Japanese researchers develop an adaptive robot motion system that enables human-like grasping using minimal training data.

Regularization in Deep Learning is very important to overcome overfitting. When your training accuracy is very high, but test accuracy is very low, the model highly overfits the training dataset set ...

Specific problems have been studied simultaneously by different areas, using their own concepts and definitions. When each area defines a solution to this problem, it may result in similar, analogous, ...

Netflix has partnered with Lionsgate to produce a brand-new sci-fi action movie starring Alan Ritchson, who is becoming a highly sought-after action star. Filming concluded in late 2024 and is ...

ABSTRACT: The stochastic configuration network (SCN) is an incremental neural network with fast convergence, efficient learning and strong generalization ability, and is widely used in fields such as ...

ABSTRACT: The stock market faces persistent challenges, including inefficiencies, volatility, and barriers to entry, which hinder its accessibility and reliability for investors. This paper explores ...

Abstract: We examine the problem of classifying biological sequences, and in particular the challenge of generalizing results to novel input data. We observe that the high-dimensionality of sequence ...

Weight decay and ℓ2 regularization are crucial in machine learning, especially in limiting network capacity and reducing irrelevant weight components. These techniques align with Occam’s razor ...

Artificial intelligence (AI) is transforming our world, but within this broad domain, two distinct technologies often confuse people: machine learning (ML) and generative AI. While both are ...

Gradient descent algorithms on Riemannian manifolds have been used recently for the optimization of quantum channels. In this contribution, we investigate the influence of various regularization terms ...