dustintran

5 ms·

1.
by dustintran 7y ago
Hello. I'm the person that was linked to in that GitHub issue! I sympathize with the post's frustration. The TF tutorials on the official website are well-written. But they mostly cover basic features, and as a recent Reddit threa
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TensorFlow Distributions(arxiv.org)
3 points by dustintran 9y ago|1 comments
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Machine Learning Videos: A collection of recorded talks(github.com)
2 points by dustintran 10y ago|0 comments
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by dustintran 10y ago
The author doesn't really apply Bayesian inference. He applies Bayes' rule which is a mathematical property.
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by dustintran 11y ago
Note that this should not validate or invalidate his claims, but here are videos of his teaching: http://math1afall2015.blogspot.com
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by dustintran 11y ago
You've voiced my own feelings exactly.
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by dustintran 11y ago
The paper described in the article: http://arxiv.org/pdf/1506.05439v1.pdf
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by dustintran 11y ago
Hi, stan dev here. I think viewing Stan as a better BUGS is helpful but limiting. The syntax is similar, but the class of models Stan fits is far more general. The class of algorithms we have available also goes beyond MCMC, e.g., variation
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by dustintran 11y ago
Most all probabilistic programming languages in fact treat every model as equivalent to an HMM. So certainly inference on them can be done.
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by dustintran 11y ago
The conundrum during all research talks.
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by dustintran 11y ago
To clarify, it has been studied in Zoubin Ghahramani's group [1] (and also more recently in Ryan Adam's group [2]), and it's most widely known through Radford Neal [3] who's won a lot of competitions using the Bayesian a
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by dustintran 11y ago
This is not completely accurate. Deep learning at the moment simply means the use of "deep" architectures in neural networks. Graphical models, standard Bayesian hierarchical models, and the likes all form hierarchies of features
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by dustintran 12y ago
I strongly disagree with not using linear models, at least to build some theory and intuition before continuing with more sophisticated algorithms. What I find to be more egregiously misused when doing machine learning in practice is that e
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by dustintran 12y ago
I'm curious to see when spam bots will eventually use sophisticated language models in order to generate text, aimed to combat a search engine's own machine learning algorithms which detect spam. Barring the actual scenario where
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by dustintran 12y ago
The problem is the more than likely chance that only doing one resample (in bootstrap terminology) may not lead to any clear rejection of statistical significance. That is, it is quite likely that in practice of this "A/A/B&q
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by dustintran 12y ago
This is not a good analogy. Money is the obvious reason one stays at the "lower hill". Thus it is not a question of whether the person can stoop down from the "lower hill" and get to the "higher" one. It's
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by dustintran 12y ago
Yup, it seems k was fixed since the first time these scripts were made for NIPS 2012 (?). Some of the more well-established advances since LDA would also likely help, like HDP.
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by dustintran 12y ago
I imagine more preventative solutions to develop earlier which are more effective, e.g., "treat" citizens who are predicted highly likely to be cause a dangerous outcome in the future. Or in the simple context of computer vision a
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by dustintran 12y ago
You can split screens (and virtually never use the mouse) in Vim. What particular feature are you looking for here?
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by dustintran 12y ago
I'm not sure how useful this would be. Do people willfully drive from work to a coffee shop in order to get work done, and not simply walk to their favorite coffee shop within walking distance? It seems like distance is more the cont
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by dustintran 12y ago
Shameless hijacking and also just not as good..