Abstract: The emerging field of graph learning, which aims to learn reasonable graph structures from data, plays a vital role in Graph Signal Processing (GSP) and finds applications in various data ...

Abstract: Graph Convolutional Networks (GCNs) have been widely studied for attribute graph data learning. In many applications, graph node attributes/features may contain various kinds of noises, such ...

Physics and Python stuff. Most of the videos here are either adapted from class lectures or solving physics problems. I really like to use numerical calculations without all the fancy programming ...

import numpy as np def doSomethingInPython(vec: np.ndarray) -> np.ndarray: """ Accepts a numpy vector and returns a new vector where each element is: sin(x) + log(abs ...

A TechRadar article noted that nearly 90% of enterprise information (documents, emails, videos) lies dormant in unstructured systems. This "dark data" isn't just neglected; it's a liability. GenAI ...

STM-Graph is a Python framework for analyzing spatial-temporal urban data and doing predictions using Graph Neural Networks. It provides a complete end-to-end pipeline from raw event data to trained ...

I co-created Graph Neural Networks while at Stanford. I recognized early on that this technology was incredibly powerful. Every data point, every observation, every piece of knowledge doesn’t exist in ...

MathWorks, a leading developer of mathematical simulation and computing software, revealed that a ransomware gang stole the data of over 10,000 people after breaching its network in April. The company ...

Forbes contributors publish independent expert analyses and insights. I track enterprise software application development & data management. Jul 03, 2025, 10:43am EDT Business 3d tablet virtual growth ...

Have you ever found yourself wrestling with Excel formulas, wishing for a more powerful tool to handle your data? Or maybe you’ve heard the buzz about Python in Excel and wondered if it’s truly the ...