factorgrad.blogspot.com

The Grad Factor

Friday, July 27, 2012. Here are my high lights from NIPS 2011 in. How biased are maximum entropy models? Jakob H. Macke, Iain Murray, Peter E. Latham. They show that some of the common approaches to maximum entropy learning (subject to constraints in the data like moments) can severely under-estimate the entropy of the data. One might naively assume max-ent over-estimates the entropy of the data. Iain calls his paper a "health warning" for methodology he says he sees many neuroscientists use. Then you ca...

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The Grad Factor | factorgrad.blogspot.com Reviews

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Friday, July 27, 2012. Here are my high lights from NIPS 2011 in. How biased are maximum entropy models? Jakob H. Macke, Iain Murray, Peter E. Latham. They show that some of the common approaches to maximum entropy learning (subject to constraints in the data like moments) can severely under-estimate the entropy of the data. One might naively assume max-ent over-estimates the entropy of the data. Iain calls his paper a health warning for methodology he says he sees many neuroscientists use. Then you ca...

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The Grad Factor | factorgrad.blogspot.com Reviews

https://factorgrad.blogspot.com

Friday, July 27, 2012. Here are my high lights from NIPS 2011 in. How biased are maximum entropy models? Jakob H. Macke, Iain Murray, Peter E. Latham. They show that some of the common approaches to maximum entropy learning (subject to constraints in the data like moments) can severely under-estimate the entropy of the data. One might naively assume max-ent over-estimates the entropy of the data. Iain calls his paper a "health warning" for methodology he says he sees many neuroscientists use. Then you ca...

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factorgrad.blogspot.com

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The Grad Factor: CBMS highlights

http://factorgrad.blogspot.com/2010/09/cbms-highlights.html

Monday, September 20, 2010. Here are my highlights from CBMS: the non-parametric Bayes conference at UC Santa Cruz. It was organized more like a summer school, however. Michael Jordan gave one lecture where he went through various models various NP Bayes models he has worked with: LDA, IBPs, sticky HMMs, . He didn't get too technical, but tried to give a high level view of many models motivated by applications such as speaker diarization. Wes Johnson gave one lecture giving examples of NP Bayes in biology.

2

The Grad Factor: MLSP 2010 highlights

http://factorgrad.blogspot.com/2010/09/mlsp-highlights.html

Monday, September 20, 2010. Here are my highlights from MLSP. To my knowledge this was the first machine learning conference to occur within the arctic circle 68 N. The conference took place on top of the gondola. The key highlight of the conference was the summer bobsled track from the conference center to the village. The food was mostly raindeer (in various forms) and berries ;). On the technical side:. Kalman Filtering and Smoothing Solutions to Temporal Gaussian Process. A nice and complete post in ...

3

The Grad Factor: July 2010

http://factorgrad.blogspot.com/2010_07_01_archive.html

Sunday, July 4, 2010. Why LaTeX is superior to MS Word. Speed: Anything that requires a mouse and clicking through menus will be slower than one where you can write it out in a few key strokes. This means that writing characters with accents, special symbols, and especially equations will be much faster to write in LaTeX. MS Word (and even worse open office) can get sluggish when editing large documents with lots of equations and figures. Security: stored in plain text. You also know the diff tool will s...

4

The Grad Factor: October 2010

http://factorgrad.blogspot.com/2010_10_01_archive.html

Tuesday, October 19, 2010. Following the well known latex-one-pager. There is now the LaTeX A0. That combines 16 pages of LaTeX summary into poster. Subscribe to: Posts (Atom). View my complete profile.

5

The Grad Factor: Eureka

http://factorgrad.blogspot.com/2010/12/eureka.html

Monday, December 27, 2010. Undirected Grad has a blog post. About symbolic regression using a new peice of software called Eureka. I have tried it out and it is pretty effective at uncovering the latent function from the synthetic experiments a tried, such as y = logistic(x 2 sin(x) . Subscribe to: Post Comments (Atom). View my complete profile.

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The Grad Factor

Friday, July 27, 2012. Here are my high lights from NIPS 2011 in. How biased are maximum entropy models? Jakob H. Macke, Iain Murray, Peter E. Latham. They show that some of the common approaches to maximum entropy learning (subject to constraints in the data like moments) can severely under-estimate the entropy of the data. One might naively assume max-ent over-estimates the entropy of the data. Iain calls his paper a "health warning" for methodology he says he sees many neuroscientists use. Then you ca...

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