Weight space learning on Implicit Neural Representations

Study of the state of the art on INRs (Implicit Neural Representations), metanetworks for INR processing, and permutation symmetries of neural networks. Definition of specifications for building datasets of INRs representing images and 3D data, with shared and arbitrary initialization. Development of INR alignment algorithms and of interpolability optimization procedures. Training and validation of metanetworks for INR processing.