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. It Sits on a Vast Haul of Mineral Wealth. Now This Arctic ...

When getting the gradient of functions involving the diagonal of the kronecker product there is a significant increase in vram usage. In some rough tests anywhere from 2x -10x . In the example below ...

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 ...

Abstract: Gradient variance errors in gradient-based search methods are largely mitigated using momentum, however the bias gradient errors may fail the numerical search methods in reaching the true ...

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Vivek Yadav, an engineering manager from ...

One of the long-standing bottlenecks for researchers and data scientists is the inherent limitation of the tools they use for numerical computation. NumPy, the go-to library for numerical operations ...

Abstract: In this article, we propose a distributional policy-gradient method based on distributional reinforcement learning (RL) and policy gradient. Conventional RL algorithms typically estimate the ...

Understanding how convolutional neural networks (CNNs) operate is essential in deep learning. However, implementing these networks, especially convolutions and gradient calculations, can be ...