191 citations · 297 across the 6 of their papers we have counts for
6 papers
Exploring the Universal Vulnerability of Prompt-based Learning Paradigm
Lei Xu, Yangyi Chen, Ganqu Cui +2
Prompt-based learning paradigm bridges the gap between pre-training and fine-tuning, and works effectively under the few-shot setting. However, we find that this learning paradigm…
R&R: Metric-guided Adversarial Sentence Generation
Lei Xu, Alfredo Cuesta-Infante, Laure Berti-Equille +1
Adversarial examples are helpful for analyzing and improving the robustness of text classifiers. Generating high-quality adversarial examples is a challenging task as it requires g…
AutoML to Date and Beyond: Challenges and Opportunities
Shubhra Kanti Karmaker Santu, Md. Mahadi Hassan, Micah J. Smith +3
As big data becomes ubiquitous across domains, and more and more stakeholders aspire to make the most of their data, demand for machine learning tools has spurred researchers to ex…
Modeling Tabular data using Conditional GAN
Lei Xu, Maria Skoularidou, Alfredo Cuesta-Infante +1
Modeling the probability distribution of rows in tabular data and generating realistic synthetic data is a non-trivial task. Tabular data usually contains a mix of discrete and con…
Input Convex Neural Networks
Brandon Amos, Lei Xu, J. Zico Kolter
This paper presents the input convex neural network architecture. These are scalar-valued (potentially deep) neural networks with constraints on the network parameters such that th…
Topic Sensitive Neural Headline Generation
Lei Xu, Ziyun Wang, Ayana +2
Neural models have recently been used in text summarization including headline generation. The model can be trained using a set of document-headline pairs. However, the model does…