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02/03/2023
Leveraging Contaminated Datasets to Learn Clean-Data Distribution with Purified Generative Adversarial Networks
Generative adversarial networks (GANs) are known for their strong abilit...
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10/31/2022
Efficient Document Retrieval by End-to-End Refining and Quantizing BERT Embedding with Contrastive Product Quantization
Efficient document retrieval heavily relies on the technique of semantic...
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05/26/2022
Federated Non-negative Matrix Factorization for Short Texts Topic Modeling with Mutual Information
Non-negative matrix factorization (NMF) based topic modeling is widely u...
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05/13/2022
Modeling Semantic Composition with Syntactic Hypergraph for Video Question Answering
A key challenge in video question answering is how to realize the cross-...
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04/28/2022
Anomaly Detection by Leveraging Incomplete Anomalous Knowledge with Anomaly-Aware Bidirectional GANs
The goal of anomaly detection is to identify anomalous samples from norm...
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03/03/2022
Learning Set Functions Under the Optimal Subset Oracle via Equivariant Variational Inference
Learning set functions becomes increasingly more important in many appli...
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09/07/2021
Refining BERT Embeddings for Document Hashing via Mutual Information Maximization
Existing unsupervised document hashing methods are mostly established on...
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05/27/2021
Integrating Semantics and Neighborhood Information with Graph-Driven Generative Models for Document Retrieval
With the need of fast retrieval speed and small memory footprint, docume...
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05/13/2021
Unsupervised Hashing with Contrastive Information Bottleneck
Many unsupervised hashing methods are implicitly established on the idea...
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12/28/2020
Syntax-Enhanced Pre-trained Model
We study the problem of leveraging the syntactic structure of text to en...
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06/16/2020
Generative Semantic Hashing Enhanced via Boltzmann Machines
Generative semantic hashing is a promising technique for large-scale inf...
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04/22/2020
Discretized Bottleneck in VAE: Posterior-Collapse-Free Sequence-to-Sequence Learning
Variational autoencoders (VAEs) are important tools in end-to-end repres...
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08/29/2019
Document Hashing with Mixture-Prior Generative Models
Hashing is promising for large-scale information retrieval tasks thanks ...
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08/29/2019
A Deep Neural Information Fusion Architecture for Textual Network Embeddings
Textual network embeddings aim to learn a low-dimensional representation...
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05/24/2018
Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms
Many deep learning architectures have been proposed to model the composi...
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05/14/2018
NASH: Toward End-to-End Neural Architecture for Generative Semantic Hashing
Semantic hashing has become a powerful paradigm for fast similarity sear...
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09/21/2017
Deconvolutional Latent-Variable Model for Text Sequence Matching
A latent-variable model is introduced for text matching, inferring sente...
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09/18/2017
A Probabilistic Framework for Nonlinearities in Stochastic Neural Networks
We present a probabilistic framework for nonlinearities, based on doubly...
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09/06/2017
Symmetric Variational Autoencoder and Connections to Adversarial Learning
A new form of the variational autoencoder (VAE) is proposed, based on th...
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09/04/2017
A Convergence Analysis for A Class of Practical Variance-Reduction Stochastic Gradient MCMC
Stochastic gradient Markov Chain Monte Carlo (SG-MCMC) has been develope...
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11/23/2016
Scalable Bayesian Learning of Recurrent Neural Networks for Language Modeling
Recurrent neural networks (RNNs) have shown promising performance for la...
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11/15/2016
Unsupervised Learning with Truncated Gaussian Graphical Models
Gaussian graphical models (GGMs) are widely used for statistical modelin...
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06/02/2016