E. Fedorenko

Cognitive Neuroscience · 2006–2020

A researcher in the Science corpus: 8 papers; trusts 27 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Cognitive Neuroscience — 127 citations into it; #2,699 of 10,852 by trust here, #2,494 by citations
• Artificial Intelligence — 64 citations into it
• Natural Language Processing — 45 citations into it
• Developmental and Educational Psychology — 31 citations into it
• Representation & Self-Supervised Learning — 18 citations into it
• Large Language Models — 17 citations into it
• Experimental and Cognitive Psychology — 15 citations into it
• Radiology, Nuclear Medicine and Imaging — 13 citations into it

Whom E. Fedorenko trusts overall

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

1. John S. Duncan
2. Nancy Kanwisher
3. D. L. Hintzman
4. Joshua B. Julian
5. Earl K. Miller
6. Edward Gibson
7. Rebecca Saxe
8. Nikolaus Kriegeskorte
9. Nathalie Tzourio-Mazoyer
10. Po-Jang Hsieh

Who trusts E. Fedorenko overall

1. Naomi P. Friedman
2. Alex Warstadt
3. Mark A. Chevillet
4. Joshua B. Julian
5. Nancy Kanwisher
6. Jon Sprouse
7. James Davey
8. John S. Duncan
9. Trevor W. Robbins
10. Leila Wehbe

Papers from E. Fedorenko's perspective

Ordered by how strongly the researchers E. Fedorenko trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
3. GloVe: Global Vectors for Word Representation
4. Language Models are Unsupervised Multitask Learners
5. Long Short-Term Memory
6. An integrative theory of prefrontal cortex function.
7. 5分で分かる!? 有名論文ナナメ読み：Jacob Devlin et al. : BERT : Pre-training of Deep Bidirectional Transformers for Language Understanding
8. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer (Journal of machine learning research)
9. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
10. Improving Language Understanding by Generative Pre-Training

Their papers

• The neural architecture of language: Integrative modeling converges on predictive processing (2020) — 663 citations
• Toward a universal decoder of linguistic meaning from brain activation (2018) — 379 citations
• Broad domain generality in focal regions of frontal and parietal cortex (2013) — 932 citations
• An algorithmic method for functionally defining regions of interest in the ventral visual pathway (2012) — 338 citations
• Language processing in the occipital cortex of congenitally blind adults (2011) — 427 citations
• Weak quantitative standards in linguistics research. (2010) — 123 citations
• New method for fMRI investigations of language: defining ROIs functionally in individual subjects. (2010) — 719 citations
• The nature of working memory capacity in sentence comprehension : Evidence against domain-specific working memory resources (2006) — 189 citations