233 citations · 328 across the 16 of their papers we have counts for
7 papers · 1 filter
Harnessing Synthetic Datasets: The Role of Shape Bias in Deep Neural Network Generalization
Elior Benarous, Sotiris Anagnostidis, Luca Biggio +1
Recent advancements in deep learning have been primarily driven by the use of large models trained on increasingly vast datasets. While neural scaling laws have emerged to predict…
Accelerating galaxy dynamical modeling using a neural network for joint lensing and kinematics analyses
Matthew R. Gomer, Sebastian Ertl, Luca Biggio +6
Strong gravitational lensing is a powerful tool to provide constraints on galaxy mass distributions and cosmological parameters, such as the Hubble constant, . Nevertheless, i…
Gemtelligence: Accelerating Gemstone classification with Deep Learning
Tommaso Bendinelli, Luca Biggio, Daniel Nyfeler +4
The value of luxury goods, particularly investment-grade gemstones, is greatly influenced by their origin and authenticity, sometimes resulting in differences worth millions of dol…
Dynamic Context Pruning for Efficient and Interpretable Autoregressive Transformers
Sotiris Anagnostidis, Dario Pavllo, Luca Biggio +3
Autoregressive Transformers adopted in Large Language Models (LLMs) are hard to scale to long sequences. Despite several works trying to reduce their computational cost, most of LL…
Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A Tutorial
Venkat Nemani, Luca Biggio, Xun Huan +6
On top of machine learning models, uncertainty quantification (UQ) functions as an essential layer of safety assurance that could lead to more principled decision making by enablin…
Controllable Neural Symbolic Regression
Tommaso Bendinelli, Luca Biggio, Pierre-Alexandre Kamienny
In symbolic regression, the goal is to find an analytical expression that accurately fits experimental data with the minimal use of mathematical symbols such as operators, variable…
An SDE for Modeling SAM: Theory and Insights
Enea Monzio Compagnoni, Luca Biggio, Antonio Orvieto +3
We study the SAM (Sharpness-Aware Minimization) optimizer which has recently attracted a lot of interest due to its increased performance over more classical variants of stochastic…