Noa García

Computer Vision and Pattern Recognition · 2019–2022

A researcher in the Science corpus: 3 papers; trusts 476 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 83 citations into it
• Artificial Intelligence — 74 citations into it
• Natural Language Processing — 33 citations into it
• Multimodal Learning — 30 citations into it
• Question Answering & Reading Comprehension — 20 citations into it
• Evaluation & Benchmarks — 13 citations into it
• Safety, Robustness & Interpretability — 13 citations into it
• Representation & Self-Supervised Learning — 10 citations into it

Whom Noa García trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Diederik P. Kingma
5. Alex Krizhevsky
6. Ilya Sutskever
7. Sepp Hochreiter
8. Ross B. Girshick
9. Yann LeCun
10. Jimmy Ba

Who trusts Noa García overall

1. Devshree Patel
2. Antoine Yang

Papers from Noa García's perspective

Ordered by how strongly the researchers Noa García 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 classification with deep convolutional neural networks
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. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• BERT Representations for Video Question Answering (2020) — 118 citations
• KnowIT VQA: Answering Knowledge-Based Questions about Videos (2019) — 97 citations
• Quantifying Societal Bias Amplification in Image Captioning (2022) — 63 citations