Charles F. Cadieu

Cognitive Neuroscience · 2005–2021

A researcher in the Science corpus: 8 papers; trusts 127 researchers and is trusted by 58 researchers here, as inferred from the citation graph.

Subfields

• Cognitive Neuroscience — 99 citations into it; #1,041 of 10,852 by trust here, #624 by citations
• Computer Vision and Pattern Recognition — 66 citations into it; #3,824 of 21,575 by trust here, #3,035 by citations
• Neuroscience — 32 citations into it; #191 of 487 by trust here, #84 by citations
• Artificial Intelligence — 26 citations into it
• Biophysics — 18 citations into it
• Representation & Self-Supervised Learning — 18 citations into it; #2,721 of 3,145 by trust here, #1,370 by citations
• Cellular and Molecular Neuroscience — 16 citations into it
• Molecular Biology — 16 citations into it

Whom Charles F. Cadieu trusts overall

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

1. Barbara Olshausen
2. Tomaso A. Poggio
3. Geoffrey E. Hinton
4. Yann LeCun
5. Maximilian Riesenhuber
6. Thomas Serre
7. David H. Hubel
8. Alex Krizhevsky
9. David J. Field
10. James J. DiCarlo

Who trusts Charles F. Cadieu overall

1. Nikolaus Kriegeskorte
2. Nitish Srivastava
3. Taco Cohen
4. Robert Geirhos
5. Seyed-Mahdi Khaligh-Razavi
6. Matthias Bethge
7. Dinesh Jayaraman
8. Guangyu Robert Yang
9. Daniel L. K. Yamins
10. Max Welling

Papers from Charles F. Cadieu's perspective

Ordered by how strongly the researchers Charles F. Cadieu 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. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Performance-optimized hierarchical models predict neural responses in higher visual cortex (2014) — 2,251 citations
• Learning Intermediate-Level Representations of Form and Motion from Natural Movies (2012) — 129 citations
• Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition (2014) — 837 citations
• Utility of a Deep-Learning Algorithm to Guide Novices to Acquire Echocardiograms for Limited Diagnostic Use (2021) — 362 citations
• Hierarchical Modular Optimization of Convolutional Networks Achieves Representations Similar to Macaque IT and Human Ventral Stream (2013) — 171 citations
• Detecting event-related changes of multivariate phase coupling in dynamic brain networks. (2012)
• A quantitative theory of immediate visual recognition. (2007) — 301 citations
• A Theory of Object Recognition: Computations and Circuits in the Feedforward Path of the Ventral Stream in Primate Visual Cortex (2005) — 332 citations