Nham Le — accepted AI-conference papers

2023

Towards Reliable Neural Specifications

Chuqin Geng, Nham Le, Xiaojie Xu, Zhaoyue Wang, Arie Gurfinkel, Xujie Si

ICML 2023oral

Having reliable specifications is an unavoidable challenge in achieving verifiable correctness, robustness, and interpretability of AI systems. Existing specifications for neural networks are in the paradigm of data as specification. That is, the local neighborhood centering around a reference input…

Cited by 21 Source ↗PDF ↗Scholar ↗

2020

Explain by Evidence: An Explainable Memory-based Neural Network for Question Answering

Quan Hung Tran, Nhan Dam, Tuan Lai, Franck Dernoncourt, Trung Le, Nham Le +1

COLING 2020main

Interpretability and explainability of deep neural net models are always challenging due to their size and complexity. Many previous works focused on visualizing internal components of neural networks to represent them through human-friendly concepts. On the other hand, in real life, when making a d…

Cited by 7 Source ↗PDF ↗Scholar ↗