Aida Nematzadeh

Artificial Intelligence · 2017–2022

A researcher in the Science corpus: 9 papers; trusts 471 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 279 citations into it
• Computer Vision and Pattern Recognition — 225 citations into it
• Multimodal Learning — 133 citations into it
• Natural Language Processing — 104 citations into it
• Large Language Models — 66 citations into it
• Representation & Self-Supervised Learning — 55 citations into it
• Evaluation & Benchmarks — 38 citations into it
• Model Architectures — 36 citations into it; #1,808 of 2,278 by trust here, #1,855 by citations

Whom Aida Nematzadeh trusts overall

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

1. Geoffrey E. Hinton
2. Tomas Mikolov
3. Yoshua Bengio
4. Jason Aaron Edward Weston
5. Ilya Sutskever
6. Sepp Hochreiter
7. Diederik P. Kingma
8. Alex Krizhevsky
9. Yann LeCun
10. Kaiming He

Who trusts Aida Nematzadeh overall

1. Matt Le
2. Christelle Langley
3. Michal Kosinski
4. Y-Lan Boureau
5. Bogdan-Ionut Cirstea

Papers from Aida Nematzadeh's perspective

Ordered by how strongly the researchers Aida Nematzadeh 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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. ImageNet Large Scale Visual Recognition Challenge
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Scaling Language Models: Methods, Analysis & Insights from Training Gopher (2021) — 1,640 citations
• Decoupling the Role of Data, Attention, and Losses in Multimodal Transformers (2021) — 134 citations
• Visual Grounding in Video for Unsupervised Word Translation (2020) — 52 citations
• Evaluating Theory of Mind in Question Answering (2018) — 110 citations
• MAPL: Parameter-Efficient Adaptation of Unimodal Pre-Trained Models for Vision-Language Few-Shot Prompting (2022) — 59 citations
• Flamingo: a Visual Language Model for Few-Shot Learning (2022) — 6,220 citations
• A Systematic Investigation of Commonsense Understanding in Large Language Models (2021) — 8 citations
• O N M EMORY IN H UMAN AND A RTIFICIAL L ANGUAGE P ROCESSING S YSTEMS (2020)
• How Can Memory-Augmented Neural Networks Pass a False-Belief Task? (2017) — 35 citations