3 papers
cs.CL2026
LRCC: Generalizing Low-Rank Compression with Conditional Computation
Thomas Vaitses Fontanari, Maximo Eduardo Rulli, Federico Alvetreti +2
Low-rank compression reduces the cost of pretrained language models by replacing linear transformations with low-rank factorizations. However, conventional methods use a fixed rank…
cs.AI2026
Subliminal Clocks: Latent Time Modelling in Diffusion Language Models
Maximo Eduardo Rulli, Thomas Vaitses Fontanari, Simone Petruzzi +9
Diffusion Language Models (DLMs) have recently emerged as a promising alternative to autoregressive models. Unlike standard diffusion-based approaches, DLMs are not explicitly cond…
cs.LG2026
Hierarchical Pooling and Explainability in Graph Neural Networks for Tumor and Tissue-of-Origin Classification Using RNA-seq Data
Thomas Vaitses Fontanari, Mariana Recamonde-Mendoza
This study explores the use of graph neural networks (GNNs) with hierarchical pooling and multiple convolution layers for cancer classification based on RNA-seq data. We combine ge…