The models we evaluate do not have any abstract inference rules built-in, and yet they closely match human reasoning effort. Further, we can conclude very little about how the model reasons and ...

Abstract: Robot interaction control with variable impedance parameters may conform to task requirements during continuous interaction with dynamic environments. Iterative learning (IL) is effective to ...

Gov. Ron DeSantis has released a $117 billion budget proposal. Here's what Floridians need to know about what comes next. Gov. Ron DeSantis has released a $117 billion budget proposal. It's around $2 ...

SVGP-KAN is a library for building interpretable, probabilistic, and scalable neural networks. It merges the architecture of Kolmogorov-Arnold Networks (KANs) with ...

This important work introduces a family of interpretable Gaussian process models that allows us to learn and model sequence-function relationships in biomolecules. These models are applied to three ...

ABSTRACT: This paper introduces a method to develop a common model based on machine learning (ML) that predicts the mechanical behavior of a family with three composite materials. The latter are ...

Spatiotemporal Gaussian process modeling for environmental data: non-stationary PDE prior, deep kernels, multi-fidelity fusion, and A-optimal sampling.非稳态 PDE ...

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