Letitia Parcalabescu

Artificial Intelligence · 2020–2021

A researcher in the Science corpus: 2 papers; trusts 283 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 71 citations into it
• Computer Vision and Pattern Recognition — 65 citations into it
• Multimodal Learning — 39 citations into it; #882 of 1,211 by trust here, #1,076 by citations
• Large Language Models — 18 citations into it
• Natural Language Processing — 17 citations into it
• Representation & Self-Supervised Learning — 7 citations into it

Whom Letitia Parcalabescu trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Shaoqing Ren
4. Yoshua Bengio
5. Alex Krizhevsky
6. Diederik P. Kingma
7. Ross B. Girshick
8. Ilya Sutskever
9. Jiasen Lu
10. Yann LeCun

Who trusts Letitia Parcalabescu overall

1. Jing Yu Koh

Papers from Letitia Parcalabescu's perspective

Ordered by how strongly the researchers Letitia Parcalabescu 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. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
8. Gradient-based learning applied to document recognition
9. ImageNet Large Scale Visual Recognition Challenge
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Seeing past words: Testing the cross-modal capabilities of pretrained V&L models on counting tasks (2020) — 53 citations
• MAGMA - Multimodal Augmentation of Generative Models through Adapter-based Finetuning (2021) — 112 citations