2 citations · 3 across the 8 of their papers we have counts for
3 papers · 1 filter
Learning Functional Subspaces for Neural Network Compression
Massimo Bini, Anders Christensen, Stephan Alaniz +5
Modern transformers pair impressive capabilities with substantial memory and compute demands. Low-rank weight factorization reduces both while keeping the matrices dense, and thus…
DiffEnc: Variational Diffusion with a Learned Encoder
Beatrix M. G. Nielsen, Anders Christensen, Andrea Dittadi +1
Diffusion models may be viewed as hierarchical variational autoencoders (VAEs) with two improvements: parameter sharing for the conditional distributions in the generative process…
Addressing caveats of neural persistence with deep graph persistence
Leander Girrbach, Anders Christensen, Ole Winther +2
Neural Persistence is a prominent measure for quantifying neural network complexity, proposed in the emerging field of topological data analysis in deep learning. In this work, how…