Priya Lekha Donti

Artificial Intelligence · 2017–2022

A researcher in the Science corpus: 5 papers; trusts 45 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 95 citations into it; #28,774 of 35,345 by trust here, #33,294 by citations
• Computer Vision and Pattern Recognition — 46 citations into it
• Management Science and Operations Research — 25 citations into it; #9,501 of 11,946 by trust here, #5,548 by citations
• Optimization & Training — 19 citations into it; #2,290 of 2,601 by trust here, #2,519 by citations
• Model Architectures — 18 citations into it
• Natural Language Processing — 16 citations into it
• Statistical and Nonlinear Physics — 14 citations into it
• Control and Systems Engineering — 9 citations into it

Whom Priya Lekha Donti trusts overall

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

1. Alex Graves
2. Brandon Amos
3. Geoffrey E. Hinton
4. Yann LeCun
5. Yoshua Bengio
6. Diederik P. Kingma
7. Kaiming He
8. Alex Krizhevsky
9. Chelsea Finn
10. J. Kolter

Who trusts Priya Lekha Donti overall

1. Adam N. Elmachtoub
2. Paul Grigas
3. Bryan Wilder
4. Amir-massoud Farahmand
5. Bistra N. Dilkina
6. Noam Wies
7. Yatin Nandwani

Papers from Priya Lekha Donti's perspective

Ordered by how strongly the researchers Priya Lekha Donti 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver (2019) — 319 citations
• Task-based End-to-end Model Learning (2017) — 25 citations
• Aligning artificial intelligence with climate change mitigation (2022)
• Tackling Climate Change with Machine Learning (2019) — 1,184 citations
• Task-based End-to-end Model Learning in Stochastic Optimization (2017) — 462 citations