Jim Mutch

Computer Vision and Pattern Recognition · 2006–2016

A researcher in the Science corpus: 6 papers; trusts 129 researchers and is trusted by 776 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 67 citations into it; #1,558 of 21,575 by trust here, #5,565 by citations
• Cognitive Neuroscience — 41 citations into it; #4,365 of 10,852 by trust here, #4,352 by citations
• Representation & Self-Supervised Learning — 28 citations into it
• Artificial Intelligence — 25 citations into it; #4,187 of 35,345 by trust here, #10,798 by citations
• Model Architectures — 17 citations into it
• Neuroscience — 12 citations into it
• Media Technology — 7 citations into it

Whom Jim Mutch trusts overall

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

1. Tomaso A. Poggio
2. Thomas Serre
3. Maximilian Riesenhuber
4. Yann LeCun
5. Li Fei-Fei
6. Alex Krizhevsky
7. Geoffrey E. Hinton
8. Stéphane Mallat
9. Rob Fergus
10. Yoshua Bengio

Who trusts Jim Mutch overall

1. Marc'Aurelio Ranzato
2. Yann LeCun
3. Clément Farabet
4. Joel Z. Leibo
5. Jaeyong Sung
6. Sanja Fidler
7. Juergen Gall
8. Koray Kavukcuoglu
9. Fabio Anselmi
10. Jun Liu

Papers from Jim Mutch's perspective

Ordered by how strongly the researchers Jim Mutch 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. Long Short-Term Memory
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. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Multiclass Object Recognition with Sparse, Localized Features (2006) — 548 citations
• Unsupervised learning of invariant representations with low sample complexity: the magic of sensory cortex or a new framework for machine learning? (2013) — 86 citations
• Unsupervised learning of invariant representations (2016) — 102 citations
• Computational role of eccentricity dependent cortical magnification (2014) — 30 citations
• Unsupervised Learning of Invariant Representations in Hierarchical Architectures (2013) — 31 citations
• Automated home-cage behavioural phenotyping of mice. (2010) — 301 citations