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Found 5 papers, 2 papers with code
On The Relationship Between Universal Adversarial Attacks And Sparse Representations
To this end, we show that sparse coding algorithms, and the neural network-based learned iterative shrinkage thresholding algorithm (LISTA) among them, suffer from this sensitivity, and that common attacks on neural networks can be expressed as attacks on the sparse representation of the input image.
AI Increases Global Access to Reliable Flood Forecasts
Using AI, we achieve reliability in predicting extreme riverine events in ungauged watersheds at up to a 5-day lead time that is similar to or better than the reliability of nowcasts (0-day lead time) from a current state of the art global modeling system (the Copernicus Emergency Management Service Global Flood Awareness System).
Flood forecasting with machine learning models in an operational framework
During the 2021 monsoon season, the flood warning system was operational in India and Bangladesh, covering flood-prone regions around rivers with a total area of 287, 000 km2, home to more than 350M people.
Separable Joint Blind Deconvolution and Demixing
Blind deconvolution and demixing is the problem of reconstructing convolved signals and kernels from the sum of their convolutions.
Face Authentication from Grayscale Coded Light Field
Face verification is a fast-growing authentication tool for everyday systems, such as smartphones.
