Kohitij Kar

Computer Vision and Pattern Recognition · 2018–2019

A researcher in the Science corpus: 5 papers; trusts 118 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 78 citations into it
• Cognitive Neuroscience — 62 citations into it; #2,503 of 10,852 by trust here, #2,116 by citations
• Artificial Intelligence — 45 citations into it
• Model Architectures — 27 citations into it
• Neuroscience — 20 citations into it
• Optimization & Training — 9 citations into it
• Biophysics — 7 citations into it; #431 of 1,196 by trust here, #833 by citations
• Media Technology — 5 citations into it

Whom Kohitij Kar trusts overall

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

1. Christian Szegedy
2. Alex Krizhevsky
3. Kaiming He
4. Karen Simonyan
5. Geoffrey E. Hinton
6. Ilya Sutskever
7. Yoshua Bengio
8. Andrew Zisserman
9. Sergey Ioffe
10. Yann LeCun

Who trusts Kohitij Kar overall

1. Grace W. Lindsay
2. Martin Schrimpf
3. Guangyu Robert Yang
4. Andrew M. Saxe
5. Chengxu Zhuang
6. Jonas Kubilius
7. Robert Geirhos
8. Xiao-Jing Wang
9. Pouya Bashivan
10. Stephanie Nelli

Papers from Kohitij Kar's perspective

Ordered by how strongly the researchers Kohitij Kar trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. ImageNet: A large-scale hierarchical image database
4. Gradient-based learning applied to document recognition
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Long Short-Term Memory
7. ImageNet Large Scale Visual Recognition Challenge
8. Going deeper with convolutions
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Attention is All you Need (Neural Information Processing Systems)

Their papers

• Large-Scale, High-Resolution Comparison of the Core Visual Object Recognition Behavior of Humans, Monkeys, and State-of-the-Art Deep Artificial Neural Networks (2018) — 355 citations
• Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs (2019) — 333 citations
• Evidence that recurrent circuits are critical to the ventral stream’s execution of core object recognition behavior (2018) — 488 citations
• Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like? (2018) — 725 citations
• Neural population control via deep image synthesis (2018) — 464 citations