BIOENGINEER

Comparing methods for predicting missing links in email and covert networks

A comparative study evaluates link prediction, hyperlink prediction and ERGM estimation for inferring missing connections in ...
BIOENGINEER

Bayesian Network Matches Classifiers for IgE Sensitization Risk

A Bayesian network calibrated sensitization probabilities in 8,285 NHANES participants while matching logistic regression and ...
umass

Causal inference in network experiments: regression-based analysis and design-based properties

Investigating interference or spillover effects among units is a central task in many social science problems. Network experiments are powerful tools for this task, which avoids endogeneity by ...
SDxCentral

Former Juniper CTO unveils startup tackling AI inference with ‘probability-native silicon’

Former Juniper Networks CTO Raj Yavatkar has unveiled his next endeavor: a startup building silicon focused entirely on inference. Dubbed Ludwig Computing, the upstart is designing “probability-native ...
Semiconductor Engineering

Inference Moves To The Network

Machine-learning inference started out as a data-center activity, but tremendous effort is being put into inference at the edge. At this point, the “edge” is not a well-defined concept, and future ...
Telecoms

Why AI Inference Demands a Different Network Architecture

The economics of artificial intelligence (AI) are definitively moving away from AI training towards AI production or inference and the real-time use of AI models. While training often represents huge ...
Network World

Arrcus targets AI inference bottleneck with policy-aware network fabric

As AI workloads shift from centralized training to distributed inference, the network faces new demands around latency requirements, data sovereignty boundaries, model preferences, and power ...