Radoslaw Martin Cichy
Cognitive Neuroscience · 2014–2019
A researcher in the Science corpus: 5 papers; trusts 92 researchers and is trusted by 9 researchers here, as inferred from the citation graph.
Subfields
- Cognitive Neuroscience — 84 citations into it; #3,244 of 10,852 by trust here, #2,832 by citations
- Computer Vision and Pattern Recognition — 55 citations into it; #20,000 of 21,575 by trust here, #10,905 by citations
- Artificial Intelligence — 31 citations into it
- Neuroscience — 22 citations into it
- Representation & Self-Supervised Learning — 14 citations into it
- Signal Processing — 10 citations into it
- Biophysics — 7 citations into it
- Model Architectures — 7 citations into it
Whom Radoslaw Martin Cichy trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Alex Krizhevsky
- Nikolaus Kriegeskorte
- Daniel L. K. Yamins
- Yoshua Bengio
- Ilya Sutskever
- James J. DiCarlo
- Maximilian Riesenhuber
- Diederik P. Kingma
- Kaiming He
Who trusts Radoslaw Martin Cichy overall
- Nikolaus Kriegeskorte
- David Watson
- Kshitij Dwivedi
- Jörn Diedrichsen
- Tijl Grootswagers
- Martin Schrimpf
- Demis Hassabis
- Tim C Kietzmann
- Zhichao Li
- Gemma Roig
Papers from Radoslaw Martin Cichy's perspective
Ordered by how strongly the researchers Radoslaw Martin Cichy trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Going deeper with convolutions
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence (2016) — 751 citations
- Deep Neural Networks as Scientific Models. (2019) — 406 citations
- Recurrence is required to capture the representational dynamics of the human visual system (2019) — 405 citations
- Multivariate pattern analysis for MEG: A comparison of dissimilarity measures (2018)
- Resolving human object recognition in space and time (2014) — 837 citations