Danilo P. Mandic

Computational Mathematics · IEEE Signal Processing Magazine · 2001–2017

A researcher in the Science corpus: 12 papers; trusts 77 researchers and is trusted by 25 researchers here, as inferred from the citation graph.

Subfields

• Computational Mathematics — 132 citations into it
• Computational Theory and Mathematics — 76 citations into it
• Artificial Intelligence — 74 citations into it; #13,531 of 35,345 by trust here, #17,412 by citations
• Signal Processing — 62 citations into it; #3,197 of 7,572 by trust here, #2,990 by citations
• Computational Mechanics — 61 citations into it; #9,626 of 10,001 by trust here, #6,582 by citations
• Computer Vision and Pattern Recognition — 49 citations into it
• Atomic and Molecular Physics, and Optics — 31 citations into it
• Control and Systems Engineering — 29 citations into it; #1,009 of 8,257 by trust here, #2,890 by citations

Whom Danilo P. Mandic trusts overall

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

1. Paul J. Werbos
2. Zhaohua Wu
3. Gabriel Rilling
4. Norden E. Huang
5. Patrick Flandrin
6. Kenneth M. Rose
7. Naveed Urr Rehman
8. Ivan V. Oseledets
9. Lieven De Lathauwer
10. Tomaso A. Poggio

Who trusts Danilo P. Mandic overall

1. Naveed Urr Rehman
2. Ruiyang Zhang
3. Petr Kadlec
4. Heng Li
5. Anne Humeau-Heurtier
6. Jiajun Han
7. Yang Liu
8. Kamran Kowsari
9. Wei Hu
10. Hania Aftab

Papers from Danilo P. Mandic's perspective

Ordered by how strongly the researchers Danilo P. Mandic trusts endorse each paper. Same corpus, a different order for every researcher.

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

Their papers

• Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions (2016) — 600 citations
• Multivariate multiscale entropy: a tool for complexity analysis of multichannel data. (2011) — 327 citations
• Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 2 Applications and Future Perspectives (2017) — 341 citations
• A Complex Least Squares Enhanced Smart DFT Technique for Power System Frequency Estimation (2017) — 74 citations
• Tensor Decompositions for Signal Processing Applications: From two-way to multiway component analysis (2014) — 1,400 citations
• Empirical Mode Decomposition-Based Time-Frequency Analysis of Multivariate Signals: The Power of Adaptive Data Analysis (2013) — 392 citations
• Filter Bank Property of Multivariate Empirical Mode Decomposition (2011) — 428 citations
• Multivariate empirical mode decomposition (2010) — 1,069 citations
• The Quaternion LMS Algorithm for Adaptive Filtering of Hypercomplex Processes (2009) — 317 citations
• Complex Valued Nonlinear Adaptive Filters: Noncircularity, Widely Linear and Neural Models (2009) — 579 citations
• The Delay Vector Variance Method for Detecting Determinism and Nonlinearity in Time Series (2004) — 152 citations
• Recurrent Neural Networks for Prediction: Learning Algorithms, Architectures and Stability (2001) — 630 citations