I will discuss a new paradigm for cosmological likelihood-based inference, leveraging recent developments in machine learning and its underlying technology, to accelerate Bayesian inference in high-dimensional settings. This paradigm combines (i) emulation, where a machine learning model is trained to mimic cosmological observables, e.g. CosmoPower-JAX; (ii) differentiable and probabilistic...
Latent Diffusion Models (LDMs) have achieved remarkable success in various domains of generative modeling, particularly due to their ability to efficiently learn and represent complex data distributions in an abstract latent space. This success is exemplified by their use in Stable Diffusion or FLUX.1, state-of-the-art text-to-image synthesis techniques that leverage the strengths of LDMs to...
The SMART Pulsar survey produced Petabytes of data, only a small fraction of which has been processed for Pulsar search. Processing was done with a coarse dedispersion plan and using a simple periodicity search. An improvement in the software is required to search a larger fraction of the dataset and to conduct fine searches. We share our contributions to the GPU based implementations of these...
