John A. Bullinaria
Cognitive Neuroscience · 2000–2017
A researcher in the Science corpus: 11 papers; trusts 54 researchers and is trusted by 49 researchers here, as inferred from the citation graph.
Subfields
- Cognitive Neuroscience — 25 citations into it; #6,601 of 10,852 by trust here, #9,185 by citations
- Artificial Intelligence — 23 citations into it; #3,363 of 35,345 by trust here, #8,619 by citations
- Natural Language Processing — 19 citations into it; #1,260 of 5,688 by trust here, #2,732 by citations
- Sociology and Political Science — 15 citations into it; #8,305 of 22,756 by trust here, #18,468 by citations
- Cellular and Molecular Neuroscience — 12 citations into it
- Experimental and Cognitive Psychology — 12 citations into it; #3,439 of 9,731 by trust here, #8,015 by citations
- Social Psychology — 11 citations into it
- Molecular Biology — 9 citations into it
Whom John A. Bullinaria trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Scott E. Fahlman
- T. Landauer
- Walter Kintsch
- Peter D. Turney
- Susan T. Dumais
- Dmitri "Mitya" Chklovskii
- M. Kline
- Simon S. Haykin
- Scott McDonald
Who trusts John A. Bullinaria overall
- Andrea Soltoggio
- Peter D. Turney
- Shaosheng Cao
- Marco Baroni
- Kenneth O. Stanley
- Alessandro Lenci
- Kai Olav Ellefsen
- Sebastian Risi
- William L. Hamilton
- Alexandre Salle
Papers from John A. Bullinaria's perspective
Ordered by how strongly the researchers John A. Bullinaria 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)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
Their papers
- Extracting semantic representations from word co-occurrence statistics: A computational study (2007) — 788 citations
- Extracting semantic representations from word co-occurrence statistics: stop-lists, stemming, and SVD (2012) — 329 citations
- Lifetime Learning as a Factor in Life History Evolution (2009) — 29 citations
- Imitative and Direct Learning as Interacting Factors in Life History Evolution (2017) — 5 citations
- The effect of learning on life history evolution (2007) — 5 citations
- Evolved Dual Weight Neural Architectures to Facilitate Incremental Learning (2009) — 6 citations
- Evolutionary Advantages of Neuromodulated Plasticity in Dynamic, Reward-based Scenarios (2008) — 154 citations
- Understanding the Emergence of Modularity in Neural Systems (2007) — 47 citations
- From biological models to the evolution of robot control systems (2003) — 25 citations
- Exploring the Baldwin Effect in Evolving Adaptable Control Systems (2000) — 23 citations
- The Importance of Neurophysiological Constraints for Modelling the Emergence of Modularity