Jonas Kubilius

Computer Vision and Pattern Recognition · 2016–2020

A researcher in the Science corpus: 6 papers; trusts 306 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 121 citations into it; #14,608 of 21,575 by trust here, #7,499 by citations
• Cognitive Neuroscience — 68 citations into it; #5,048 of 10,852 by trust here, #2,494 by citations
• Artificial Intelligence — 66 citations into it
• Model Architectures — 32 citations into it
• Neuroscience — 24 citations into it
• Representation & Self-Supervised Learning — 13 citations into it
• Optimization & Training — 11 citations into it
• Aerospace Engineering — 7 citations into it

Whom Jonas Kubilius trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Christian Szegedy
4. Geoffrey E. Hinton
5. Karen Simonyan
6. David G. Lowe
7. Andrew Zisserman
8. Ilya Sutskever
9. Yoshua Bengio
10. Jia Deng

Who trusts Jonas Kubilius overall

1. Robert Geirhos
2. Grace W. Lindsay
3. Martin Schrimpf
4. Eiji Watanabe
5. Pouya Bashivan
6. Katherine L. Hermann
7. Jacques Kaiser
8. Felix Wichmann
9. Radoslaw Martin Cichy
10. Chengxu Zhuang

Papers from Jonas Kubilius's perspective

Ordered by how strongly the researchers Jonas Kubilius 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet: A large-scale hierarchical image database
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet Large Scale Visual Recognition Challenge
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation (2020) — 385 citations
• Deep Neural Networks as a Computational Model for Human Shape Sensitivity (2016) — 321 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
• Integrative Benchmarking to Advance Neurally Mechanistic Models of Human Intelligence. (2020) — 298 citations