Martin Schrimpf

Computer Vision and Pattern Recognition · 2018–2020

A researcher in the Science corpus: 6 papers; trusts 185 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 130 citations into it
• Artificial Intelligence — 123 citations into it
• Cognitive Neuroscience — 99 citations into it
• Model Architectures — 37 citations into it
• Natural Language Processing — 31 citations into it
• Representation & Self-Supervised Learning — 28 citations into it
• Neuroscience — 21 citations into it
• Large Language Models — 18 citations into it

Whom Martin Schrimpf trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Ilya Sutskever
6. Diederik P. Kingma
7. Yann LeCun
8. Richard Socher
9. Christian Szegedy
10. Karen Simonyan

Who trusts Martin Schrimpf overall

1. Yu Takagi
2. Pouya Bashivan
3. Katherine L. Hermann
4. Damián E. Blasi
5. Minyoung Huh
6. Grace W. Lindsay
7. Chengxu Zhuang
8. Shinji Nishimoto
9. Idan Asher Blank
10. Kohitij Kar

Papers from Martin Schrimpf's perspective

Ordered by how strongly the researchers Martin Schrimpf 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation (2020) — 385 citations
• Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs (2019) — 333 citations
• The neural architecture of language: Integrative modeling converges on predictive processing (2020) — 663 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
• Unsupervised neural network models of the ventral visual stream (2020) — 414 citations