1 citations · 1 across the 6 of their papers we have counts for
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Scalable Random Feature Latent Variable Models
Ying Li, Zhidi Lin, Yuhao Liu +3
Random feature latent variable models (RFLVMs) represent the state-of-the-art in latent variable models, capable of handling non-Gaussian likelihoods and effectively uncovering pat…
Improved Variational Inference in Discrete VAEs using Error Correcting Codes
María Martínez-García, Grace Villacrés, David Mitchell +1
Despite advances in deep probabilistic models, learning discrete latent representations remains challenging. This work introduces a novel method to improve inference in discrete Va…
Alzheimer's disease detection in PSG signals
Lorena Gallego-Viñarás, Juan Miguel Mira-Tomás, Anna Michela-Gaeta +4
Alzheimer's disease (AD) and sleep disorders exhibit a close association, where disruptions in sleep patterns often precede the onset of Mild Cognitive Impairment (MCI) and early-s…
Training Implicit Generative Models via an Invariant Statistical Loss
José Manuel de Frutos, Pablo M. Olmos, Manuel A. Vázquez +1
Implicit generative models have the capability to learn arbitrary complex data distributions. On the downside, training requires telling apart real data from artificially-generated…