Its dynamic computation graph helps developers build and modify models on the fly, making it a preferred choice for AI researchers, data scientists, and engineers working in neural networks.

Part 7 of learning in public about multiagent systems, specifically on technical/strategic differentiations between AI ...

Deep learning models are represented in PyTorch as Dynamic Computation Graphs (DCGs). Unlike with pre-constructed static graphs, the structure of the neural network is built and modified on the ...

Deep learning models are represented in PyTorch as Dynamic Computation Graphs (DCGs). Unlike with pre-constructed static graphs, the structure of the neural network is built and modified on the ...

In recent years, the Massively Parallel Computation (MPC) model has gained significant attention. However, most distributed and parallel graph algorithms in the MPC model are designed for static ...

With help from Apple's Metal team, PyTorch now includes a backend based on MPS, with processor-specific kernels and a mapping of the PyTorch model computation graph onto the MPS Graph Framework.

TigerGraph is delivering the next stage in the evolution of the graph database. Its Native Parallel Graph (NPG) design focuses on both storage and computation, supporting real-time graph updates ...

A sui generis, multi-model open source database, designed from the ground up to be distributed. ArangoDB keeps up with the times and uses graph, and machine learning, as the entry points for its ...