Context graphs, graph memory, and ontologies for AI are converging. What does this mean for enterprise AI in 2026?
Qualcomm confirmed a $3.92 billion all-stock deal to buy AI software startup Modular, paired with a Meta Platforms CPU ...
Abstract: Graph neural networks (GNNs) are highly effective models for tasks involving non-Euclidean data. To improve their performance, researchers have explored strategies to increase the depth of ...
In the life cycle of any kernel branch, patch releases, those minor “.x” updates, play a vital role in refining performance, patching regressions, and ironing out rough edges. Kernel 6.15.4 is one ...
The PyGSP is a Python package to ease Signal Processing on Graphs. The documentation is available on Read the Docs and development takes place on GitHub. A (mostly unmaintained) Matlab version exists.
deepmd-gnn is a DeePMD-kit plugin for various graph neural network (GNN) models, which connects DeePMD-kit and atomistic GNN packages by enabling GNN models in DeePMD-kit. The official MACE and NequIP ...
Abstract: Identifying links within biological networks is important in various biomedical applications. Recent studies have revealed that each node in a network may play a unique role in different ...
Once you get past the chatbot hype, it’s clear that generative AI is a useful tool, providing a way of navigating applications and services using natural language. By tying our large language models ...
Microsoft’s Semantic Kernel SDK makes it easier to manage complex prompts and get focused results from large language models like GPT. At first glance, building a large language model (LLM) like GPT-4 ...
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