Towards Reliable Neural Specifications

no code implementations • 28 Oct 2022 • Chuqin Geng, Nham Le, Xiaojie Xu, Zhaoyue Wang, Arie Gurfinkel, Xujie Si

We show that by using NAP, we can verify a significant region of the input space, while still recalling 84% of the data on MNIST.

Adversarial Robustness

AutoNLU: An On-demand Cloud-based Natural Language Understanding System for Enterprises

no code implementations • Asian Chapter of the Association for Computational Linguistics 2020 • Nham Le, Tuan Lai, Trung Bui, Doo Soon Kim

With the renaissance of deep learning, neural networks have achieved promising results on many natural language understanding (NLU) tasks.

Keyphrase Extraction Natural Language Understanding

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

no code implementations • COLING 2020 • Quan Tran, Nhan Dam, Tuan Lai, Franck Dernoncourt, Trung Le, Nham Le, Dinh Phung

Interpretability and explainability of deep neural networks are challenging due to their scale, complexity, and the agreeable notions on which the explaining process rests.

Question Answering

A System for Automated Image Editing from Natural Language Commands

no code implementations • 3 Dec 2018 • Jacqueline Brixey, Ramesh Manuvinakurike, Nham Le, Tuan Lai, Walter Chang, Trung Bui

This work presents the task of modifying images in an image editing program using natural language written commands.