Steven M. Matsuoka

Cognitive Neuroscience · 2005–2020

A researcher in the Science corpus: 5 papers; trusts 55 researchers and is trusted by 41 researchers here, as inferred from the citation graph.

Subfields

• Cognitive Neuroscience — 202 citations into it
• Artificial Intelligence — 94 citations into it
• Ecology — 86 citations into it
• Molecular Biology — 84 citations into it
• Computer Vision and Pattern Recognition — 81 citations into it
• Cellular and Molecular Neuroscience — 79 citations into it
• Social Psychology — 73 citations into it
• Experimental and Cognitive Psychology — 62 citations into it

Whom Steven M. Matsuoka trusts overall

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

1. Jerome H. Friedman
2. Linda Chao
3. Geoffrey E. Hinton
4. Antonio Torralba
5. Jorma Rissanen
6. Alex Martin
7. Robert E. Schapire
8. David G. Lowe
9. Mehrdad Hajibabaei
10. Ran Nathan

Who trusts Steven M. Matsuoka overall

1. Kerem Altun
2. Srinath Sridhar
3. Michael Hofmann
4. F. Ingrand
5. Ronald Poppe
6. Billur Barshan
7. Ferda Ofli
8. Alireza Fathi
9. Yucheng Chen
10. Varun Ganapathi

Papers from Steven M. Matsuoka's perspective

Ordered by how strongly the researchers Steven M. Matsuoka 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Climate‐change refugia in boreal North America: what, where, and for how long? (2020) — 127 citations
• Calibrating indices of avian density from non‐standardized survey data: making the most of a messy situation (2013) — 117 citations
• Using Binomial Distance-Sampling Models to Estimate the Effective Detection Radius of Point-Count Surveys Across Boreal Canada (2012) — 50 citations
• Spruce beetles and forest ecosystems in south-central alaska : A review of 30 years of research (2006) — 119 citations
• Non-commercial Research and Educational Use including without Limitation Use in Instruction at Your Institution, Sending It to Specific Colleagues That You Know, and Providing a Copy to Your Institution's Administrator. All Other Uses, Reproduction and Distribution, including without Limitation Comm (2005)