Megha Srivastava

Artificial Intelligence · 2018–2022

A researcher in the Science corpus: 5 papers; trusts 73 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 87 citations into it
• Computer Vision and Pattern Recognition — 54 citations into it
• Natural Language Processing — 48 citations into it
• Reinforcement Learning — 29 citations into it
• Safety, Robustness & Interpretability — 23 citations into it; #891 of 2,251 by trust here, #758 by citations
• Control and Systems Engineering — 13 citations into it
• Optimization & Training — 13 citations into it
• Statistics and Probability — 11 citations into it; #5,087 of 12,932 by trust here, #1,688 by citations

Whom Megha Srivastava trusts overall

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

1. Hongseok Namkoong
2. John C. Duchi
3. Yishay Mansour
4. Geoffrey E. Hinton
5. Nicolai Meinshausen
6. Lotfi A. Zadeh
7. Alex Krizhevsky
8. Yoshua Bengio
9. Diederik P. Kingma
10. Jan Peters

Who trusts Megha Srivastava overall

1. Shiori Sagawa
2. Rohan Taori
3. Elliot Creager
4. Tatsunori B. Hashimoto
5. Percy Liang
6. Maxime Cauchois
7. Pang Wei Koh
8. Thomas Liao
9. Jörn-Henrik Jacobsen
10. Runtian Zhai

Papers from Megha Srivastava's perspective

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

Their papers

• LILA: Language-Informed Latent Actions (2021) — 42 citations
• Robustness to Spurious Correlations via Human Annotations (2020) — 103 citations
• Fairness Without Demographics in Repeated Loss Minimization (2018) — 661 citations
• Do Users Write More Insecure Code with AI Assistants? (2022) — 397 citations
• An Empirical Analysis of Backward Compatibility in Machine Learning Systems (2020) — 53 citations