Emma Ozanich

Signal Processing · 2019

A researcher in the Science corpus: 1 paper; trusts 13 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Signal Processing — 42 citations into it
• Computer Vision and Pattern Recognition — 32 citations into it
• Model Architectures — 10 citations into it
• Representation & Self-Supervised Learning — 9 citations into it
• Speech & Audio — 8 citations into it
• Evaluation & Benchmarks — 7 citations into it
• Statistics and Probability — 7 citations into it

Whom Emma Ozanich trusts overall

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

1. Geoffrey E. Hinton
2. Radford M. Neal
3. Yoshua Bengio
4. Stéphane Mallat
5. David G. Lowe
6. Kaiming He
7. Yann LeCun
8. Alex Krizhevsky
9. Diederik P. Kingma
10. Matthew D. Zeiler

Papers from Emma Ozanich's perspective

Ordered by how strongly the researchers Emma Ozanich trusts endorse each paper. Same corpus, a different order for every researcher.

1. Gradient-based learning applied to document recognition
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. ImageNet classification with deep convolutional neural networks
4. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Dropout: a simple way to prevent neural networks from overfitting
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Machine learning in acoustics: Theory and applications. (2019) — 487 citations