Emilia G'omez

Artificial Intelligence · 2012–2020

A researcher in the Science corpus: 9 papers; trusts 36 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 56 citations into it
• Computer Vision and Pattern Recognition — 36 citations into it; #13,317 of 21,575 by trust here, #19,766 by citations
• Sociology and Political Science — 29 citations into it
• Signal Processing — 28 citations into it; #3,041 of 7,572 by trust here, #3,309 by citations
• Economics and Econometrics — 25 citations into it
• Safety, Robustness & Interpretability — 25 citations into it
• Safety Research — 24 citations into it
• AI Ethics, Fairness & Society — 18 citations into it

Whom Emilia G'omez trusts overall

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

1. Masataka Goto
2. J. Stephen Downie
3. Albert S. Bregman
4. Ching-Hua Chuan
5. Zachary Chase Lipton
6. Geoffrey E. Hinton
7. Moritz Hardt
8. Alexandra Chouldechova
9. Alex Krizhevsky
10. Yoshua Bengio

Who trusts Emilia G'omez overall

1. Fabian-Robert Stöter
2. Eduardo Fonseca
3. Dmitry Bogdanov
4. Rachel M. Bittner
5. Justin Salamon
6. Colin Raffel
7. Xavier Serra

Papers from Emilia G'omez's perspective

Ordered by how strongly the researchers Emilia G'omez 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. ImageNet: A large-scale hierarchical image database
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Learning Multiple Layers of Features from Tiny Images
6. Gradient-based learning applied to document recognition
7. Long Short-Term Memory
8. Attention is All you Need (Neural Information Processing Systems)
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

• WGANSing: A Multi-Voice Singing Voice Synthesizer Based on the Wasserstein-GAN (2019) — 68 citations
• Measuring the Occupational Impact of AI: Tasks, Cognitive Abilities and AI Benchmarks (2020) — 112 citations
• Why Machine Learning May Lead to Unfairness: Evidence from Risk Assessment for Juvenile Justice in Catalonia (2019) — 100 citations
• Addressing multiple metrics of group fairness in data-driven decision making (2020) — 9 citations
• Exploring Artist Gender Bias in Music Recommendation (2020) — 46 citations
• Acoustic Scene Classification by Ensembling Gradient Boosting Machine and Convolutional Neural Networks (2017) — 29 citations
• Monoaural Audio Source Separation Using Deep Convolutional Neural Networks (2017) — 192 citations
• Essentia: An Audio Analysis Library for Music Information Retrieval (2013) — 453 citations
• Melody Extraction From Polyphonic Music Signals Using Pitch Contour Characteristics (2012) — 486 citations