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Found 5 papers, 4 papers with code
Infomorphic networks: Locally learning neural networks derived from partial information decomposition
Understanding the intricate cooperation among individual neurons in performing complex tasks remains a challenge to this date.
A Measure of the Complexity of Neural Representations based on Partial Information Decomposition
However, the specific way in which this mutual information about the classification label is distributed among the individual neurons is not well understood: While parts of it may only be obtainable from specific single neurons, other parts are carried redundantly or synergistically by multiple neurons.
Bits and Pieces: Understanding Information Decomposition from Part-whole Relationships and Formal Logic
In this paper we show, first, that the entire theory of partial information decomposition can be derived from considerations of elementary parthood relationships between information contributions.
Introducing a differentiable measure of pointwise shared information
We here present a novel measure that satisfies this property, emerges solely from information-theoretic principles, and has the form of a local mutual information.
Information Theory Information Theory
MAXENT3D_PID: An Estimator for the Maximum-entropy Trivariate Partial Information Decomposition
Chicharro (2017) introduced a procedure to determine multivariate partial information measures within the maximum entropy framework, separating unique, redundant, and synergistic components of information.
Computation Optimization and Control
