Justice Department expands FACE Act enforcement to synagogue protests, with Assistant Attorney General Harmeet Dhillon citing cases against accused protesters.

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Today's deep neural networks perform incredible feats when provided sufficient training data. Sadly, annotating enough raw data to train your favorite classifier can often be prohibitively expensive, especially in important scenarios like computer-assisted medical diagnoses where labeling requires the advice of human experts. This issue has led to a surge of interest in active learning, a paradigm introduced to mitigate extravagant labeling costs. Active learning, originally studied by Angluin in 1988 [1], is in essence formed around two basic hypotheses: raw (unlabeled) data is cheap, and not all data is equally useful. The idea is that by adaptively selecting only the most informative data to label, we can get the same accuracy without the prohibitive cost.

Text-to-image diffusion has attracted vast attention due to its impressive image-generation capabilities.

Republican lawmakers Carlos Gimenez, Mario Diaz-Balart and Maria Elvira Salazar demand Trump administration shut off commercial support for Cuba's Communist regime after oil blockade.

Fox News poll finds 60% of voters believe you can find love with someone whose political views oppose their own this Valentine's Day season.