Shawn Hershey

Computer Vision and Pattern Recognition · 2016–2021

A researcher in the Science corpus: 6 papers; trusts 131 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 71 citations into it; #15,470 of 21,575 by trust here, #4,438 by citations
• Signal Processing — 67 citations into it; #2,352 of 7,572 by trust here, #459 by citations
• Artificial Intelligence — 66 citations into it; #22,520 of 35,345 by trust here, #15,542 by citations
• Representation & Self-Supervised Learning — 37 citations into it; #2,222 of 3,145 by trust here, #1,913 by citations
• Optimization & Training — 10 citations into it
• Multimodal Learning — 8 citations into it

Whom Shawn Hershey trusts overall

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

1. Geoffrey E. Hinton
2. Kaiming He
3. Diederik P. Kingma
4. Alex Krizhevsky
5. Jimmy Ba
6. Karen Simonyan
7. Ilya Sutskever
8. Justin Salamon
9. Annamaria Mesaros
10. Yoshua Bengio

Who trusts Shawn Hershey overall

1. Eduardo Fonseca
2. Xavier Favory
3. Aren Jansen
4. Dan Stowell
5. Antoine Miech
6. Qiuqiang Kong
7. D. Ellis
8. Yin-wei Wei
9. Xavier Serra
10. Arsha Nagrani

Papers from Shawn Hershey's perspective

Ordered by how strongly the researchers Shawn Hershey 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. ImageNet: A large-scale hierarchical image database
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. Gradient-based learning applied to document recognition
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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• CNN architectures for large-scale audio classification (2016) — 3,011 citations
• Unsupervised Learning of Semantic Audio Representations (2017) — 137 citations
• Coincidence, Categorization, and Consolidation: Learning to Recognize Sounds with Minimal Supervision (2019) — 32 citations
• The Benefit of Temporally-Strong Labels in Audio Event Classification (2021) — 159 citations
• Self-Supervised Learning from Automatically Separated Sound Scenes (2021) — 13 citations
• Addressing Missing Labels in Large-Scale Sound Event Recognition Using a Teacher-Student Framework With Loss Masking (2020) — 27 citations