A. Swanson
Ecology · bioRxiv · 2015–2018
A researcher in the Science corpus: 6 papers; trusts 88 researchers and is trusted by 1 researcher here, as inferred from the citation graph.
Subfields
- Ecology — 55 citations into it; #9,164 of 10,717 by trust here, #4,052 by citations
- Artificial Intelligence — 54 citations into it
- Computer Vision and Pattern Recognition — 52 citations into it
- Ecological Modeling — 37 citations into it; #2,362 of 2,530 by trust here, #1,385 by citations
- Model Architectures — 14 citations into it
- Representation & Self-Supervised Learning — 14 citations into it
- Social Psychology — 13 citations into it
- Media Technology — 8 citations into it
Whom A. Swanson trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- A. F. O'connell
- Geoffrey E. Hinton
- J. Andrew Royle
- Alex Krizhevsky
- Yoshua Bengio
- Karen Simonyan
- Kaiming He
- Ilya Sutskever
- J. Marcus Rowcliffe
- James D. Nichols
Who trusts A. Swanson overall
- Sara Beery
- Mohammad Sadegh Norouzzadeh
- Alexander Gómez-Villa
- Marco Willi
- Dan Morris
- Anh Totti Nguyen
- Pengzhen Ren
- Augusto Salazar
- Ben. G. Weinstein
- Margaret Kosmala
Papers from A. Swanson's perspective
Ordered by how strongly the researchers A. Swanson 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)
- ImageNet: A large-scale hierarchical image database
- ImageNet Large Scale Visual Recognition Challenge
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Going deeper with convolutions
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
Their papers
- Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning (2017) — 1,085 citations
- Evaluating relative abundance indices for terrestrial herbivores from large-scale camera trap surveys (2018) — 118 citations
- Identifying animal species in camera trap images using deep learning and citizen science (2018) — 361 citations
- Automatically identifying wild animals in camera trap images with deep learning (2017) — 51 citations
- Assessing data quality in citizen science (preprint) (2016) — 823 citations
- Snapshot Serengeti, high-frequency annotated camera trap images of 40 mammalian species in an African savanna (2015) — 524 citations