Jiachun Liao

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 4 papers; trusts 35 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 98 citations into it
• Safety, Robustness & Interpretability — 35 citations into it
• Safety Research — 31 citations into it
• Privacy & Security — 25 citations into it
• Computer Vision and Pattern Recognition — 17 citations into it
• Statistics and Probability — 13 citations into it
• AI Ethics, Fairness & Society — 11 citations into it; #422 of 494 by trust here, #464 by citations
• Statistical Learning Theory — 9 citations into it

Whom Jiachun Liao trusts overall

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

1. Cynthia Dwork
2. Flávio du Pin Calmon
3. Martín Abadi
4. Tan Minh Nguyen
5. Sergey Ioffe
6. Peter Kairouz
7. Alexander Kraskov
8. John C. Duchi
9. Christian Szegedy
10. Davide Anguita

Who trusts Jiachun Liao overall

1. Simon Caton
2. Peter Kairouz
3. Enyan Dai

Papers from Jiachun Liao's perspective

Ordered by how strongly the researchers Jiachun Liao 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. ImageNet: A large-scale hierarchical image database
6. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
7. Learning Multiple Layers of Features from Tiny Images
8. Long Short-Term Memory
9. GENERATIVE ADVERSARIAL NETS
10. Attention is All you Need (Neural Information Processing Systems)

Their papers

• A Better Bound Gives a Hundred Rounds: Enhanced Privacy Guarantees via f-Divergences (2020) — 42 citations
• Theoretical Guarantees for Model Auditing with Finite Adversaries (2019)
• Learning Generative Adversarial RePresentations (GAP) under Fairness and Censoring Constraints (2019) — 22 citations
• Censored and Fair Universal Representations using Generative Adversarial Models (2019) — 18 citations