coifman
- Sunil K. Narang
- Pierre Vandergheynst
- Wim Sweldens
- Gabriel Taubin
- Aliaksei Sandryhaila
- Shlomo Hoory
Who trusts David I. Shuman overall
- Nathanael Perraudin
- Michael M. Bronstein
- Pierre Vandergheynst
- Davide Boscaini
- Michael Edwards
- Michaël Defferrard
- Filippo Maria Bianchi
- Matthias Fey
- Federico Monti
- Qimai Li
Papers from David I. Shuman's perspective
Ordered by how strongly the researchers David I. Shuman 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)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Normalized cuts and image segmentation
Their papers
- The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains (2012) — 4,524 citations
- Vertex-Frequency Analysis on Graphs (2013) — 407 citations
- GSPBOX: A toolbox for signal processing on graphs (2014) — 354 citations
- Global and Local Uncertainty Principles for Signals on Graphs (2016) — 73 citations
- A Multiscale Pyramid Transform for Graph Signals (2013) — 179 citations
- Semi-Supervised Learning with Spectral Graph Wavelets (2011) — 22 citations