In the "This Paper Changed My Life" series, neuroscientists reflect on papers that have profoundly influenced their careers ...

are hybrid algorithms that combine quantum computing and classical optimization. By using parameterized quantum circuits and optimization techniques such as gradient descent, VQAs iteratively adjust ...

In the previous chapter, we learned various strategies to guide AI models 'down the mountain' (optimization algorithms), such as SGD and Adam. The core of these strategies relies on a key piece of ...

Abstract: We introduce, for the first time in wireless communication networks, a quantum gradient descent (QGD) algorithm to maximize sum data rates in non-orthogonal multiple access (NOMA)-based ...

1 Computational and Communication Science and Engineering (CoCSE), The Nelson Mandela African Institution of Science and Technology (NM-AIST), Arusha, Tanzania 2 Life Sciences and Bio-engineering ...

Dr. James McCaffrey presents a complete end-to-end demonstration of the kernel ridge regression technique to predict a single numeric value. The demo uses stochastic gradient descent, one of two ...

The 20-year-old gunman who tried to assassinate President Donald Trump at a rally in Butler, Pennsylvania, last summer experienced a "descent into madness" leading up to the incident, during which he ...

Learn how gradient descent really works by building it step by step in Python. No libraries, no shortcuts—just pure math and code made simple. Donald Trump's Epstein problem keeps coming back Michael ...

1 Department of Computer Science, Nagoya Institute of Technology, Aichi, Japan 2 RIKEN Center for Advanced Intelligence Project, Tokyo, Japan In recent years, a learning method for classifiers using ...

ABSTRACT: In this paper, we consider a more general bi-level optimization problem, where the inner objective function is consisted of three convex functions, involving a smooth and two non-smooth ...