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Found 6 papers, 2 papers with code
Explainable Learning with Gaussian Processes
When using integrated gradients as an attribution method, we show that the attributions of a GPR model also follow a Gaussian process distribution, which quantifies the uncertainty in attribution arising from uncertainty in the model.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+1
Fusion of Gaussian Processes Predictions with Monte Carlo Sampling
In this paper, we operate within the Bayesian paradigm, relying on Gaussian processes as our models.
Dagma-DCE: Interpretable, Non-Parametric Differentiable Causal Discovery
We introduce Dagma-DCE, an interpretable and model-agnostic scheme for differentiable causal discovery.
Near-field Tracking with Large Antenna Arrays: Fundamental Limits and Practical Algorithms
In this paper, we study a near-field tracking problem for inferring the state (i. e., the position and velocity) of a moving source with an ad-hoc observation model that accounts for the phase profile of a large receiving array.
Information Theory Systems and Control Systems and Control Information Theory
Reinforcement Learning for UAV Autonomous Navigation, Mapping and Target Detection
In this paper, we study a joint detection, mapping and navigation problem for a single unmanned aerial vehicle (UAV) equipped with a low complexity radar and flying in an unknown environment.
Dynamic Radar Network of UAVs: A Joint Navigation and Tracking Approach
Nowadays there is a growing research interest on the possibility of enriching small flying robots with autonomous sensing and online navigation capabilities.
