200 citations · 1.6k across the 79 of their papers we have counts for
17 papers · 1 filter
VIRT: Improving Representation-based Models for Text Matching through Virtual Interaction
Dan Li, Yang Yang, Hongyin Tang +4
With the booming of pre-trained transformers, representation-based models based on Siamese transformer encoders have become mainstream techniques for efficient text matching. Howev…
Regularizing Variational Autoencoder with Diversity and Uncertainty Awareness
Dazhong Shen, Chuan Qin, Chao Wang +3
As one of the most popular generative models, Variational Autoencoder (VAE) approximates the posterior of latent variables based on amortized variational inference. However, when t…
Fast Variational AutoEncoder with Inverted Multi-Index for Collaborative Filtering
Jin Chen, Defu Lian, Binbin Jin +3
Variational AutoEncoder (VAE) has been extended as a representative nonlinear method for collaborative filtering. However, the bottleneck of VAE lies in the softmax computation ove…
DAE-GAN: Dynamic Aspect-aware GAN for Text-to-Image Synthesis
Shulan Ruan, Yong Zhang, Kun Zhang +4
Text-to-image synthesis refers to generating an image from a given text description, the key goal of which lies in photo realism and semantic consistency. Previous methods usually…
SIFN: A Sentiment-aware Interactive Fusion Network for Review-based Item Recommendation
Kai Zhang, Hao Qian, Qi Liu +4
Recent studies in recommender systems have managed to achieve significantly improved performance by leveraging reviews for rating prediction. However, despite being extensively stu…
LadRa-Net: Locally-Aware Dynamic Re-read Attention Net for Sentence Semantic Matching
Kun Zhang, Guangyi Lv, Le Wu +3
Sentence semantic matching requires an agent to determine the semantic relation between two sentences, which is widely used in various natural language tasks, such as Natural Langu…
DGA-Net Dynamic Gaussian Attention Network for Sentence Semantic Matching
Kun Zhang, Guangyi Lv, Meng Wang +1
Sentence semantic matching requires an agent to determine the semantic relation between two sentences, where much recent progress has been made by the advancement of representation…
GraphMI: Extracting Private Graph Data from Graph Neural Networks
Zaixi Zhang, Qi Liu, Zhenya Huang +4
As machine learning becomes more widely used for critical applications, the need to study its implications in privacy turns to be urgent. Given access to the target model and auxil…
Estimating Fund-Raising Performance for Start-up Projects from a Market Graph Perspective
Likang Wu, Zhi Li, Hongke Zhao +2
In the online innovation market, the fund-raising performance of the start-up project is a concerning issue for creators, investors and platforms. Unfortunately, existing studies a…
XCrossNet: Feature Structure-Oriented Learning for Click-Through Rate Prediction
Runlong Yu, Yuyang Ye, Qi Liu +4
Click-Through Rate (CTR) prediction is a core task in nowadays commercial recommender systems. Feature crossing, as the mainline of research on CTR prediction, has shown a promisin…
Towards Variable-Length Textual Adversarial Attacks
Junliang Guo, Zhirui Zhang, Linlin Zhang +4
Adversarial attacks have shown the vulnerability of machine learning models, however, it is non-trivial to conduct textual adversarial attacks on natural language processing tasks…
LightSpeech: Lightweight and Fast Text to Speech with Neural Architecture Search
Renqian Luo, Xu Tan, Rui Wang +5
Text to speech (TTS) has been broadly used to synthesize natural and intelligible speech in different scenarios. Deploying TTS in various end devices such as mobile phones or embed…
Drug Package Recommendation via Interaction-aware Graph Induction
Zhi Zheng, Chao Wang, Tong Xu +5
Recent years have witnessed the rapid accumulation of massive electronic medical records (EMRs), which highly support the intelligent medical services such as drug recommendation.…
Inheritance-guided Hierarchical Assignment for Clinical Automatic Diagnosis
Yichao Du, Pengfei Luo, Xudong Hong +5
Clinical diagnosis, which aims to assign diagnosis codes for a patient based on the clinical note, plays an essential role in clinical decision-making. Considering that manual diag…
Learning the Implicit Semantic Representation on Graph-Structured Data
Likang Wu, Zhi Li, Hongke Zhao +4
Existing representation learning methods in graph convolutional networks are mainly designed by describing the neighborhood of each node as a perceptual whole, while the implicit s…
Quality meets Diversity: A Model-Agnostic Framework for Computerized Adaptive Testing
Haoyang Bi, Haiping Ma, Zhenya Huang +5
Computerized Adaptive Testing (CAT) is emerging as a promising testing application in many scenarios, such as education, game and recruitment, which targets at diagnosing the knowl…
Adam revisited: a weighted past gradients perspective
Hui Zhong, Zaiyi Chen, Chuan Qin +4
Adaptive learning rate methods have been successfully applied in many fields, especially in training deep neural networks. Recent results have shown that adaptive methods with expo…