75 citations · 372 across the 57 of their papers we have counts for
8 papers · 1 filter
Integral Continual Learning Along the Tangent Vector Field of Tasks
Tian Yu Liu, Aditya Golatkar, Stefano Soatto +1
We propose a lightweight continual learning method which incorporates information from specialized datasets incrementally, by integrating it along the vector field of "generalist"…
Critical Learning Periods for Multisensory Integration in Deep Networks
Michael Kleinman, Alessandro Achille, Stefano Soatto
We show that the ability of a neural network to integrate information from diverse sources hinges critically on being exposed to properly correlated signals during the early phases…
On the Learnability of Physical Concepts: Can a Neural Network Understand What's Real?
Alessandro Achille, Stefano Soatto
We revisit the classic signal-to-symbol barrier in light of the remarkable ability of deep neural networks to generate realistic synthetic data. DeepFakes and spoofing highlight th…
On Leave-One-Out Conditional Mutual Information For Generalization
Mohamad Rida Rammal, Alessandro Achille, Aditya Golatkar +2
We derive information theoretic generalization bounds for supervised learning algorithms based on a new measure of leave-one-out conditional mutual information (loo-CMI). Contrary…
Gacs-Korner Common Information Variational Autoencoder
Michael Kleinman, Alessandro Achille, Stefano Soatto +1
We propose a notion of common information that allows one to quantify and separate the information that is shared between two random variables from the information that is unique t…
Task Adaptive Parameter Sharing for Multi-Task Learning
Matthew Wallingford, Hao Li, Alessandro Achille +4
Adapting pre-trained models with broad capabilities has become standard practice for learning a wide range of downstream tasks. The typical approach of fine-tuning different models…