József Fiser

Cognitive Neuroscience · 2002–2010

A researcher in the Science corpus: 4 papers; trusts 10 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Cognitive Neuroscience — 41 citations into it; #2,975 of 10,852 by trust here, #2,307 by citations
• Neuroscience — 14 citations into it
• Cognitive Science & Psychology — 13 citations into it
• Artificial Intelligence — 12 citations into it; #23,968 of 35,345 by trust here, #16,771 by citations
• Developmental and Educational Psychology — 9 citations into it; #5,465 of 8,223 by trust here, #4,023 by citations
• Probabilistic & Bayesian Methods — 9 citations into it
• Representation & Self-Supervised Learning — 7 citations into it; #2,489 of 3,145 by trust here, #2,404 by citations

Whom József Fiser trusts overall

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

1. Geoffrey E. Hinton
2. Marvin M. Chun
3. Jenny R. Saffran
4. Terrence J. Sejnowski
5. David John Cameron MacKay
6. Radford M. Neal
7. Richard N. Aslin
8. Josh Tenenbaum
9. Christoph von der Malsburg
10. F. Attneave

Who trusts József Fiser overall

1. Nikolaus Kriegeskorte
2. Laurence Aitchison
3. Patricia K. Kuhl
4. Stephen M. Fleming
5. Jenny R. Saffran
6. Máté Lengyel
7. Gergo Orbán
8. Brendan Nyhan
9. Alexandre Pouget
10. Emre O. Neftci

Papers from József Fiser's perspective

Ordered by how strongly the researchers József Fiser 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. Learning Multiple Layers of Features from Tiny Images
8. ImageNet: A large-scale hierarchical image database
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Bayesian learning of visual chunks by human observers (2008) — 276 citations
• Statistical learning of higher-order temporal structure from visual shape sequences. (2002) — 630 citations
• From the Cover: Statistical learning of new visual feature combinations by infants (2002) — 506 citations
• Statistically optimal perception and learning: from behavior to neural representations (2010) — 727 citations