Abstract: As modern System-on-chip (SoC) designs grow in complexity, traditional debug mechanisms involving manual waveform and log analysis struggle to keep up with the volume and intricacies of ...

New papers on Apple's machine learning blog detail how AI can be used for faster, cheaper, and more effective QE testing, as well as for bug fixing and identification. Now, one of its new studies ...

Apple has published three interesting studies that offer some insight into how AI-based development could improve workflows, quality, and productivity. Here are the details. Software Defect Prediction ...

Reference implementation for a variational autoencoder in TensorFlow and PyTorch. I recommend the PyTorch version. It includes an example of a more expressive variational family, the inverse ...

Abstract: Operations of power distribution systems with Distributed Energy Resources (DERs) can be managed in scalable manner with advanced distributed control algorithms. Distributed algorithms ...

Variational Autoencoder with Arbitrary Conditioning (VAEAC) is a neural probabilistic model based on variational autoencoder that can be conditioned on an arbitrary subset of observed features and ...

Molecular dynamics (MD) simulations have been actively used in the study of protein structure and function. However, extensive sampling in the protein conformational space requires large computational ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Non-Commercial (NC): Only non-commercial uses of the work are permitted. No ...

Generating synthetic data is useful when you have imbalanced training data for a particular class, for example, generating synthetic females in a dataset of employees that has many males but few ...