From one Dubai café to 13 countries: How AI is powering FiLLi Cafe’s rise - Gulf Business
<a href="https://news.google.com/rss/articles/CBMiqgFBVV95cUxNV2Uxc3pjNjNOa2QyNk1LaWNBcFdXSDlaLVg2Z19HYWduZFVna0ZUS0Vzcjk0NElUd1R1SDFha25IZDdOdC1QTjhna2thRU5ubzNHd1M2Z0RfWXJPMjJLYjdmSFNMcGZhcDhVZXN0TUxpOHRiY081LXpjUF9rTjBuNnFiLWhXYmRDeGh4NlFEQ2cybUVIcWJGcXB6c3ZmdE5SSUEwYXVGQ2Y3QQ?oc=5" target="_blank">From one Dubai café to 13 countries: How AI is powering FiLLi Cafe’s rise</a> <font color="#6f6f6f">Gulf Business</font>
Could not retrieve the full article text.
Read on Google News AI UAE →Sign in to highlight and annotate this article

Conversation starters
Daily AI Digest
Get the top 5 AI stories delivered to your inbox every morning.
Knowledge Map
Connected Articles — Knowledge Graph
This article is connected to other articles through shared AI topics and tags.
More in Laws & Regulation

Diffusion Policy with Bayesian Expert Selection for Active Multi-Target Tracking
arXiv:2604.03404v1 Announce Type: new Abstract: Active multi-target tracking requires a mobile robot to balance exploration for undetected targets with exploitation of uncertain tracked ones. Diffusion policies have emerged as a powerful approach for capturing diverse behavioral strategies by learning action sequences from expert demonstrations. However, existing methods implicitly select among strategies through the denoising process, without uncertainty quantification over which strategy to execute. We formulate expert selection for diffusion policies as an offline contextual bandit problem and propose a Bayesian framework for pessimistic, uncertainty-aware strategy selection. A multi-head Variational Bayesian Last Layer (VBLL) model predicts the expected tracking performance of each exp




Discussion
Sign in to join the discussion
No comments yet — be the first to share your thoughts!