Russell A. Epstein
Cognitive Neuroscience · 1998–2020
A researcher in the Science corpus: 5 papers; trusts 20 researchers and is trusted by 68 researchers here, as inferred from the citation graph.
Subfields
- Cognitive Neuroscience — 75 citations into it; #1,820 of 10,852 by trust here, #5,844 by citations
- Cellular and Molecular Neuroscience — 20 citations into it
- Computer Vision and Pattern Recognition — 18 citations into it; #18,407 of 21,575 by trust here, #19,766 by citations
- Automotive Engineering — 14 citations into it; #231 of 3,081 by trust here, #1,728 by citations
- Developmental and Educational Psychology — 13 citations into it; #1,336 of 8,223 by trust here, #5,027 by citations
- Neuroscience — 10 citations into it; #56 of 487 by trust here, #59 by citations
- Representation & Self-Supervised Learning — 6 citations into it
Whom Russell A. Epstein trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Nancy Kanwisher
- Jerry A. Fodor
- Neil Burgess
- Eleanor A. Maguire
- Patrick Byrne
- Moshe Bar
- Aude Oliva
- Zoe Kourtzi
- Suzanna Becker
- Elissa Michele Aminoff
Who trusts Russell A. Epstein overall
- Nancy Kanwisher
- Tobias Egner
- Neil Burgess
- Brian J. Scholl
- Kalanit Grill-Spector
- Moshe Bar
- Matthew Brett
- Roberto Cabeza
- Mark D'Esposito
- Bruno Rossion
Papers from Russell A. Epstein's perspective
Ordered by how strongly the researchers Russell A. Epstein 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)
- Attention is All you Need (Neural Information Processing Systems)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Going deeper with convolutions
- ImageNet: A large-scale hierarchical image database
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- The cortical basis of visual scene processing (2005) — 153 citations
- Object representations in the human brain reflect the co-occurrence statistics of vision and language (2020) — 90 citations
- The cognitive map in humans: Spatial navigation and beyond (2017) — 823 citations
- Parahippocampal and retrosplenial contributions to human spatial navigation (2008) — 943 citations
- A cortical representation of the local visual environment (1998)