Aylin Caliskan

Artificial Intelligence · 2016–2022

A researcher in the Science corpus: 4 papers; trusts 304 researchers and is trusted by 183 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 87 citations into it; #1,635 of 35,345 by trust here, #2,350 by citations
• Safety, Robustness & Interpretability — 52 citations into it; #89 of 2,251 by trust here, #164 by citations
• Natural Language Processing — 49 citations into it; #4,026 of 5,688 by trust here, #4,250 by citations
• Computer Vision and Pattern Recognition — 42 citations into it
• Sociology and Political Science — 36 citations into it
• Representation & Self-Supervised Learning — 35 citations into it
• Cognitive Science & Psychology — 23 citations into it
• Social Sciences & Humanities — 18 citations into it

Whom Aylin Caliskan trusts overall

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

1. A. Greenwald
2. Tomas Mikolov
3. Alex Krizhevsky
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. Jeffrey Pennington
7. Kaiming He
8. Ilya Sutskever
9. Richard Socher
10. Brian A. Nosek

Who trusts Aylin Caliskan overall

1. Sunipa Dev
2. Anne Lauscher
3. Eric Michael Smith
4. Hila Gonen
5. Percy Liang
6. Emilio Ferrara
7. Adina Williams
8. Jieyu Zhao
9. Andrew Silva
10. Yulia Tsvetkov

Papers from Aylin Caliskan's perspective

Ordered by how strongly the researchers Aylin Caliskan 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. Long Short-Term Memory
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Semantics derived automatically from language corpora contain human-like biases (2016) — 3,191 citations
• Image Representations Learned With Unsupervised Pre-Training Contain Human-like Biases (2020) — 186 citations
• Detecting Emergent Intersectional Biases: Contextualized Word Embeddings Contain a Distribution of Human-like Biases (2020) — 290 citations
• Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scale (2022) — 535 citations