4 citations · 4 across the 6 of their papers we have counts for
6 papers
CPR: Retrieval Augmented Generation for Copyright Protection
Aditya Golatkar, Alessandro Achille, Luca Zancato +3
Retrieval Augmented Generation (RAG) is emerging as a flexible and robust technique to adapt models to private users data without training, to handle credit attribution, and to all…
Multi-Modal Hallucination Control by Visual Information Grounding
Alessandro Favero, Luca Zancato, Matthew Trager +5
Generative Vision-Language Models (VLMs) are prone to generate plausible-sounding textual answers that, however, are not always grounded in the input image. We investigate this phe…
SemiGPC: Distribution-Aware Label Refinement for Imbalanced Semi-Supervised Learning Using Gaussian Processes
Abdelhak Lemkhenter, Manchen Wang, Luca Zancato +3
In this paper we introduce SemiGPC, a distribution-aware label refinement strategy based on Gaussian Processes where the predictions of the model are derived from the labels poster…
Prompt Algebra for Task Composition
Pramuditha Perera, Matthew Trager, Luca Zancato +2
We investigate whether prompts learned independently for different tasks can be later combined through prompt algebra to obtain a model that supports composition of tasks. We consi…
Train/Test-Time Adaptation with Retrieval
Luca Zancato, Alessandro Achille, Tian Yu Liu +3
We introduce Train/Test-Time Adaptation with Retrieval (), a method to adapt models both at train and test time by means of a retrieval module and a searchable pool of…
À-la-carte Prompt Tuning (APT): Combining Distinct Data Via Composable Prompting
Benjamin Bowman, Alessandro Achille, Luca Zancato +4
We introduce À-la-carte Prompt Tuning (APT), a transformer-based scheme to tune prompts on distinct data so that they can be arbitrarily composed at inference time. The individual…