Understanding Amortized Inference With User Simulations
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Key Takeaways about Amortized Inference With User Simulations
- Talk by Jakob Macke at the One World ABC Seminar on April 29 2021. For more information on the seminar series, see ...
- Machine Learning for Physics and the Physics of Learning 2019 Workshop II: Interpretable Learning in Physical Sciences ...
- Paper (published at ICLR): https://openreview.net/pdf?id=bj0dcKp9t6 Github: https://github.com/goncalab/multifidelity-NPE Across ...
- Video corresponding to the paper "
- Recorded 17 November 2021. Kyle Cranmer of New York University presents "
Detailed Analysis of Amortized Inference With User Simulations
Current large language models and other large-scale neural nets directly fit data, thus learning to imitate its distribution. Support & Resources → Support the show on Patreon: https://www.patreon.com/c/learnbayesstats → Bayesian This video explores
Amortised Likelihood-free
In summary, understanding Amortized Inference With User Simulations gives us a better perspective.