Matrix denoising: Bayes-optimal estimators via low-degree polynomials
G Semerjian - Journal of Statistical Physics, 2024 - Springer
We consider the additive version of the matrix denoising problem, where a random
symmetric matrix S of size n has to be inferred from the observation of Y= S+ Z, with Z an …
symmetric matrix S of size n has to be inferred from the observation of Y= S+ Z, with Z an …
Matrix inference in growing rank regimes
The inference of a large symmetric signal-matrix corrupted by additive Gaussian noise, is
considered for two regimes of growth of the rank M as a function of N. For sub-linear ranks …
considered for two regimes of growth of the rank M as a function of N. For sub-linear ranks …
Bilinear Sequence Regression: A Model for Learning from Long Sequences of High-dimensional Tokens
Current progress in artificial intelligence is centered around so-called large language
models that consist of neural networks processing long sequences of high-dimensional …
models that consist of neural networks processing long sequences of high-dimensional …