Ha Hong

Cognitive Neuroscience · 2013–2019

A researcher in the Science corpus: 6 papers; trusts 64 researchers and is trusted by 27 researchers here, as inferred from the citation graph.

Subfields

• Cognitive Neuroscience — 73 citations into it; #1,111 of 10,852 by trust here, #619 by citations
• Computer Vision and Pattern Recognition — 71 citations into it; #6,357 of 21,575 by trust here, #3,251 by citations
• Artificial Intelligence — 43 citations into it
• Model Architectures — 25 citations into it
• Neuroscience — 17 citations into it; #192 of 487 by trust here, #84 by citations
• Molecular Biology — 13 citations into it
• Representation & Self-Supervised Learning — 13 citations into it; #2,717 of 3,145 by trust here, #1,370 by citations
• Biophysics — 10 citations into it

Whom Ha Hong trusts overall

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

1. James J. DiCarlo
2. Alex Krizhevsky
3. Nicolas Pinto
4. Yann LeCun
5. Geoffrey E. Hinton
6. David G. Lowe
7. Daniel L. K. Yamins
8. Yoshua Bengio
9. Ilya Sutskever
10. Kaiming He

Who trusts Ha Hong overall

1. Nikolaus Kriegeskorte
2. H. Francis Song
3. Grace W. Lindsay
4. Daniel L. K. Yamins
5. Robert Geirhos
6. Radoslaw Martin Cichy
7. Guangyu Robert Yang
8. Hamid Karimi-Rouzbahani
9. Leon A. Gatys
10. Seyed-Mahdi Khaligh-Razavi

Papers from Ha Hong's perspective

Ordered by how strongly the researchers Ha Hong 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. ImageNet: A large-scale hierarchical image database
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Long Short-Term Memory
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Attention is All you Need (Neural Information Processing Systems)
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
• Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition (2014) — 837 citations
• Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs (2019) — 333 citations
• Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like? (2018) — 725 citations
• Explicit information for category-orthogonal object properties increases along the ventral stream (2016) — 331 citations
• Hierarchical Modular Optimization of Convolutional Networks Achieves Representations Similar to Macaque IT and Human Ventral Stream (2013) — 171 citations