Five takeaways for network professionals from Rami Rahim, former CEO of Juniper Networks and current head of HPE Networking.

Spread the love“`html Understanding how to create a neural network can be a game-changer in the fields of artificial intelligence and machine learning. As industries increasingly rely on data-driven ...

Abstract: The software engineering community is working to develop reliable metrics to improve software quality. It is estimated that understanding the source code accounts for 60% of the software ...

Implementation and example training scripts of various flavours of graph neural network in TensorFlow 2.0. Much of it is based on the code in the tf-gnn-samples repo. The code is maintained by the ...

This is the github repo for sharing the code for implementing the Graph Markov Network (GMN) proposed in [1]. The GMN is proposed to solve the traffic forecasting problems while the traffic data has ...

Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey 08854, United ...

Machine learning is a complex discipline but implementing machine learning models is far less daunting than it used to be. Machine learning frameworks like Google’s TensorFlow ease the process of ...

Data amount and variety have soared as never seen before, offering a unique opportunity to better understand complex systems. Among the different modes of representation of data, networks appear as ...

In terms of seizure prediction, how to fully mine relational data information among multiple channels of epileptic EEG? This is a scientific research subject worthy of further exploration. Recently, ...

Modern nanoscale connectomics research commonly involves the conversion of microscopy imagery data into a graph representation of connectivity, where nodes represent neurons, and directed edges ...