Conventional methods for UQ and calibration of deep learning-based ISP solvers primarily include deep ensemble and dropout-based methods based on Bayesian neural networks (BNNs). However, these ...

We use this handy neural mechanism to learn ... the traditional Hopfield network model is powerful, but it doesn't tell the full story of how new information guides memory retrieval.

A prototype system that uses a CNN encoder and edge-based features to retrieve visually similar fashion items from the Fashion MNIST dataset using cosine similarity.

Geometric Dynamic Variational Autoencoders (GD-VAEs) for learning embedding maps for nonlinear dynamics into general latent spaces. This includes methods for standard latent spaces or manifold latent ...

and reveals a novel neural-glia-fibroblast-lymphatic regulatory axis. This provides a new framework for understanding how the brain adapts its lymphatic network based on functional needs ...

The autoencoder, which consists of a nonlinear encoder and a linear decoder, plays an important role to learn features from the nonlinear samples. Meanwhile, the learned features are used as a new ...