I make short, to-the-point online math tutorials. I struggled with math growing up and have been able to use those experiences to help students improve in math through practical applications and tips.

To some retail traders, former hedge fund manager Jim Cramer is known as the inspiration for the "Inverse Cramer" theory. Autopilot, launched in 2023, allows users to put money behind the theory that ...

Paying invoices sounds simple enough. A vendor creates an invoice and sends a bill, your team approves it, and the money goes out. In practice, though, invoice payments are where a lot of finance ...

Abstract: This article presents a novel and efficient method for characteristic mode decomposition in multistructure systems. By leveraging the translation and rotation matrices of vector spherical ...

Dion2 reduces the cost of Muon’s orthonormalization step by orthonormalizing only a small, selected submatrix at each iteration. This lightweight approach preserves Muon’s strong performance while ...

This repository contains code to precompute the numerical Green's function (NGF) matrix and perform optimization utilizing the PNGF method and direct binary search as described in the paper "Near real ...

Recently, the ConFIG method has also been applied to train physics-constrained generative models: Flow Matching Meets PDEs: A Unified Framework for Physics-Constrained Generation, Giacomo Baldan, ...

Abstract: Data is growing explosively in digital world, which requires efficient technique to store and transmit data. Because of limited resources, we need data compression (DC) techniques to ...

A new study by Shanghai Jiao Tong University and SII Generative AI Research Lab (GAIR) shows that training large language models (LLMs) for complex, autonomous tasks does not require massive datasets.

One of the fundamental operations in machine learning is computing the inverse of a square matrix. But not all matrices have an inverse. The most common way to check if a matrix has an inverse or not ...

Dozens of machine learning algorithms require computing the inverse of a matrix. Computing a matrix inverse is conceptually easy, but implementation is one of the most challenging tasks in numerical ...