@article{shridhar2019comprehensive, title={A comprehensive guide to bayesian convolutional neural network with variational inference}, author={Shridhar, Kumar and Laumann, Felix and Liwicki, Marcus}, ...

light-weight Bayesian convolutional neural networks for image segmentation. They extend a number of ideas from state-of-the-art alternatives and encapsulate them with Bayesian inference through Monte ...

However, the network topology can be radically recast after an adversarial attack and may remain unknown for subsequent analysis. In this work, we propose a novel Bayesian sequential learning approach ...

Artificial Intelligence (AI) and automation are central to Industry 4.0, driving complex decision-making, optimization, and ...

After people stopped caring, artificial intelligence got more interesting.

The mathematical foundations of deep learning have been crucial in driving the field's advancements and potential future breakthroughs ...

In collaboration with the International Conference on Research in Computational Molecular Biology (RECOMB), Genome ...

And then we beam with pleasure as we realize we've got most of the knowledge we need to understand and appreciate Bayesian Networks already. The remainder of the tutorial introduces the important ...

Researchers used single-cell and spatial genomics to map cell-type-specific changes in the middle temporal gyrus, identifying ...

For example, frame-based systems and semantic networks were created to store linguistic and ... semantic interpretation, and ...

Researchers introduced Controllable Autoregressive Modeling (CAR), a new framework that improves control and efficiency in ...