Fatemehsadat Mireshghallah

Artificial Intelligence · 2018–2023

A researcher in the Science corpus: 9 papers; trusts 202 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 258 citations into it
• Safety, Robustness & Interpretability — 72 citations into it
• Computer Vision and Pattern Recognition — 66 citations into it
• Privacy & Security — 55 citations into it
• Natural Language Processing — 51 citations into it
• Large Language Models — 44 citations into it
• Information Systems — 21 citations into it
• Signal Processing — 19 citations into it

Whom Fatemehsadat Mireshghallah trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Yann LeCun
5. Yoshua Bengio
6. Ilya Sutskever
7. Kaiming He
8. Jimmy Ba
9. Cynthia Dwork
10. Karen Simonyan

Who trusts Fatemehsadat Mireshghallah overall

1. Sachin Kumar
2. Abhishek Singh
3. Vidhisha Balachandran
4. Praneeth Vepakomma

Papers from Fatemehsadat Mireshghallah's perspective

Ordered by how strongly the researchers Fatemehsadat Mireshghallah trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. 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

• What Does it Mean for a Language Model to Preserve Privacy? (2022) — 331 citations
• Privacy Regularization: Joint Privacy-Utility Optimization in LanguageModels (2021) — 55 citations
• Shredder: Learning Noise Distributions to Protect Inference Privacy (2019) — 113 citations
• Mix and Match: Learning-free Controllable Text Generationusing Energy Language Models (2022) — 91 citations
• ReLeQ : A Reinforcement Learning Approach for Automatic Deep Quantization of Neural Networks (2020) — 61 citations
• Membership Inference Attacks against Language Models via Neighbourhood Comparison (2023) — 339 citations
• A Principled Approach to Learning Stochastic Representations for Privacy in Deep Neural Inference (2020) — 13 citations
• Shredder: Learning Noise to Protect Privacy with Partial DNN Inference on the Edge (2019) — 12 citations
• ReLeQ: An Automatic Reinforcement Learning Approach for Deep Quantization of Neural Networks (2018)