Ourcontributionistoextend theidentifiability theory ofdeepgenerativemodels for a very broad class of structured models.

Plus: Apple's Lockdown mode keeps the FBI out of a reporter's phone, Elon Musk's Starlink cuts off Russian forces, and more. An analysis by WIRED this week found that ICE and CBP's face recognition app Mobile Fortify, which is being used to identify people across the United States, isn't actually designed to verify who people are and was only approved for Department of Homeland Security use by relaxing some of the agency's own privacy rules. WIRED took a close look at highly militarized ICE and CBP units that use extreme tactics typically seen only in active combat. Two agents involved in the shooting deaths of US citizens in Minneapolis are reportedly members of these paramilitary units. And a new report from the Public Service Alliance this week found that data brokers can fuel violence against public servants, who are facing more and more threats but have few ways to protect their personal information under state privacy laws.

We propose a novel class of graph neural networks based on the discretised Beltrami flow, a non-Euclidean diffusion PDE.

In this paper, we focus on a specific federated learning scenario: Vertical Federated Learning(VFL) [5].

Deep machine unlearning is the problem of'removing' from a trained neural network a subset of its training set.

Effectively leveraging large, previously collected datasets in reinforcement learning (RL) is a key challenge for large-scale real-world applications. Offline RL algorithms promise to learn effective policies from previously-collected, static datasets without further interaction.