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Riccardo Valperga

4 accepted papers

2025

Grounding Continuous Representations in Geometry: Equivariant Neural Fields

ICLR 2025poster

Conditional Neural Fields (CNFs) are increasingly being leveraged as continuous signal representations, by associating each data-sample with a latent variable that conditions a shared backbone Neural Field (NeF) to reconstruct the sample. However, existing CNF architectures face limitations when usi…

2024

Ai-sampler: Adversarial Learning of Markov kernels with involutive maps

ICML 2024poster

Markov chain Monte Carlo methods have become popular in statistics as versatile techniques to sample from complicated probability distributions. In this work, we propose a method to parameterize and train transition kernels of Markov chains to achieve efficient sampling and good mixing. This trainin…

2024

How to Train Neural Field Representations: A Comprehensive Study and Benchmark

CVPR 2024poster

Neural fields (NeFs) have recently emerged as a versatile method for modeling signals of various modalities including images shapes and scenes. Subsequently a number of works have explored the use of NeFs as representations for downstream tasks e.g. classifying an image based on the parameters of a…

2024

Space-Time Continuous PDE Forecasting using Equivariant Neural Fields

NeurIPS 2024poster

Recently, Conditional Neural Fields (NeFs) have emerged as a powerful modelling paradigm for PDEs, by learning solutions as flows in the latent space of the Conditional NeF. Although benefiting from favourable properties of NeFs such as grid-agnosticity and space-time-continuous dynamics modelling,…

Cited by 7SourcePDFScholar