Mauro Maggioni

Computer Vision and Pattern Recognition · 2004–2008

A researcher in the Science corpus: 7 papers; trusts 100 researchers and is trusted by 87 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 43 citations into it; #15,266 of 21,575 by trust here, #19,766 by citations
• Computational Theory and Mathematics — 38 citations into it; #7,967 of 12,182 by trust here, #752 by citations
• Representation & Self-Supervised Learning — 36 citations into it; #598 of 3,145 by trust here, #1,507 by citations
• Artificial Intelligence — 32 citations into it; #3,885 of 35,345 by trust here, #11,327 by citations
• Reinforcement Learning — 28 citations into it; #363 of 2,242 by trust here, #1,055 by citations
• Computational Mechanics — 14 citations into it; #6,433 of 10,001 by trust here, #6,287 by citations
• Applied Mathematics — 11 citations into it; #4,707 of 7,620 by trust here, #2,192 by citations
• Numerical Methods & Scientific Computing — 11 citations into it

Whom Mauro Maggioni trusts overall

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

1. Mikhail Belkin
2. Ronald R. Coifman
3. Partha Niyogi
4. Richard S. Sutton
5. Risi Kondor
6. Hrushikesh Narhar Mhaskar
7. Andrew Barto
8. Andrew Y. Ng
9. Geoffrey E. Hinton
10. Stéphane Mallat

Who trusts Mauro Maggioni overall

1. Frank Filbir
2. Hrushikesh Narhar Mhaskar
3. Sridhar Mahadevan
4. Ronald E. Parr
5. Amir-massoud Farahmand
6. Shimon Whiteson
7. Alessandro Lazaric
8. Sunil K. Narang
9. Stéphane Lafon
10. David I. Shuman

Papers from Mauro Maggioni's perspective

Ordered by how strongly the researchers Mauro Maggioni 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Proto-value Functions: A Laplacian Framework for Learning Representation and Control in Markov Decision Processes (2007) — 388 citations
• Geometric diffusions as a tool for harmonic analysis and structure definition of data: diffusion maps. (2005) — 1,850 citations
• Diffusion-driven multiscale analysis on manifolds and graphs: top-down and bottom-up constructions (2005) — 68 citations
• Diffusion Maps, Reduction Coordinates, and Low Dimensional Representation of Stochastic Systems (2008) — 315 citations
• Diffusion polynomial frames on metric measure spaces (2008) — 100 citations
• Universal Local Parametrizations via Heat Kernels and Eigenfunctions of the Laplacian (2007) — 54 citations
• Diffusion Wavelets (2004)