Cătălina Cangea

Artificial Intelligence · 2018–2022

A researcher in the Science corpus: 5 papers; trusts 136 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 94 citations into it; #29,965 of 35,345 by trust here, #21,316 by citations
• Computer Vision and Pattern Recognition — 72 citations into it; #18,404 of 21,575 by trust here, #11,498 by citations
• Model Architectures — 36 citations into it
• Graph Learning — 34 citations into it; #587 of 693 by trust here, #547 by citations
• Generative Models — 21 citations into it
• Materials Chemistry — 20 citations into it
• Molecular Biology — 18 citations into it
• Natural Language Processing — 17 citations into it

Whom Cătălina Cangea trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Kaiming He
5. Alex Krizhevsky
6. Alex Graves
7. Ilya Sutskever
8. Max Welling
9. Yann LeCun
10. Jimmy Ba

Who trusts Cătălina Cangea overall

1. Matthias Fey
2. Christopher Morris
3. Filippo Maria Bianchi
4. Ekagra Ranjan
5. Dongkwan Kim
6. Rana Hanocka
7. Vijay Prakash Dwivedi
8. Jan Eric Lenssen
9. Qian Huang
10. Xiaoxiao Li

Papers from Cătălina Cangea's perspective

Ordered by how strongly the researchers Cătălina Cangea 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
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

• Graph Density-Aware Losses for Novel Compositions in Scene Graph Generation (2020) — 60 citations
• General-purpose, long-context autoregressive modeling with Perceiver AR (2022) — 87 citations
• Structure-aware generation of drug-like molecules (2021) — 21 citations
• Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networks (2020) — 374 citations
• Towards Sparse Hierarchical Graph Classifiers (2018) — 291 citations