6 papers
Ambient Physics: Training Neural PDE Solvers with Partial Observations
Harris Abdul Majid, Giannis Daras, Francesco Tudisco +1
In many scientific settings, acquiring complete observations of PDE coefficients and solutions can be expensive, hazardous, or impossible. Recent diffusion-based methods can recons…
Low-Rank Compression of Language Models via Differentiable Rank Selection
Sidhant Sundrani, Francesco Tudisco, Pasquale Minervini
Approaches for compressing large-language models using low-rank decomposition have made strides, particularly with the introduction of activation and loss-aware SVD, which improves…
Algebraformer: A Neural Approach to Linear Systems
Pietro Sittoni, Francesco Tudisco
Recent work in deep learning has opened new possibilities for solving classical algorithmic tasks using end-to-end learned models. In this work, we investigate the fundamental task…
Solaris: A Foundation Model of the Sun
Harris Abdul Majid, Pietro Sittoni, Francesco Tudisco
Foundation models have demonstrated remarkable success across various scientific domains, motivating our exploration of their potential in solar physics. In this paper, we present…
Subhomogeneous Deep Equilibrium Models
Pietro Sittoni, Francesco Tudisco
Implicit-depth neural networks have grown as powerful alternatives to traditional networks in various applications in recent years. However, these models often lack guarantees of e…
The self-consistent field iteration for p-spectral clustering
Parikshit Upadhyaya, Elias Jarlebring, Francesco Tudisco
The self-consistent field (SCF) iteration, combined with its variants, is one of the most widely used algorithms in quantum chemistry. We propose a procedure to adapt the SCF itera…