Scientific Research Publishing

Wilson, A.C., Roelofs, R., Stern, M., Srebro, N. and Recht, B. (2017) The Marginal Value of Adaptive Gradient Methods in Machine Learning. Advances in Neural Information ...

ABSTRACT: Artificial deep neural networks (ADNNs) have become a cornerstone of modern machine learning, but they are not immune to challenges. One of the most significant problems plaguing ADNNs is ...
IEEE

Effect Conjugate Gradient Method for Solving Nonlinear Optimization Problems

Abstract: Conjugate gradient techniques are widely used to solve unconstrained optimization issues. The accelerated conjugate gradient approach provides superior numerical effects for the ...
Frontiers

Numerical optimization of large-scale monotone equations using the free-derivative spectral conjugate gradient method

This study introduced an efficient method for solving non-linear equations. Our approach enhances the traditional spectral conjugate gradient parameter, resulting in significant improvements in the ...
marktechpost

This AI Paper Introduces Semantic Backpropagation and Gradient Descent: Advanced Methods for Optimizing Language-Based Agentic Systems

Language-based agentic systems represent a breakthrough in artificial intelligence, allowing for the automation of tasks such as question-answering, programming, and advanced problem-solving. These ...
marktechpost

ADOPT: A Universal Adaptive Gradient Method for Reliable Convergence without Hyperparameter Tuning

Adam is widely used in deep learning as an adaptive optimization algorithm, but it struggles with convergence unless the hyperparameter β2 is adjusted based on the specific problem. Attempts to fix ...
Frontiers

Improving the convergence of an iterative algorithm for solving arbitrary linear equation systems using classical or quantum binary optimization

1 Centro Brasileiro de Pesquisas Físicas, Rio de Janeiro, RJ, Brazil 2 Petróleo Brasileiro S.A., Centro de Pesquisas Leopoldo Miguez de Mello, Rio de Janeiro, Brazil Recent advancements in quantum ...