55 citations · 103 across the 11 of their papers we have counts for
11 papers
SOM-VQ: Topology-Aware Tokenization for Interactive Generative Models
Alessandro Londei, Denise Lanzieri, Matteo Benati
Vector-quantized representations enable powerful discrete generative models but lack semantic structure in token space, limiting interpretable human control. We introduce SOM-VQ, a…
Inverting Self-Organizing Maps: A Unified Activation-Based Framework
Alessandro Londei, Matteo Benati, Denise Lanzieri +1
Self-Organizing Maps (SOMs) provide topology-preserving projections of high-dimensional data, yet their use as generative models remains largely unexplored. We show that the activa…
First-Extinction Law for Resampling Processes
Matteo Benati, Alessandro Londei, Denise Lanzieri +1
Extinction times in resampling processes are fundamental yet often intractable, as previous formulas scale as with the number of states present in the initial probability…
Lyapunov Learning at the Onset of Chaos
Matteo Benati, Alessandro Londei, Denise Lanzieri +1
Handling regime shifts and non-stationary time series in deep learning systems presents a significant challenge. In the case of online learning, when new information is introduced,…
Dreaming Learning
Alessandro Londei, Matteo Benati, Denise Lanzieri +1
Incorporating novelties into deep learning systems remains a challenging problem. Introducing new information to a machine learning system can interfere with previously stored data…
Simulation-Based Inference Benchmark for Weak Lensing Cosmology
Justine Zeghal, Denise Lanzieri, François Lanusse +5
Standard cosmological analysis, which relies on two-point statistics, fails to extract the full information of the data. This limits our ability to constrain with precision cosmolo…
Optimal Neural Summarisation for Full-Field Weak Lensing Cosmological Implicit Inference
Denise Lanzieri, Justine Zeghal, T. Lucas Makinen +3
Traditionally, weak lensing cosmological surveys have been analyzed using summary statistics motivated by their analytically tractable likelihoods, or by their ability to access hi…
Forecasting the power of Higher Order Weak Lensing Statistics with automatically differentiable simulations
Denise Lanzieri, François Lanusse, Chirag Modi +4
We present the Differentiable Lensing Lightcone (DLL), a fully differentiable physical model designed for being used as a forward model in Bayesian inference algorithms requiring a…
JAX-COSMO: An End-to-End Differentiable and GPU Accelerated Cosmology Library
Jean-Eric Campagne, François Lanusse, Joe Zuntz +7
We present jax-cosmo, a library for automatically differentiable cosmological theory calculations. It uses the JAX library, which has created a new coding ecosystem, especially in…
Hybrid Physical-Neural ODEs for Fast N-body Simulations
Denise Lanzieri, François Lanusse, Jean-Luc Starck
We present a new scheme to compensate for the small-scales approximations resulting from Particle-Mesh (PM) schemes for cosmological N-body simulations. This kind of simulations ar…
AMICO galaxy clusters in KiDS-DR3: Measurement of the halo bias and power spectrum normalization from a stacked weak lensing analysis
Lorenzo Ingoglia, Giovanni Covone, Mauro Sereno +19
Galaxy clusters are biased tracers of the underlying matter density field. At very large radii beyond about 10 Mpc/\textit{h}, the shear profile shows evidence of a second-halo ter…