Fabio Anselmi

Computer Vision and Pattern Recognition · 2013–2016

A researcher in the Science corpus: 8 papers; trusts 93 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 92 citations into it; #10,252 of 21,575 by trust here, #7,584 by citations
• Cognitive Neuroscience — 87 citations into it; #2,151 of 10,852 by trust here, #2,465 by citations
• Representation & Self-Supervised Learning — 42 citations into it; #1,655 of 3,145 by trust here, #1,781 by citations
• Artificial Intelligence — 36 citations into it; #16,384 of 35,345 by trust here, #15,004 by citations
• Neuroscience — 29 citations into it
• Model Architectures — 27 citations into it
• Molecular Biology — 14 citations into it
• Cellular and Molecular Neuroscience — 12 citations into it

Whom Fabio Anselmi trusts overall

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

1. Tomaso A. Poggio
2. Stéphane Mallat
3. Kunihiko Fukushima
4. Yann LeCun
5. Yoshua Bengio
6. David H. Hubel
7. Alex Krizhevsky
8. Geoffrey E. Hinton
9. Maximilian Riesenhuber
10. Nuo Li

Who trusts Fabio Anselmi overall

1. Joel Z. Leibo
2. Stefano Soatto
3. Alessandro Achille
4. Tomaso A. Poggio
5. Taco Cohen
6. Lorenzo Rosasco
7. Qianli Liao
8. Jure Sokolić
9. Max Welling
10. Raja Giryes

Papers from Fabio Anselmi's perspective

Ordered by how strongly the researchers Fabio Anselmi 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. Going deeper with convolutions
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Unsupervised learning of invariant representations with low sample complexity: the magic of sensory cortex or a new framework for machine learning? (2013) — 86 citations
• On Invariance and Selectivity in Representation Learning (2015) — 110 citations
• Unsupervised learning of invariant representations (2016) — 102 citations
• Deep Convolutional Networks are Hierarchical Kernel Machines (2015) — 54 citations
• Visual Cortex and Deep Networks: Learning Invariant Representations (2016) — 75 citations
• The Invariance Hypothesis Implies Domain-Specific Regions in Visual Cortex (2014) — 35 citations
• View-tolerant face recognition and Hebbian learning imply mirror-symmetric neural tuning to head orientation (2016) — 57 citations
• Unsupervised Learning of Invariant Representations in Hierarchical Architectures (2013) — 31 citations