36 citations · 80 across the 13 of their papers we have counts for
18 papers
Towards Visual Foundational Models of Physical Scenes
Chethan Parameshwara, Alessandro Achille, Matthew Trager +7
We describe a first step towards learning general-purpose visual representations of physical scenes using only image prediction as a training criterion. To do so, we first define "…
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…
Function Space and Critical Points of Linear Convolutional Networks
Kathlén Kohn, Guido Montúfar, Vahid Shahverdi +1
We study the geometry of linear networks with one-dimensional convolutional layers. The function spaces of these networks can be identified with semi-algebraic families of polynomi…
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…
Linear Spaces of Meanings: Compositional Structures in Vision-Language Models
Matthew Trager, Pramuditha Perera, Luca Zancato +3
We investigate compositional structures in data embeddings from pre-trained vision-language models (VLMs). Traditionally, compositionality has been associated with algebraic operat…