33 citations · 126 across the 24 of their papers we have counts for
24 papers
Learning Summary-Worthy Visual Representation for Abstractive Summarization in Video
Zenan Xu, Xiaojun Meng, Yasheng Wang +4
Multimodal abstractive summarization for videos (MAS) requires generating a concise textual summary to describe the highlights of a video according to multimodal resources, in our…
Leveraging Contaminated Datasets to Learn Clean-Data Distribution with Purified Generative Adversarial Networks
Bowen Tian, Qinliang Su, Jianxing Yu
Generative adversarial networks (GANs) are known for their strong abilities on capturing the underlying distribution of training instances. Since the seminal work of GAN, many vari…
Efficient Document Retrieval by End-to-End Refining and Quantizing BERT Embedding with Contrastive Product Quantization
Zexuan Qiu, Qinliang Su, Jianxing Yu +1
Efficient document retrieval heavily relies on the technique of semantic hashing, which learns a binary code for every document and employs Hamming distance to evaluate document di…
Federated Non-negative Matrix Factorization for Short Texts Topic Modeling with Mutual Information
Shijing Si, Jianzong Wang, Ruiyi Zhang +2
Non-negative matrix factorization (NMF) based topic modeling is widely used in natural language processing (NLP) to uncover hidden topics of short text documents. Usually, training…
Modeling Semantic Composition with Syntactic Hypergraph for Video Question Answering
Zenan Xu, Wanjun Zhong, Qinliang Su +2
A key challenge in video question answering is how to realize the cross-modal semantic alignment between textual concepts and corresponding visual objects. Existing methods mostly…
Anomaly Detection by Leveraging Incomplete Anomalous Knowledge with Anomaly-Aware Bidirectional GANs
Bowen Tian, Qinliang Su, Jian Yin
The goal of anomaly detection is to identify anomalous samples from normal ones. In this paper, a small number of anomalies are assumed to be available at the training stage, but t…
Learning Neural Set Functions Under the Optimal Subset Oracle
Zijing Ou, Tingyang Xu, Qinliang Su +3
Learning neural set functions becomes increasingly more important in many applications like product recommendation and compound selection in AI-aided drug discovery. The majority o…
Refining BERT Embeddings for Document Hashing via Mutual Information Maximization
Zijing Ou, Qinliang Su, Jianxing Yu +3
Existing unsupervised document hashing methods are mostly established on generative models. Due to the difficulties of capturing long dependency structures, these methods rarely mo…
Integrating Semantics and Neighborhood Information with Graph-Driven Generative Models for Document Retrieval
Zijing Ou, Qinliang Su, Jianxing Yu +5
With the need of fast retrieval speed and small memory footprint, document hashing has been playing a crucial role in large-scale information retrieval. To generate high-quality ha…
Unsupervised Hashing with Contrastive Information Bottleneck
Zexuan Qiu, Qinliang Su, Zijing Ou +2
Many unsupervised hashing methods are implicitly established on the idea of reconstructing the input data, which basically encourages the hashing codes to retain as much informatio…
Syntax-Enhanced Pre-trained Model
Zenan Xu, Daya Guo, Duyu Tang +7
We study the problem of leveraging the syntactic structure of text to enhance pre-trained models such as BERT and RoBERTa. Existing methods utilize syntax of text either in the pre…
Embedding Dynamic Attributed Networks by Modeling the Evolution Processes
Zenan Xu, Zijing Ou, Qinliang Su +3
Network embedding has recently emerged as a promising technique to embed nodes of a network into low-dimensional vectors. While fairly successful, most existing works focus on the…
Generative Semantic Hashing Enhanced via Boltzmann Machines
Lin Zheng, Qinliang Su, Dinghan Shen +1
Generative semantic hashing is a promising technique for large-scale information retrieval thanks to its fast retrieval speed and small memory footprint. For the tractability of tr…
Improve Variational Autoencoder for Text Generationwith Discrete Latent Bottleneck
Yang Zhao, Ping Yu, Suchismit Mahapatra +2
Variational autoencoders (VAEs) are essential tools in end-to-end representation learning. However, the sequential text generation common pitfall with VAEs is that the model tends…
Document Hashing with Mixture-Prior Generative Models
Wei Dong, Qinliang Su, Dinghan Shen +1
Hashing is promising for large-scale information retrieval tasks thanks to the efficiency of distance evaluation between binary codes. Generative hashing is often used to generate…
A Deep Neural Information Fusion Architecture for Textual Network Embeddings
Zenan Xu, Qinliang Su, Xiaojun Quan +1
Textual network embeddings aim to learn a low-dimensional representation for every node in the network so that both the structural and textual information from the networks can be…
Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms
Dinghan Shen, Guoyin Wang, Wenlin Wang +6
Many deep learning architectures have been proposed to model the compositionality in text sequences, requiring a substantial number of parameters and expensive computations. Howeve…
NASH: Toward End-to-End Neural Architecture for Generative Semantic Hashing
Dinghan Shen, Qinliang Su, Paidamoyo Chapfuwa +4
Semantic hashing has become a powerful paradigm for fast similarity search in many information retrieval systems. While fairly successful, previous techniques generally require two…
A Probabilistic Framework for Nonlinearities in Stochastic Neural Networks
Qinliang Su, Xuejun Liao, Lawrence Carin
We present a probabilistic framework for nonlinearities, based on doubly truncated Gaussian distributions. By setting the truncation points appropriately, we are able to generate v…
A Convergence Analysis for A Class of Practical Variance-Reduction Stochastic Gradient MCMC
Changyou Chen, Wenlin Wang, Yizhe Zhang +2
Stochastic gradient Markov Chain Monte Carlo (SG-MCMC) has been developed as a flexible family of scalable Bayesian sampling algorithms. However, there has been little theoretical…
Deconvolutional Latent-Variable Model for Text Sequence Matching
Dinghan Shen, Yizhe Zhang, Ricardo Henao +2
A latent-variable model is introduced for text matching, inferring sentence representations by jointly optimizing generative and discriminative objectives. To alleviate typical opt…
Unsupervised Learning with Truncated Gaussian Graphical Models
Qinliang Su, Xuejun Liao, Chunyuan Li +2
Gaussian graphical models (GGMs) are widely used for statistical modeling, because of ease of inference and the ubiquitous use of the normal distribution in practical approximation…
Scalable Bayesian Learning of Recurrent Neural Networks for Language Modeling
Zhe Gan, Chunyuan Li, Changyou Chen +3
Recurrent neural networks (RNNs) have shown promising performance for language modeling. However, traditional training of RNNs using back-propagation through time often suffers fro…
Nonlinear Statistical Learning with Truncated Gaussian Graphical Models
Qinliang Su, Xuejun Liao, Changyou Chen +1
We introduce the truncated Gaussian graphical model (TGGM) as a novel framework for designing statistical models for nonlinear learning. A TGGM is a Gaussian graphical model (GGM)…