Author(s): Guo D; Luo Z; Bouguila N; Fan W;

Neural topic models (NTMs) based on variational autoencoders (VAEs) have emerged as a scalable and flexible alternative to classical probabilistic models for uncovering latent thematic structures in text corpora. However, most existing NTMs either overlook the geometric structure of word embeddings or rely on Euclidean priors that are poorly aligned with ...

Article GUID: 41791177

Author(s): Stefanuk B; Skonieczny K;

In the domain of planetary science, novelty detection is gaining attention because of the operational opportunities it offers, including annotated data products and downlink prioritization. Using a variational autoencoder (VAE), this work improves upon state-of-the-art novelty detection performance in the context of Martian exploration by > 7 % (measured ...

Article GUID: 36313243

Author(s): Yang J, Xie G, Yang Y, Zhang Y, Liu W

ISA Trans. 2019 May 30;: Authors: Yang J, Xie G, Yang Y, Zhang Y, Liu W

Article GUID: 31174854