23 citations · 157 across the 46 of their papers we have counts for
4 papers · 1 filter
Improving Variational Autoencoders with Density Gap-based Regularization
Jianfei Zhang, Jun Bai, Chenghua Lin +2
Variational autoencoders (VAEs) are one of the powerful unsupervised learning frameworks in NLP for latent representation learning and latent-directed generation. The classic optim…
On the Latent Holes of VAEs for Text Generation
Ruizhe Li, Xutan Peng, Chenghua Lin
In this paper, we provide the first focused study on the discontinuities (aka. holes) in the latent space of Variational Auto-Encoders (VAEs), a phenomenon which has been shown to…
Highly Efficient Knowledge Graph Embedding Learning with Orthogonal Procrustes Analysis
Xutan Peng, Guanyi Chen, Chenghua Lin +1
Knowledge Graph Embeddings (KGEs) have been intensively explored in recent years due to their promise for a wide range of applications. However, existing studies focus on improving…
Latent Space Factorisation and Manipulation via Matrix Subspace Projection
Xiao Li, Chenghua Lin, Ruizhe Li +2
We tackle the problem disentangling the latent space of an autoencoder in order to separate labelled attribute information from other characteristic information. This then allows u…