Ruoxi Jia

Artificial Intelligence · with Wang · 2019–2021

A researcher in the Science corpus: 7 papers; trusts 69 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 117 citations into it
• Safety, Robustness & Interpretability — 51 citations into it
• Computer Vision and Pattern Recognition — 39 citations into it
• Privacy & Security — 26 citations into it
• Natural Language Processing — 21 citations into it
• Signal Processing — 12 citations into it
• Model Architectures — 9 citations into it
• Computational Mechanics — 5 citations into it

Whom Ruoxi Jia trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Yann LeCun
4. Alex Krizhevsky
5. Cynthia Dwork
6. Ian J. Goodfellow
7. Yoshua Bengio
8. Karen Simonyan
9. Diederik P. Kingma
10. Lloyd S. Shapley

Who trusts Ruoxi Jia overall

1. Zheda Mai
2. Dongsub Shim
3. Weixin Liang
4. Jihwan Jeong
5. Lilly Kumari
6. Pang Wei Koh
7. Andrew Ilyas
8. Scott Sanner
9. James Y. Zou
10. Maria Rigaki

Papers from Ruoxi Jia's perspective

Ordered by how strongly the researchers Ruoxi Jia 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. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
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

• The Secret Revealer: Generative Model-Inversion Attacks Against Deep Neural Networks (2019) — 533 citations
• InfoBERT: Improving Robustness of Language Models from An Information Theoretic Perspective (2020) — 135 citations
• Selective Differential Privacy for Language Modeling (2021) — 91 citations
• Towards Efficient Data Valuation Based on the Shapley Value (2019) — 573 citations
• Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms (2019) — 303 citations
• D2P-Fed: Differentially Private Federated Learning With Efficient Communication (2020) — 29 citations
• Robust Anomaly Detection and Backdoor Attack Detection Via Differential Privacy (2019) — 201 citations