Data work in 2026 asks for more than chart building. Professionals are expected to clean data, query databases, explain trends, and present findings clearly across business, finance, product, and ...

Abstract: Graph neural networks (GNNs), as a cutting-edge technology in deep learning, perform particularly well in various tasks that process graph structure data. However, their foundation on ...

So, you want to get better at those tricky LeetCode Python problems, huh? It’s a common goal, especially if you’re aiming for tech jobs. Many people try to just grind through tons of problems, but ...

If you had walked onto a trading floor thirty years ago, you would have heard noise before you saw anything. Phones ringing, ...

Here’s a quick library to write your GPU-based operators and execute them in your Nvidia, AMD, Intel or whatever, along with my new VisualDML tool to design your operators visually. This is a follow ...

print("hello world, I'm learning Python"!) ...

Explore the power of interactive physics visualizations with animated graphs using VPython and GlowScript for dynamic simulations! This guide demonstrates how to create real-time animated graphs that ...

️ Added reconstruction logic to identify which items were chosen ️ Improved knapsack solution beyond just max value ...

Abstract: This paper presents a novel approach to graph learning, GL-AR, which leverages estimated autoregressive coefficients to recover undirected graph structures from time-series graph signals ...

This repository contains a refactoring of the code used in the paper "Learning Latent Graph Structures and Their Uncertainty" (ICML 2025). The code is designed to be modular and easy to use, allowing ...