Explore tweets tagged as #probprog
In our newest paper we discuss the frontier of simulation-based inference (aka likelihood-free inference) for a broad audience. We identify three main forces driving the frontier including: #ML, active learning, and integration of autodiff and probprog. https://t.co/R6vMUAnaul
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Happy to hear another PROBPROG conference is being organized (note: not by me!) ( https://t.co/0dU4YdZ2yY). Key dates: January 10th, 2020 - Deadline for Workshop Abstracts. April 23rd-25th, 2020 - PROBPROG 2020 (in Cambridge, MA). Last PROBPROG was excellent.
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Thanks to Kristian for giving a shout out for our research on human in the loop AI at ProbProg.
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Sturat Russell motivating Statistical Relational AI at ProbProg conference at MIT.
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The International Conference on Probabilistic Programming Talks from the #PROBPROG 2018 Conference, held at the MIT Media Lab in Cambridge https://t.co/kstWPLMiQF
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PROBPROG 2021 will feature a keynote from John Winn @johnmwinn, Principal Researcher at Microsoft Research Cambridge, in the Machine Learning and Perception group
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Very compelling talk by @sschoenholz on implementing molecular dynamics with Jax. I think the general strategy of upgrading our simulation to include autodiff (and probprog) will be a major theme of the next 5 years. Those points apply equally well to HEP #NeurIPS2019 #ML4PS2019
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Zoubin Ghahramani at PROBPROG 2018: The Achilles heel of many ML methods is overconfidence; Many large dataset problems are in fact a large collection of small data problems.
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Yordan Zaykov (MSR) at PROBPROG 2018: Effective today, https://t.co/iKXOvtFzKS is Open Source under the MIT license! https://t.co/sVx962PBAb
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Glad to share that we (w Antonio Filieri and Yuan Zhou @yuaanzhou) will be presenting a poster on our work “SYMPAIS: SYMbolic Parallel Adaptive Importance Sampling for Probabilistic Program Analysis” at PROBPROG 2020. https://t.co/uxDMrTMoo7 1/n
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