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Both @ericmjl and I are firm believers that Probabilistic Programming has a bright and huge future.
I know other people believe the same. @springcoil has said toe me previously that "PP is the new …
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- Abstract (2-3 lines)
Simulation-based inference is a technique that uses normalizing flows, GANs, and variational inference to perform likelihood-free Bayesian machine learning. SBI has applicat…
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I've developed a new Bayesian sampler called [nautilus](https://github.com/johannesulf/nautilus). It's based on combining importance nested sampling with deep learning. This new sampler has been verif…
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There's a culture in ML of authors making their textbooks available online (to supplement the traditional print editions), which is extremely beneficial to students & researchers. The following is a l…
ghost updated
5 years ago
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### Contact Details
N.J.Rahder@tilburguniversity.edu
### Is your content request related to a problem you've encountered during your research process? Please describe.
To construct a more comprehen…
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### Dependency
- Re-evaluate once the tutorial issues are complete.
### Overview
Guides and tutorials to be produced to help new data scientists and analysts with tasks assigned to them at Hack f…
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**Paper**: Local Competition and Stochasticity for Adversarial Robustness in Deep Learning (http://proceedings.mlr.press/v130/panousis21a)
**Venue**: International Conference on Artificial Intellig…
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## Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks (MAML)
- Authors: Chelsea Finn, Pieter Abbeel, Sergey Levine
- Organization: UC Berkeley & OpenAI
- Conference: ICML 2017
- Pap…
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## Description
I'd like to suggest the implementation of implicit reparameterization gradients, as described in the paper [1], for the Gamma distribution: ndarray.sample_gamma and symbol.sample_gamma…
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Here is an excellent talk from ProbAI summer school last year by Yingzhen Li
https://www.youtube.com/watch?v=cRzNWVjnD6I
Notes: http://yingzhenli.net/home/pdf/ProbAI2022_lecture_note.pdf
[[vide…