Boxiang Wang

Artificial Intelligence · 2021

A researcher in the Science corpus: 1 paper; trusts 9 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 19 citations into it
• Safety, Robustness & Interpretability — 11 citations into it

Whom Boxiang Wang trusts overall

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

1. Tong Wang

Papers from Boxiang Wang's perspective

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

1. Model selection and estimation in regression with grouped variables
2. Axiomatic Attribution for Deep Networks
3. Learning Important Features Through Propagating Activation Differences
4. XGBoost: A Scalable Tree Boosting System
5. Generalized Additive Models
6. Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead
7. Intelligible Models for HealthCare: Predicting Pneumonia Risk and Hospital 30-day Readmission
8. Accurate intelligible models with pairwise interactions
9. Grad-CAM: Why did you say that?
10. SmoothGrad: removing noise by adding noise

Their papers

• Partially Interpretable Estimators (PIE): Black-Box-Refined Interpretable Machine Learning (2021) — 5 citations