A big part of AI and Deep Learning these days is the tuning/optimizing of the algorithms for speed and accuracy. Much of today’s deep learning algorithms involve the use of the gradient descent ...

a) Conceptual diagram of the on-chip optical processor used for optical switching and channel decoder in an MDM optical communications system. (b) Integrated reconfigurable optical processor schematic ...

In this paper, we presented a new three-term conjugate gradient method based on combining the conjugate gradient method proposed by Cheng et al [15] with the idea of the modified FR method [22]. In ...

We propose a functional gradient descent algorithm (FGD) for estimating volatility and conditional covariances (given the past) for very high-dimensional financial time series of asset price returns.

Optimization problems can be tricky, but they make the world work better. These kinds of questions, which strive for the best way of doing something, are absolutely everywhere. Your phone’s GPS ...

Modeled on the human brain, neural networks are one of the most common styles of machine learning. Get started with the basic design and concepts of artificial neural networks. Artificial intelligence ...

This paper proposes a path-based algorithm for solving the well-known logit-based stochastic user equilibrium (SUE) problem in transportation planning and management. Based on the gradient projection ...

Machine learning happens a lot like erosion. Data is hurled at a mathematical model like grains of sand skittering across a rocky landscape. Some of those grains simply sail along with little or no ...