Manuele Brambilla

Computer Vision and Pattern Recognition · 2013–2018

A researcher in the Science corpus: 2 papers; trusts 43 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 26 citations into it; #17,979 of 21,575 by trust here, #15,955 by citations
• Electrical and Electronic Engineering — 20 citations into it; #12,093 of 16,539 by trust here, #11,641 by citations
• Artificial Intelligence — 16 citations into it
• Computer Networks and Communications — 12 citations into it
• Cognitive Neuroscience — 9 citations into it
• Cellular and Molecular Neuroscience — 8 citations into it
• Reinforcement Learning — 7 citations into it
• Sociology and Political Science — 6 citations into it

Whom Manuele Brambilla trusts overall

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

1. Veysel Gazi
2. Maja J. Mataric
3. Richard S. Sutton
4. Marvin Minsky
5. David E. Goldberg
6. Alex Krizhevsky
7. Alex M. Andrew
8. Erol Sahin
9. Leslie Pack Kaelbling
10. Kevin M. Passino

Who trusts Manuele Brambilla overall

1. Henri Rebecq
2. Melanie Schranz
3. Daniel Gehrig
4. Michael Pfeiffer
5. Levent Bayindir
6. Martina Umlauft
7. Davide Scaramuzza
8. René Ranftl

Papers from Manuele Brambilla's perspective

Ordered by how strongly the researchers Manuele Brambilla 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. ImageNet: A large-scale hierarchical image database
6. Learning Multiple Layers of Features from Tiny Images
7. Long Short-Term Memory
8. Attention is All you Need (Neural Information Processing Systems)
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• HATS: Histograms of Averaged Time Surfaces for Robust Event-Based Object Classification (2018) — 552 citations
• Swarm robotics: a review from the swarm engineering perspective (2013) — 1,765 citations