Evaluating a policy by deploying it in the real world can be risky and costly.

Many events occur in the world. Some event types are stochastically excited or inhibited--in the sense of having their probabilities elevated or decreased--by patterns in the sequence of previous events. Discovering such patterns can help us predict which type of event will happen next and when. We model streams of discrete events in continuous time, by constructing a neurally self-modulating multivariate point process in which the intensities of multiple event types evolve according to a novel continuous-time LSTM . This generative model allows past events to influence the future in complex and realistic ways, by conditioning future event intensities on the hidden state of a recurrent neural network that has consumed the stream of past events. Our model has desirable qualitative properties. It achieves competitive likelihood and predictive accuracy on real and synthetic datasets, including under missing-data conditions.

Imitation learning has traditionally been applied to learn a single task from demonstrations thereof. The requirement of structured and isolated demonstrations limits the scalability of imitation learning approaches as they are difficult to apply to real-world scenarios, where robots have to be able to execute a multitude of tasks. In this paper, we propose a multi-modal imitation learning framework that is able to segment and imitate skills from unlabelled and unstructured demonstrations by learning skill segmentation and imitation learning jointly. The extensive simulation results indicate that our method can efficiently separate the demonstrations into individual skills and learn to imitate them using a single multi-modal policy.

New analysis reveals why America's $5 trillion AI race against China proves as consequential as the Cold War, citing economic dominance and national security at stake.

We're learning more about what vitamin D does to our bodies The sunshine vitamin could affect your immune system and heart health. It has started to get really wintry here in London over the last few days. The mornings are frosty, the wind is biting, and it's already dark by the time I pick my kids up from school. The darkness in particular has got me thinking about vitamin D, a.k.a. the sunshine vitamin. At a checkup a few years ago, a doctor told me I was deficient in vitamin D. But he wouldn't write me a prescription for supplements, simply because, as he put it, in the UK is deficient. Putting the entire population on vitamin D supplements would be too expensive for the country's national health service, he told me.

The Public Says'No Thanks' The maker of the eCoffee Energyband says it electrically stimulates your nerves to keep you alert. Researchers are skeptical, and critics see it as a way for China's bosses to keep workers productive. Forget coffee, you can now stay alert by strapping on a wristband that lightly zaps you awake. That's what eCoffee Energyband, a Chinese gadget that sells for just over $100, is claiming to do. First released in late 2023, the product is a lightweight wearable with two electrode pads that sit against the inner wrist.

Kernel methods have recently attracted resurgent interest, showing performance competitive with deep neural networks in tasks such as speech recognition.

Figure 1: Simple examples of identifiable structures using the proposed approach.

Following [25, 36, 26], we summarize the unique challenges of federated learning below.

We ask here how the brain might solve the SLAM problem.