Andrea Frome
Computer Vision and Pattern Recognition · 2004–2019
A researcher in the Science corpus: 9 papers; trusts 627 researchers and is trusted by 1,026 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 154 citations into it; #1,347 of 21,575 by trust here, #2,177 by citations
- Artificial Intelligence — 140 citations into it; #4,190 of 35,345 by trust here, #3,023 by citations
- Efficiency & Compression — 53 citations into it
- Representation & Self-Supervised Learning — 28 citations into it; #1,524 of 3,145 by trust here, #848 by citations
- Evaluation & Benchmarks — 24 citations into it
- Safety, Robustness & Interpretability — 21 citations into it
- Optimization & Training — 20 citations into it
- Natural Language Processing — 15 citations into it
Whom Andrea Frome trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Alex Krizhevsky
- David G. Lowe
- Yoshua Bengio
- Kaiming He
- Yann LeCun
- Ilya Sutskever
- Diederik P. Kingma
- Ross B. Girshick
- Andrew Zisserman
Who trusts Andrea Frome overall
- Zeynep Akata
- Jia Deng
- Bernt Schiele
- Yi Yang
- Trevor Darrell
- Ruslan Salakhutdinov
- Yongqin Xian
- Quoc V. Le
- Kristen Grauman
- Yoshua Bengio
Papers from Andrea Frome's perspective
Ordered by how strongly the researchers Andrea Frome trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
Their papers
- Zero-Shot Learning by Convex Combination of Semantic Embeddings (2013) — 981 citations
- Learning Globally-Consistent Local Distance Functions for Shape-Based Image Retrieval and Classification (2007) — 429 citations
- Recognizing Objects in Range Data Using Regional Point Descriptors (2004) — 985 citations
- Attention for Fine-Grained Categorization (2014) — 160 citations
- Large-scale privacy protection in Google Street View (2009) — 284 citations
- Large-Scale Object Classification Using Label Relation Graphs (2014) — 481 citations
- What Do Compressed Deep Neural Networks Forget (2019) — 221 citations
- Selective Brain Damage: Measuring the Disparate Impact of Model Pruning (2019) — 38 citations
- DeViSE: A Deep Visual-Semantic Embedding Model (2013) — 3,053 citations