12 citations · 14 across the 5 of their papers we have counts for
5 papers
Chopping and distilling variational autoencoders for real-time anomaly detection in high energy physics
Max Cohen, Rajat Gupta, Sterre Hoogendoorn +3
Anomaly detection (AD) has recently emerged as an exciting alternative to conventional search strategies in high energy physics using artificial intelligence (AI) and machine learn…
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…
End-to-end deep meta modelling to calibrate and optimize energy consumption and comfort
Max Cohen, Sylvain Le Corff, Maurice Charbit +2
In this paper, we propose a new end-to-end methodology to optimize the energy performance as well as comfort and air quality in large buildings without any renovation work. We intr…