Rachel Lim

Safety, Robustness & Interpretability · 2019–2024

A researcher in the Science corpus: 3 papers; trusts 1 researcher and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Rachel Lim trusts

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

  1. Xiaowei Huang (in Safety, Robustness & Interpretability)

Papers from Rachel Lim's perspective

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

  1. Intriguing properties of neural networks
  2. Safety Verification of Deep Neural Networks
  3. ImageNet classification with deep convolutional neural networks
  4. Deep Learning
  5. AI2: Safety and Robustness Certification of Neural Networks with Abstract Interpretation
  6. Safety Verification of Deep Neural Networks ∗
  7. Piecewise Linear Neural Network verification: A comparative study
  8. Output Reachable Set Estimation and Verification for Multilayer Neural Networks
  9. DeepSafe: A Data-driven Approach for Checking Adversarial Robustness in Neural Networks
  10. Formal Security Analysis of Neural Networks using Symbolic Intervals

Their papers