A biologically plausible reinforcement learning model that integrates associative memory and hippocampal remapping explains context-dependent flexible behavior, neural dynamics, and psychosis-related ...

Abstract: This exploratory study evaluates Gaussian blur as a baseline smoothing technique to reduce noise in digital plant images. Using Python with OpenCV, NumPy, Matplotlib, and scikit-image, we ...

:class:`moe.optimal_learning.python.interfaces.gaussian_process_interface.GaussianProcessInterface`. They specifies the interface that a GaussianProcess ...

Abstract: Gaussian process state-space models (GPSSMs) offer a principled framework for learning and inference in nonlinear dynamical systems with uncertainty quantification. However, existing GPSSMs ...

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

Department of Environmental Science, Institute of Eco-Chongming, School of Ecological and Environmental Sciences, East China Normal University, Shanghai 200241, China ...

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 ...

In today’s data-rich environment, business are always looking for a way to capitalize on available data for new insights and increased efficiencies. Given the escalating volumes of data and the ...

Neural networks revolutionized machine learning for classical computers: self-driving cars, language translation and even artificial intelligence software were all made possible. It is no wonder, then ...