Radu V. Balan

Applied Mathematics · 2005–2018

A researcher in the Science corpus: 6 papers; trusts 25 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Applied Mathematics — 31 citations into it; #2,779 of 7,620 by trust here, #1,057 by citations
• Computer Vision and Pattern Recognition — 29 citations into it; #8,924 of 21,575 by trust here, #8,502 by citations
• Artificial Intelligence — 19 citations into it; #28,880 of 35,345 by trust here, #25,809 by citations
• Signal Processing — 13 citations into it; #3,273 of 7,572 by trust here, #2,400 by citations
• Model Architectures — 9 citations into it
• Safety, Robustness & Interpretability — 7 citations into it
• Geophysics — 5 citations into it; #1,839 of 1,958 by trust here, #976 by citations
• Statistical and Nonlinear Physics — 5 citations into it

Whom Radu V. Balan trusts overall

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

1. Ole W. Christensen
2. Ingrid Daubechies
3. Stéphane Mallat
4. Karlheinz Gröchenig
5. Joan Bruna
6. Hans G. Feichtinger
7. Massimo Fornasier
8. Christian Szegedy
9. Michel Ledoux
10. Christopher Heil

Who trusts Radu V. Balan overall

1. Karlheinz Gröchenig
2. Qiyu Sun
3. E.J. Candes
4. Kevin Scaman
5. Christopher Heil
6. Götz E. Pfander
7. Henry Gouk
8. Afonso S. Bandeira
9. Youcheng Sun
10. Xiaodong Li

Papers from Radu V. Balan's perspective

Ordered by how strongly the researchers Radu V. Balan trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Long Short-Term Memory
4. Gradient-based learning applied to document recognition
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. ImageNet Large Scale Visual Recognition Challenge
7. Attention is All you Need (Neural Information Processing Systems)
8. Going deeper with convolutions
9. ImageNet: A large-scale hierarchical image database
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• On signal reconstruction without phase (2006) — 630 citations
• On Lipschitz Bounds of General Convolutional Neural Networks (2018) — 60 citations
• Lipschitz Properties for Deep Convolutional Networks (2017) — 52 citations
• A new identity for Parseval frames (2007) — 56 citations
• Density, overcompleteness, and localization of frames (2006) — 159 citations
• Density, Overcompleteness, and Localization of Frames. II. Gabor Systems (2005) — 135 citations