Pratyusha Kalluri

Artificial Intelligence · 2018–2022

A researcher in the Science corpus: 6 papers; trusts 62 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 132 citations into it; #29,298 of 35,345 by trust here, #2,292 by citations
• Computer Vision and Pattern Recognition — 92 citations into it
• Safety, Robustness & Interpretability — 49 citations into it; #1,215 of 2,251 by trust here, #168 by citations
• Natural Language Processing — 28 citations into it
• Representation & Self-Supervised Learning — 28 citations into it; #2,670 of 3,145 by trust here, #2,404 by citations
• Generative Models — 23 citations into it
• Probabilistic & Bayesian Methods — 16 citations into it
• Safety Research — 16 citations into it; #4,223 of 5,829 by trust here, #1,393 by citations

Whom Pratyusha Kalluri trusts overall

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

1. Diederik P. Kingma
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Kaiming He
6. Yann LeCun
7. Ilya Sutskever
8. Ruslan Salakhutdinov
9. Max Welling
10. Danilo Jimenez Rezende

Who trusts Pratyusha Kalluri overall

1. Irene Solaiman
2. Abeba Birhane
3. Ben Shneiderman
4. Jiaming Song
5. Congzheng Song
6. Luca Oneto
7. Iason Gabriel
8. Maarten Sap
9. Sina Baharlouei
10. Stefano Ermon

Papers from Pratyusha Kalluri's perspective

Ordered by how strongly the researchers Pratyusha Kalluri 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. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• On the Opportunities and Risks of Foundation Models (2021) — 6,923 citations
• When and why vision-language models behave like bags-of-words, and what to do about it? (2022) — 670 citations
• Learning Controllable Fair Representations (2018) — 188 citations
• The Values Encoded in Machine Learning Research (2021) — 401 citations
• Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scale (2022) — 535 citations
• Don’t ask if artificial intelligence is good or fair, ask how it shifts power (2020) — 218 citations