Dan Stowell

Artificial Intelligence · 2009–2021

A researcher in the Science corpus: 8 papers; trusts 306 researchers and is trusted by 39 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 51 citations into it
• Signal Processing — 47 citations into it; #808 of 7,572 by trust here, #1,003 by citations
• Computer Vision and Pattern Recognition — 40 citations into it; #5,415 of 21,575 by trust here, #7,249 by citations
• Developmental Biology — 19 citations into it; #217 of 711 by trust here, #250 by citations
• Ecology — 16 citations into it; #3,669 of 10,717 by trust here, #8,686 by citations
• Evaluation & Benchmarks — 13 citations into it
• Model Architectures — 13 citations into it
• Statistics and Probability — 12 citations into it

Whom Dan Stowell trusts overall

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

1. Tom Fawcett
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. R. Caruana
5. Yoshua Bengio
6. Justin Salamon
7. Alex Krizhevsky
8. Kaiming He
9. Alexandru Niculescu-Mizil
10. Andrew Zisserman

Who trusts Dan Stowell overall

1. Annamaria Mesaros
2. Eduardo Fonseca
3. Toni Heittola
4. Bruno Korbar
5. Marco Tagliasacchi
6. Peter Foster
7. Tim Sainburg
8. Rory Gibb
9. Stefan Kahl
10. Emmanouil Benetos

Papers from Dan Stowell's perspective

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

Their papers

• Detection and Classification of Acoustic Scenes and Events (2015) — 748 citations
• Fast Multidimensional Entropy Estimation by $k$-d Partitioning (2009) — 71 citations
• Computational bioacoustics with deep learning: a review and roadmap (2021) — 438 citations
• Data-efficient weakly supervised learning for low-resource audio event detection using deep learning (2018) — 15 citations
• Automatic acoustic detection of birds through deep learning: The first Bird Audio Detection challenge (2018) — 372 citations
• Acoustic Scene Classification: Classifying environments from the sounds they produce (2014) — 434 citations
• Automatic large-scale classification of bird sounds is strongly improved by unsupervised feature learning (2014) — 295 citations
• An Open Dataset for Research on Audio Field Recording Archives: freefield1010 (2013) — 68 citations