1 code implementation • NeurIPS 2023 • Michael Kleinman, Alessandro Achille, Stefano Soatto, Jonathan Kao
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 to each.
no code implementations • NeurIPS 2021 • Brandon McMahan, Michael Kleinman, Jonathan Kao
For relatively complex tasks, we find that attractor topology is invariant to the choice of learning rule, but representational geometry is not.
no code implementations • NeurIPS 2021 • Michael Kleinman, Chandramouli Chandrasekaran, Jonathan Kao
Recurrent neural networks (RNNs) trained on neuroscience-based tasks have been widely used as models for cortical areas performing analogous tasks.
no code implementations • ICLR Workshop Neural_Compression 2021 • Michael Kleinman, Alessandro Achille, Stefano Soatto, Jonathan Kao
We introduce the Redundant Information Neural Estimator (RINE), a method that allows efficient estimation for the component of information about a target variable that is common to a set of sources, previously referred to as the “redundant information.” We show that existing definitions of the redundant information can be recast in terms of an optimization over a family of deterministic or stochastic functions.