1 citations · 1 across the 7 of their papers we have counts for
7 papers
Trust And Balance: Few Trusted Samples Pseudo-Labeling and Temperature Scaled Loss for Effective Source-Free Unsupervised Domain Adaptation
Andrea Maracani, Lorenzo Rosasco, Lorenzo Natale
Deep Neural Networks have significantly impacted many computer vision tasks. However, their effectiveness diminishes when test data distribution (target domain) deviates from the o…
Key Design Choices in Source-Free Unsupervised Domain Adaptation: An In-depth Empirical Analysis
Andrea Maracani, Raffaello Camoriano, Elisa Maiettini +3
This study provides a comprehensive benchmark framework for Source-Free Unsupervised Domain Adaptation (SF-UDA) in image classification, aiming to achieve a rigorous empirical unde…
Learning Rhythmic Trajectories with Geometric Constraints for Laser-Based Skincare Procedures
Anqing Duan, Wanli Liuchen, Jinsong Wu +3
The increasing deployment of robots has significantly enhanced the automation levels across a wide and diverse range of industries. This paper investigates the automation challenge…
Sim2Real Bilevel Adaptation for Object Surface Classification using Vision-Based Tactile Sensors
Gabriele M. Caddeo, Andrea Maracani, Paolo D. Alfano +3
In this paper, we address the Sim2Real gap in the field of vision-based tactile sensors for classifying object surfaces. We train a Diffusion Model to bridge this gap using a relat…
Shortcuts for causal discovery of nonlinear models by score matching
Francesco Montagna, Nicoletta Noceti, Lorenzo Rosasco +1
The use of simulated data in the field of causal discovery is ubiquitous due to the scarcity of annotated real data. Recently, Reisach et al., 2021 highlighted the emergence of pat…
Assumption violations in causal discovery and the robustness of score matching
Francesco Montagna, Atalanti A. Mastakouri, Elias Eulig +5
When domain knowledge is limited and experimentation is restricted by ethical, financial, or time constraints, practitioners turn to observational causal discovery methods to recov…
A Structured Prediction Approach for Robot Imitation Learning
Anqing Duan, Iason Batzianoulis, Raffaello Camoriano +3
We propose a structured prediction approach for robot imitation learning from demonstrations. Among various tools for robot imitation learning, supervised learning has been observe…