
Refining BERT Embeddings for Document Hashing via Mutual Information Maximization
Existing unsupervised document hashing methods are mostly established on...
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Integrating Semantics and Neighborhood Information with GraphDriven Generative Models for Document Retrieval
With the need of fast retrieval speed and small memory footprint, docume...
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Unsupervised Hashing with Contrastive Information Bottleneck
Many unsupervised hashing methods are implicitly established on the idea...
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SyntaxEnhanced Pretrained Model
We study the problem of leveraging the syntactic structure of text to en...
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Generative Semantic Hashing Enhanced via Boltzmann Machines
Generative semantic hashing is a promising technique for largescale inf...
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Discretized Bottleneck in VAE: PosteriorCollapseFree SequencetoSequence Learning
Variational autoencoders (VAEs) are important tools in endtoend repres...
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Document Hashing with MixturePrior Generative Models
Hashing is promising for largescale information retrieval tasks thanks ...
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A Deep Neural Information Fusion Architecture for Textual Network Embeddings
Textual network embeddings aim to learn a lowdimensional representation...
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Baseline Needs More Love: On Simple WordEmbeddingBased Models and Associated Pooling Mechanisms
Many deep learning architectures have been proposed to model the composi...
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NASH: Toward EndtoEnd Neural Architecture for Generative Semantic Hashing
Semantic hashing has become a powerful paradigm for fast similarity sear...
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Deconvolutional LatentVariable Model for Text Sequence Matching
A latentvariable model is introduced for text matching, inferring sente...
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A Probabilistic Framework for Nonlinearities in Stochastic Neural Networks
We present a probabilistic framework for nonlinearities, based on doubly...
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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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A Convergence Analysis for A Class of Practical VarianceReduction Stochastic Gradient MCMC
Stochastic gradient Markov Chain Monte Carlo (SGMCMC) has been develope...
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Scalable Bayesian Learning of Recurrent Neural Networks for Language Modeling
Recurrent neural networks (RNNs) have shown promising performance for la...
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Unsupervised Learning with Truncated Gaussian Graphical Models
Gaussian graphical models (GGMs) are widely used for statistical modelin...
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Nonlinear Statistical Learning with Truncated Gaussian Graphical Models
We introduce the truncated Gaussian graphical model (TGGM) as a novel fr...
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Qinliang Su
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