Microsoft named a Leader in 2026 Gartner® Magic Quadrant™ for Integration Platform as a Service - Microsoft Azure
Microsoft named a Leader in 2026 Gartner® Magic Quadrant™ for Integration Platform as a Service Microsoft Azure
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Approximating Analytically-Intractable Likelihood Densities with Deterministic Arithmetic for Optimal Particle Filtering
arXiv:2512.01023v3 Announce Type: replace-cross Abstract: Particle filtering algorithms have enabled practical solutions to problems in autonomous robotics (self-driving cars, UAVs, warehouse robots), target tracking, and econometrics, with further applications in speech processing and medicine (patient monitoring). Yet, their inherent weakness at representing the likelihood of the observation (which often leads to particle degeneracy) remains unaddressed for real-time resource-constrained systems. Improvements such as the optimal proposal and auxiliary particle filter mitigate this issue under specific circumstances and with increased computational cost. This work presents a new particle filtering method and its implementation, which enables tunably-approximative representation of arbitra
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Common research advice #2: say precisely what you want to say
Written as part of the Inkhaven Residency program . As previously mentioned, research feedback I give to more junior research collaborators tends to fall into one of three categories: Doing quick sanity checks Saying precisely what you want to say Asking why one more time In each case, I think the advice can be taken to an extreme I no longer endorse. Accordingly, I’ve tried to spell out the degree to which you should implement the advice, as well as what “taking it too far” might look like. I talked about doing quick sanity checks in a previous piece . Here, I talk about the second piece of advice: saying precisely what you want to say. Saying precisely what you want to say The second most common feedback is that you should write down precisely what you want to express. One of the most co
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