Graph neural networks (GNNs) have recently been used to learn the representations of crystal structures through an end-to-end data-driven approach. However, a systematic top-down approach to evaluate ...
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 ...
Article Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to ...
Immune-related processes are important in underpinning the properties of clinical traits such as prognosis and drug response in cancer. The possibility to extract knowledge learned by artificial ...
Neural systems are remarkably robust against various perturbations, a phenomenon that still requires a clear explanation. Here, we graphically illustrate how neural networks can become robust. We ...
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 ...
tl;dr Our approach distills the residual information of one model with respect to another's and thereby enables translation between fixed off-the-shelf expert models such as BERT and BigGAN without ...
An LSTM autoencoder combines encoder-decoder architecture to compress and reconstruct data effectively. Long Short Term Memory networks excel in learning dependencies in sequential data. The article ...
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