Researchers have discovered a critical flaw in PyTorch’s distributed RPC system, allowing attackers to execute arbitrary commands on the OS and steal AI training data.

Remote attackers can exploit the vulnerability to compromise master nodes that are initiating the distributed training, which could result in the theft of sensitive data related to AI. CVE-2024-5480, ...

Next, you need to select the input parameters that you want to vary and test in your sensitivity analysis. These can be the features or attributes of your data, the hyperparameters of your data ...

PyTorch uses the "\" character for line continuation. The predictors are left as 32-bit values, but the class labels-to-predict are cast to a one-dimensional int64 tensor. Many of the examples I've ...

Multi-Input Deep Neural Networks with PyTorch-Lightning - Combine Image and Tabular Data One of the most significant advantages of artificial deep neural networks has always been that they can pretty ...

Figure 1: CNN for MNIST Data Using PyTorch Demo Run . ... Because the model accuracy on the test data is close to the accuracy on the training data, it appears that the model is not overfitted. ...

Pytorch models are programs, so treat its security seriously -- running untrusted models is equivalent to running untrusted code.In general we recommend that model weights and the python code for the ...

This paper presents a comprehensive and quantitative study of bugs related to Data Path in PyTorch with a focus on tensor management in memory. The bugs were reported from 2017 to 2024. Analyzing ...