36 citations · 163 across the 13 of their papers we have counts for
13 papers
How Compositional Generalization and Creativity Improve as Diffusion Models are Trained
Alessandro Favero, Antonio Sclocchi, Francesco Cagnetta +2
Natural data is often organized as a hierarchical composition of features. How many samples do generative models need in order to learn the composition rules, so as to produce a co…
Probing the Latent Hierarchical Structure of Data via Diffusion Models
Antonio Sclocchi, Alessandro Favero, Noam Itzhak Levi +1
High-dimensional data must be highly structured to be learnable. Although the compositional and hierarchical nature of data is often put forward to explain learnability, quantitati…
Could ChatGPT get an Engineering Degree? Evaluating Higher Education Vulnerability to AI Assistants
Beatriz Borges, Negar Foroutan, Deniz Bayazit +87
AI assistants are being increasingly used by students enrolled in higher education institutions. While these tools provide opportunities for improved teaching and education, they a…
A Phase Transition in Diffusion Models Reveals the Hierarchical Nature of Data
Antonio Sclocchi, Alessandro Favero, Matthieu Wyart
Understanding the structure of real data is paramount in advancing modern deep-learning methodologies. Natural data such as images are believed to be composed of features organized…
On the different regimes of Stochastic Gradient Descent
Antonio Sclocchi, Matthieu Wyart
Modern deep networks are trained with stochastic gradient descent (SGD) whose key hyperparameters are the number of data considered at each step or batch size , and the step siz…
Dissecting the Effects of SGD Noise in Distinct Regimes of Deep Learning
Antonio Sclocchi, Mario Geiger, Matthieu Wyart
Understanding when the noise in stochastic gradient descent (SGD) affects generalization of deep neural networks remains a challenge, complicated by the fact that networks can oper…