对于神经网络来说，我们已经习惯了层状网络的思维：数据进来，经过第一层，然后第二层，第三层，最后输出结果。这个过程很像流水线，每一步都是离散的。 但是现实世界的变化是连续的，比如烧开水，谁的温度不是从30度直接跳到40度，而是平滑的上生。

BingoCGN employs cross-partition message quantization to summarize inter-partition message flow, which eliminates the need for irregular off-chip memory access and utilizes a fine-grained structured ...

A technical paper titled “Accelerating Defect Predictions in Semiconductors Using Graph Neural Networks” was published by researchers at Purdue University, Indian Institute of Technology (IIT) Madras, ...

PyTorch 1.10 is production ready, with a rich ecosystem of tools and libraries for deep learning, computer vision, natural language processing, and more. Here's how to get started with PyTorch.

The goal of a regression problem is to predict a single numeric value, for example, predicting the price of a used car based on variables such as mileage, brand and year manufactured. There are ...

Graph neural networks (GNNs) are a relatively recent development in the field of machine learning. Like traditional graphs, a core principle of GNNs is that they model the dependencies and ...

Expect to hear increasing buzz around graph neural network use cases among hyperscalers in the coming year. Behind the scenes, these are already replacing existing recommendation systems and traveling ...

Dr. James McCaffrey of Microsoft Research presents the second of four machine learning articles that detail a complete end-to-end production-quality example of neural regression using PyTorch. The ...