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 ...
Abstract: This paper presents an approach to Model Predictive Control (MPC) for nonlinear systems by combining Koopman operator theory with autoencoder networks enhanced by temporal layers for ...
Method of Multi-Mode Sensor Data Fusion with an Adaptive Deep Coupling Convolutional Auto-Encoder ()
To address the difficulties in fusing multi-mode sensor data for complex industrial machinery, an adaptive deep coupling convolutional auto-encoder (ADCCAE) fusion method was proposed. First, the ...
Abstract: Operations of power distribution systems with Distributed Energy Resources (DERs) can be managed in scalable manner with advanced distributed control algorithms. Distributed algorithms ...
Spatially resolved transcriptomics (SRT) technologies, such as spatial transcriptomics (ST) (Ståhl et al., 2016), 10x Visium, and Slide-seqV2 (Stickels et al., 2021), can measure the transcript ...
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 ...
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 ...
Dr. James McCaffrey of Microsoft Research provides full code and step-by-step examples of anomaly detection, used to find items in a dataset that are different from the majority for tasks like ...
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 ...
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