How Cato Networks uses Amazon Bedrock to transform free text search into structured GraphQL queries
This is a guest post authored by Asaf Fried, Daniel Pienica, Sergey Volkovich from Cato Networks. Cato Networks is a ...
Tag: networks
Neural Networks for Time Series Imputation: Addressing Missing Data | by Sara Nóbrega | January 2025
Part 3: Discover how a simple Keras sequential model can be effectiveSource: DALL-E.One of the common problems in Time series ...
How to use AI to manage next-generation district networks
https://www.youtube.com/watch?v=eT12UTCWJ5Q eSchool News was able to catch up with Mittal Parekh, Senior Director of Product Marketing and Technical Marketing at ...
CMU researchers introduce TNNGen: an AI framework that automates the design of temporal neural networks (TNNs) from PyTorch software models to post-design netlists
The design of neuromorphic sensory processing units (NSPUs) based on temporal neural networks (TNNs) is a very challenging task due ...
Superposition: what makes it difficult to explain neural networks | by Shuyang Xiang | December 2024
When there are more features than dimensions of the modelIt would be ideal if the world of neural networks represented ...
Neural Networks for Scalable Temporal Logic Model Verification in Hardware Verification
Ensuring the accuracy of electronic designs is essential, as hardware failures are permanent in post-production and can compromise the reliability ...
ZipNN: a new lossless compression method adapted to neural networks
The rapid advancement of large language models (LLMs) has exposed critical infrastructure challenges in model implementation and communication. As models ...
This Amazon AI paper introduces DF-GNN: a dynamic kernel fusion framework for accelerating attention graph neural networks on GPUs
Graph neural networks (GNN) is a rapidly advancing field in machine learning, specifically designed to analyze graphically structured data representing ...
NFT sales fall to $146.5 million: Ethereum and Bitcoin networks recover
As bitcoin continues to face resistance near the $100,000 mark, the nft market has seen a 1.6% drop, with total ...
GRAF: A machine learning framework that converts multiplexed heterogeneous networks into homogeneous networks to make them more suitable for graph representation learning
Real-world networks, such as those in biomedical and multi-omics datasets, often exhibit complex structures characterized by multiple types of nodes ...
