4 citations · 15 across the 16 of their papers we have counts for
6 papers · 1 filter
PLATE: Plasticity-Tunable Efficient Adapters for Geometry-Aware Continual Learning
Romain Cosentino
We develop a continual learning method for pretrained models that \emph{requires no access to old-task data}, addressing a practical barrier in foundation model adaptation where pr…
The Geometry of Self-supervised Learning Models and its Impact on Transfer Learning
Romain Cosentino, Sarath Shekkizhar, Mahdi Soltanolkotabi +2
Self-supervised learning (SSL) has emerged as a desirable paradigm in computer vision due to the inability of supervised models to learn representations that can generalize in doma…
Toward a Geometrical Understanding of Self-supervised Contrastive Learning
Romain Cosentino, Anirvan Sengupta, Salman Avestimehr +4
Self-supervised learning (SSL) is currently one of the premier techniques to create data representations that are actionable for transfer learning in the absence of human annotatio…
Spatial Transformer K-Means
Romain Cosentino, Randall Balestriero, Yanis Bahroun +3
K-means defines one of the most employed centroid-based clustering algorithms with performances tied to the data's embedding. Intricate data embeddings have been designed to push $…
Deep Autoencoders: From Understanding to Generalization Guarantees
Romain Cosentino, Randall Balestriero, Richard Baraniuk +1
A big mystery in deep learning continues to be the ability of methods to generalize when the number of model parameters is larger than the number of training examples. In this work…
The Geometry of Deep Networks: Power Diagram Subdivision
Randall Balestriero, Romain Cosentino, Behnaam Aazhang +1
We study the geometry of deep (neural) networks (DNs) with piecewise affine and convex nonlinearities. The layers of such DNs have been shown to be {\em max-affine spline operators…