A suspect in the 1MDB scandal has died weeks after being deported to Malaysia to face questioning over his role in the $4.5bn fraud. Kee Kok Thiam died in hospital on Monday following a "sudden massive stroke" and was cremated on Wednesday morning, Kee's family said in a statement. "We urge all parties not to entertain any speculations on this unfortunate event and allow the family the space to grief [sic] on his passing," the statement said. News of the 56-year-old businessman's death comes hours after Al Jazeera reported that the Malaysian Anti-Corruption Commission (MACC) had confirmed the whereabouts of fugitive Malaysian financier Jho Taek Low – the alleged mastermind of the 1MDB scandal – in Macau based on its questioning of Kee. The MACC said that Kee, who was deported from Macau earlier this month, revealed he had met with Low and other 1MDB fugitives in the Chinese territory and that Low had instructed him "not to return to Malaysia as a witness in the 1MDB case".

As deep neural networks (DNNs) get adopted in an ever-increasing number of applications, explainability has emerged as a crucial desideratum for these models. In many real-world tasks, one of the principal reasons for requiring explainability is to in turn assess prediction robustness, where predictions (i.e., class labels) that do not conform to their respective explanations (e.g., presence or absence of a concept in the input) are deemed to be unreliable. However, most, if not all, prior methods for checking explanation-conformity (e.g., LIME, TCAV, saliency maps) require significant manual intervention, which hinders their large-scale deployability. In this paper, we propose a robustness-assessment framework, at the core of which is the idea of using machine-checkable concepts. Our framework defines a large number of concepts that the DNN explanations could be based on and performs the explanation-conformity check at test time to assess prediction robustness. Both steps are executed in an automated manner without requiring any human intervention and are easily scaled to datasets with a very large number of classes. Experiments on real-world datasets and human surveys show that our framework is able to enhance prediction robustness significantly: the predictions marked to be robust by our framework have significantly higher accuracy and are more robust to adversarial perturbations.

This paper introduces a new approach for multi-agent communication learning called multi-agent counterfactual communication (MACC) learning. Many real-world problems are currently tackled using multi-agent techniques. However, in many of these tasks the agents do not observe the full state of the environment but only a limited observation. This absence of knowledge about the full state makes completing the objectives significantly more complex or even impossible. The key to this problem lies in sharing observation information between agents or learning how to communicate the essential data. In this paper we present a novel multi-agent communication learning approach called MACC. It addresses the partial observability problem of the agents. MACC lets the agent learn the action policy and the communication policy simultaneously. We focus on decentralized Markov Decision Processes (Dec-MDP), where the agents have joint observability. This means that the full state of the environment can be determined using the observations of all agents. MACC uses counterfactual reasoning to train both the action and the communication policy. This allows the agents to anticipate on how other agents will react to certain messages and on how the environment will react to certain actions, allowing them to learn more effective policies. MACC uses actor-critic with a centralized critic and decentralized actors. The critic is used to calculate an advantage for both the action and communication policy. We demonstrate our method by applying it on the Simple Reference Particle environment of OpenAI and a MNIST game. Our results are compared with a communication and non-communication baseline. These experiments demonstrate that MACC is able to train agents for each of these problems with effective communication policies.

What in the world do they look like? Which one is better and Why? Why the authors design them like that? So, let's try to solve these doubts step by step. MobileNet v1 is smart enough to decompose the standard convolution operation into two separate operations: depth-wise (or channel-wise) convolution and point-wise convolution.