1 citations · 1 across the 6 of their papers we have counts for
8 papers
Flow-based Spectral Kernel Learning for Nonstationary Attention
Zi Yang, Ying Li, Alejandro Lancho +3
Kernelized attention provides a useful perspective for understanding self-attention as a similarity-based aggregation mechanism, but existing approaches often rely on fixed or stat…
A Vector-Quantized Foundation Model for Patient Behavior Monitoring
Rodrigo Oliver, Josué Pérez-Sabater, Leire Paz-Arbaizar +4
Foundation models have achieved remarkable success across various domains, yet their adoption in healthcare remains limited. While significant advances have been made in medical im…
Multi-View Oriented GPLVM: Expressiveness and Efficiency
Zi Yang, Ying Li, Zhidi Lin +2
The multi-view Gaussian process latent variable model (MV-GPLVM) aims to learn a unified representation from multi-view data but is hindered by challenges such as limited kernel ex…
Transformer Vibration Forecasting for Advancing Rail Safety and Maintenance 4.0
Darío C. Larese, Almudena Bravo Cerrada, Gabriel Dambrosio Tomei +3
Maintaining railway axles is critical to preventing severe accidents and financial losses. The railway industry is increasingly interested in advanced condition monitoring techniqu…
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…