Dr. James McCaffrey presents a complete end-to-end demonstration of linear regression using pseudo-inverse training. Compared to other training techniques, such as stochastic gradient descent, ...

The issue: Many runners (particularly women) report that their fitness trackers tell them they’re exercising in a higher zone ...

This important study reports three experiments examining how the subjective experience of task regularities influences perceptual decision-making. Although the evidence linking subjective ratings to ...

This important study introduces a new biology-informed strategy for deep learning models aiming to predict mutational effects in antibody sequences. It provides solid evidence that separating ...

Abstract: This article proposes a data-driven inverse reinforcement learning (RL) control algorithm for nonzero-sum multiplayer games in linear continuous-time differential dynamical systems. The ...

Solving robot IK by finding zeros of a polynomial, from "IK-Geo: Unified Robot Inverse Kinematics Using Subproblem Decomposition" and "Redundancy parameterization and inverse kinematics of 7-DOF ...

Background Inflammatory bowel disease (IBD) arises from complex interactions among diet, host and gut microbiome. Although diet influences intestinal inflammation, the microbial and metabolic pathways ...

Abstract: Linear codes are widely studied in coding theory as they have nice applications in distributed storage, combinatorics, lattices, cryptography and so on. Constructing linear codes with ...

Learn how the Least Squares Criterion determines the line of best fit for data analysis, enhancing predictive accuracy in finance, economics, and investing.

Deep learning (DL) methods are currently the natural choice for solving linear inverse problems. They have been applied to super-resolve and restore images, as well as to reconstruct MR and CT images.