Jorge Fernández-de-Cossio-Díaz

Jorge Fernandez-de-Cossio-Diaz, Simona Cocco, Remi Monasson

A goal of unsupervised machine learning is to build representations of complex high-dimensional data, with simple relations to their properties. Such disentangled representations make easier to interpret the significant latent factors of variation in the data, as well as to generate new data with desirable features. Methods for disentangling representations often rely on an adversarial scheme, in which representations are tuned to avoid discriminators from being able to reconstruct information about the data properties (labels). Unfortunately adversarial training is generally difficult to implement in practice. Here we propose a simple, effective way of disentangling representations without any need to train adversarial discriminators, and apply our approach to Restricted Boltzmann Machines (RBM), one of the simplest representation-based generative models. Our approach relies on the introduction of adequate constraints on the weights during training, which allows us to concentrate information about labels on a small subset of latent variables. The effectiveness of the approach is illustrated with four examples: the CelebA dataset of facial images, the two-dimensional Ising model, the MNIST dataset of handwritten digits, and the taxonomy of protein families. In addition, we show how our framework allows for analytically computing the cost, in terms of log-likelihood of the data, associated to the disentanglement of their representations.

Thomas Tulinski, Simona Cocco, Rémi Monasson et al.

Energy-based models (EBMs) are flexible generative architectures inspired by statistical physics, but their learning and generative properties remain poorly understood. Here, we analyze a solvable EBM in the high-dimensional limit: the spherical Boltzmann machine (SBM). Combining tools from random matrix theory and dynamical mean-field theory, we: solve exact equations describing the training dynamics of the SBM; compute the Bayesian evidence, which acts as a partition function in parameter space and encodes global properties of the trained model; and uncover cascades of phase transitions that occur both during training and as a function of hyperparameters, related to successive alignment and condensation of the top modes of the coupling matrix to the data. We connect these transitions to sampling-time generative phenomena in a teacher-student scenario, including: sampling temperature tuning, double descent as a function of regularization strength, tempered posterior effects, and out-of-equilibrium effects during training that induce biases in the trained model. We provide numerical evidence demonstrating that all these phenomena appear in standard generative architectures, beyond the SBM.

J. Garrido, S. Sánchez-Expósito, A. Ruiz-Falcó et al.

The SKA Observatory (SKAO), a landmark project in radio astronomy, seeks to address fundamental questions in astronomy. To process its immense data output, approximately 700 PB/year, a global network of SKA Regional Centres (SR-CNet) will provide the infrastructure, tools, computational power needed for scientific analysis and scientific support. The Spanish SRC (espSRC) focuses on ensuring the sustainability of this network by reducing its environmental impact, integrating green practices into data platforms, and developing Open Science technologies to enable reproducible research. This paper discusses and summarizes part of the research and development activities that the team is conducting to reduce the SRC energy consumption at the espSRC and SRCNet. The paper also discusses fundamental research on trusted repositories to support Open Science practices.