75 citations · 372 across the 57 of their papers we have counts for
14 papers · 1 filter
Meaning Representations from Trajectories in Autoregressive Models
Tian Yu Liu, Matthew Trager, Alessandro Achille +3
We propose to extract meaning representations from autoregressive language models by considering the distribution of all possible trajectories extending an input text. This strateg…
Critical Learning Periods Emerge Even in Deep Linear Networks
Michael Kleinman, Alessandro Achille, Stefano Soatto
Critical learning periods are periods early in development where temporary sensory deficits can have a permanent effect on behavior and learned representations. Despite the radical…
Training Data Protection with Compositional Diffusion Models
Aditya Golatkar, Alessandro Achille, Ashwin Swaminathan +1
We introduce Compartmentalized Diffusion Models (CDM), a method to train different diffusion models (or prompts) on distinct data sources and arbitrarily compose them at inference…
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…
AI Model Disgorgement: Methods and Choices
Alessandro Achille, Michael Kearns, Carson Klingenberg +1
Responsible use of data is an indispensable part of any machine learning (ML) implementation. ML developers must carefully collect and curate their datasets, and document their pro…