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Found 5 papers, 1 papers with code
PAC Learnability under Explanation-Preserving Graph Perturbations
It is shown that the sample complexity of explanation-assisted learning can be arbitrarily smaller than explanation-agnostic learning.
Factorized Explainer for Graph Neural Networks
Instead, we argue that a modified GIB principle may be used to avoid the aforementioned trivial solutions.
Towards Robust Fidelity for Evaluating Explainability of Graph Neural Networks
An explanation function for GNNs takes a pre-trained GNN along with a graph as input, to produce a `sufficient statistic' subgraph with respect to the graph label.
Optimal Fault-Tolerant Data Fusion in Sensor Networks: Fundamental Limits and Efficient Algorithms
Distributed estimation in the context of sensor networks is considered, where distributed agents are given a set of sensor measurements, and are tasked with estimating a target variable.
Quantifying the Capacity Gains in Coarsely Quantized SISO Systems with Nonlinear Analog Operators
While reducing the number and resolution of the ADCs decreases power consumption, it also leads to a reduction in channel capacity due to the information loss induced by coarse quantization.
