12 citations · 14 across the 5 of their papers we have counts for
3 papers · 1 filter
Last layer state space model for representation learning and uncertainty quantification
Max Cohen, Maurice Charbit, Sylvain Le Corff
As sequential neural architectures become deeper and more complex, uncertainty estimation is more and more challenging. Efforts in quantifying uncertainty often rely on specific tr…
Variational latent discrete representation for time series modelling
Max Cohen, Maurice Charbit, Sylvain Le Corff
Discrete latent space models have recently achieved performance on par with their continuous counterparts in deep variational inference. While they still face various implementatio…
Diffusion bridges vector quantized Variational AutoEncoders
Max Cohen, Guillaume Quispe, Sylvain Le Corff +2
Vector Quantized-Variational AutoEncoders (VQ-VAE) are generative models based on discrete latent representations of the data, where inputs are mapped to a finite set of learned em…