Highlights Network engineers are increasingly adopting Python libraries to automate device management, configuration, and monitoring.Tools like Nornir, Netmiko, ...

The $12K machine promises AI performance can scale to 32 chip servers and beyond but an immature software stack makes ...

Graph Neural Networks (GNNs) are supposed to excel at graph-structured data. But on Elliptic++ Bitcoin fraud detection, a simple XGBoost model beats all GNN baselines by 49%. This repository ...

ABSTRACT: Cloud infrastructure anomalies cause significant downtime and financial losses (estimated at $2.5 M/hour for major services). Traditional anomaly detection methods fail to capture complex ...

AI for Science Institute (CUAISci), Cornell University, Ithaca, New York 14853, United States Systems Engineering, College of Engineering, Cornell University, Ithaca, New York 14853, United States AI ...

From a neuroscience perspective, artificial neural networks are regarded as abstract models of biological neurons, yet they rely on biologically implausible backpropagation for training. Energy-based ...

Learn how Network in Network (NiN) architectures work and how to implement them using PyTorch. This tutorial covers the concept, benefits, and step-by-step coding examples to help you build better ...

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jcim.5c01525. Efficiency analysis of different normalization strategies ...

I'm compiling part of my model, and the logs instruct me to report an issue. self.decoder = torch.compile(self.decoder, backend='eager') I get these graph breaks ...

Imagine standing atop a mountain, gazing at the vast landscape below, trying to make sense of the world around you. For centuries, explorers relied on such vantage points to map their surroundings.