Google has launched TorchTPU, an engineering stack enabling PyTorch workloads to run natively on TPU infrastructure for ...

A research team led by Potsdam-based bioinformatician Prof. Dr. Zoran Nikoloski has developed a computational approach and an ...

A research team led by Potsdam-based bioinformatician Prof. Dr Zoran Nikoloski has developed a computational approach and an accompanying tool that enables the detailed analysis and reconstruction of ...

In this tutorial, we build a hierarchical planner agent using an open-source instruct model. We design a structured multi-agent architecture comprising a planner agent, an executor agent, and an ...

Explore core physics concepts and graphing techniques in Python Physics Lesson 3! In this tutorial, we show you how to use Python to visualize physical phenomena, analyze data, and better understand ...

Abstract: Graph neural networks (GNNs) have demonstrated significant success in solving real-world problems using both static and dynamic graph data. While static graphs remain constant, dynamic ...

Dynamic Graph Neural Networks (Dynamic GNNs) have emerged as powerful tools for modeling real-world networks with evolving topologies and node attributes over time. A survey by Professors Zhewei Wei, ...

Nigel Drego, Co-founder and Chief Technology Officer at Quadric, presented the “ONNX and Python to C++: State-of-the-art Graph Compilation” tutorial at this year’s Embedded Vision Summit. Quadric’s ...