Victoria Peterson

Artificial Intelligence · 2020–2023

A researcher in the Science corpus: 3 papers; trusts 4 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 19 citations into it
• Computer Vision and Pattern Recognition — 13 citations into it
• Evaluation & Benchmarks — 5 citations into it

Whom Victoria Peterson trusts overall

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

1. Kaiming He
2. Tom Fawcett
3. Gao Huang
4. Christian Szegedy
5. Michael X. Cohen

Papers from Victoria Peterson's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet: A large-scale hierarchical image database
3. ImageNet classification with deep convolutional neural networks
4. Gradient-based learning applied to document recognition
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Attention is All you Need (Neural Information Processing Systems)
7. Rethinking the Inception Architecture for Computer Vision
8. Long Short-Term Memory
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis (2020) — 627 citations
• A supervised data-driven spatial filter denoising method for speech artifacts in intracranial electrophysiological recordings (2023)
• Differentiation of speech-induced artifacts from physiological high gamma activity in intracranial recordings (2021)