21 citations · 28 across the 8 of their papers we have counts for
8 papers
Shieldstral
Antonia Calvi, Avinash Sooriyarachchi, Giada Pistilli +273
We introduce Shieldstral, a 3B-parameter policy-adaptive multimodal safety classifier that matches or outperforms models nearly 7 its size on text safety benchmarks and set…
Voxtral TTS
Mistral-AI, :, Alexander H. Liu +186
We introduce Voxtral TTS, an expressive multilingual text-to-speech model that generates natural speech from as little as 3 seconds of reference audio. Voxtral TTS adopts a hybrid…
Ministral 3
Alexander H. Liu, Kartik Khandelwal, Sandeep Subramanian +116
We introduce the Ministral 3 series, a family of parameter-efficient dense language models designed for compute and memory constrained applications, available in three model sizes:…
How Deep Networks Learn Sparse and Hierarchical Data: the Sparse Random Hierarchy Model
Umberto Tomasini, Matthieu Wyart
Understanding what makes high-dimensional data learnable is a fundamental question in machine learning. On the one hand, it is believed that the success of deep learning lies in it…
How Deep Neural Networks Learn Compositional Data: The Random Hierarchy Model
Francesco Cagnetta, Leonardo Petrini, Umberto M. Tomasini +2
Deep learning algorithms demonstrate a surprising ability to learn high-dimensional tasks from limited examples. This is commonly attributed to the depth of neural networks, enabli…
How deep convolutional neural networks lose spatial information with training
Umberto M. Tomasini, Leonardo Petrini, Francesco Cagnetta +1
A central question of machine learning is how deep nets manage to learn tasks in high dimensions. An appealing hypothesis is that they achieve this feat by building a representatio…
Failure and success of the spectral bias prediction for Kernel Ridge Regression: the case of low-dimensional data
Umberto M. Tomasini, Antonio Sclocchi, Matthieu Wyart
Recently, several theories including the replica method made predictions for the generalization error of Kernel Ridge Regression. In some regimes, they predict that the method has…
Predictors and Predictands of Linear Response in Spatially Extended Systems
Umberto Maria Tomasini, Valerio Lucarini
The goal of response theory, in each of its many statistical mechanical formulations, is to predict the perturbed response of a system from the knowledge of the unperturbed state a…