54 citations · 168 across the 17 of their papers we have counts for
17 papers
Knowing-how & Knowing-that: A New Task for Machine Comprehension of User Manuals
Hongru Liang, Jia Liu, Weihong Du +4
The machine reading comprehension (MRC) of user manuals has huge potential in customer service. However, current methods have trouble answering complex questions. Therefore, we int…
Deep Lifelong Cross-modal Hashing
Liming Xu, Hanqi Li, Bochuan Zheng +2
Hashing methods have made significant progress in cross-modal retrieval tasks with fast query speed and low storage cost. Among them, deep learning-based hashing achieves better pe…
Differentiable Genetic Programming for High-dimensional Symbolic Regression
Peng Zeng, Xiaotian Song, Andrew Lensen +4
Symbolic regression (SR) is the process of discovering hidden relationships from data with mathematical expressions, which is considered an effective way to reach interpretable mac…
Communication-efficient Federated Learning with Single-Step Synthetic Features Compressor for Faster Convergence
Yuhao Zhou, Mingjia Shi, Yuanxi Li +3
Reducing communication overhead in federated learning (FL) is challenging but crucial for large-scale distributed privacy-preserving machine learning. While methods utilizing spars…
Differentiable Search of Accurate and Robust Architectures
Yuwei Ou, Xiangning Xie, Shangce Gao +3
Deep neural networks (DNNs) are found to be vulnerable to adversarial attacks, and various methods have been proposed for the defense. Among these methods, adversarial training has…
Personalized Federated Learning with Hidden Information on Personalized Prior
Mingjia Shi, Yuhao Zhou, Qing Ye +1
Federated learning (FL for simplification) is a distributed machine learning technique that utilizes global servers and collaborative clients to achieve privacy-preserving global m…
Partial Differential Equations Meet Deep Neural Networks: A Survey
Shudong Huang, Wentao Feng, Chenwei Tang +1
Many problems in science and engineering can be represented by a set of partial differential equations (PDEs) through mathematical modeling. Mechanism-based computation following P…
Reconciliation of Pre-trained Models and Prototypical Neural Networks in Few-shot Named Entity Recognition
Youcheng Huang, Wenqiang Lei, Jie Fu +1
Incorporating large-scale pre-trained models with the prototypical neural networks is a de-facto paradigm in few-shot named entity recognition. Existing methods, unfortunately, are…
Deep Fair Clustering via Maximizing and Minimizing Mutual Information: Theory, Algorithm and Metric
Pengxin Zeng, Yunfan Li, Peng Hu +3
Fair clustering aims to divide data into distinct clusters while preventing sensitive attributes (\textit{e.g.}, gender, race, RNA sequencing technique) from dominating the cluster…
Interacting with Non-Cooperative User: A New Paradigm for Proactive Dialogue Policy
Wenqiang Lei, Yao Zhang, Feifan Song +5
Proactive dialogue system is able to lead the conversation to a goal topic and has advantaged potential in bargain, persuasion and negotiation. Current corpus-based learning manner…
DeFTA: A Plug-and-Play Decentralized Replacement for FedAvg
Yuhao Zhou, Minjia Shi, Yuxin Tian +2
Federated learning (FL) is identified as a crucial enabler for large-scale distributed machine learning (ML) without the need for local raw dataset sharing, substantially reducing…
Cluster-based Contrastive Disentangling for Generalized Zero-Shot Learning
Yi Gao, Chenwei Tang, Jiancheng Lv
Generalized Zero-Shot Learning (GZSL) aims to recognize both seen and unseen classes by training only the seen classes, in which the instances of unseen classes tend to be biased t…
Multi-Scale Adaptive Network for Single Image Denoising
Yuanbiao Gou, Peng Hu, Jiancheng Lv +2
Multi-scale architectures have shown effectiveness in a variety of tasks thanks to appealing cross-scale complementarity. However, existing architectures treat different scale feat…
Adversarial Retriever-Ranker for dense text retrieval
Hang Zhang, Yeyun Gong, Yelong Shen +3
Current dense text retrieval models face two typical challenges. First, they adopt a siamese dual-encoder architecture to encode queries and documents independently for fast indexi…
Unsupervised Neural Rendering for Image Hazing
Boyun Li, Yijie Lin, Xiao Liu +3
Image hazing aims to render a hazy image from a given clean one, which could be applied to a variety of practical applications such as gaming, filming, photographic filtering, and…
TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance
Fengbin Zhu, Wenqiang Lei, Youcheng Huang +5
Hybrid data combining both tabular and textual content (e.g., financial reports) are quite pervasive in the real world. However, Question Answering (QA) over such hybrid data is la…
XAI Beyond Classification: Interpretable Neural Clustering
Xi Peng, Yunnan Li, Ivor W. Tsang +3
In this paper, we study two challenging problems in explainable AI (XAI) and data clustering. The first is how to directly design a neural network with inherent interpretability, r…