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Machine Learning, etc

Machine Learning, etc. Tuesday, May 12, 2015. Some ICLR posters that caught my eye:. Similar to Yann LeCun's "Siamese Network" but potentially makes learning problem easier by looking at 2 pairs of examples at each step - two examples of same class two examples of different class. The loss models these pair distances - first distance must be smaller. Biased here because I've looked at fonts before (ie, see notMNIST. They get better results by doing SGD where examples are sampled using non-uniform strateg...

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Machine Learning, etc | yaroslavvb.blogspot.com Reviews
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Machine Learning, etc. Tuesday, May 12, 2015. Some ICLR posters that caught my eye:. Similar to Yann LeCun's Siamese Network but potentially makes learning problem easier by looking at 2 pairs of examples at each step - two examples of same class two examples of different class. The loss models these pair distances - first distance must be smaller. Biased here because I've looked at fonts before (ie, see notMNIST. They get better results by doing SGD where examples are sampled using non-uniform strateg...
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3 text{newparams}=g { 1} text{labels} $
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Machine Learning, etc | yaroslavvb.blogspot.com Reviews

https://yaroslavvb.blogspot.com

Machine Learning, etc. Tuesday, May 12, 2015. Some ICLR posters that caught my eye:. Similar to Yann LeCun's "Siamese Network" but potentially makes learning problem easier by looking at 2 pairs of examples at each step - two examples of same class two examples of different class. The loss models these pair distances - first distance must be smaller. Biased here because I've looked at fonts before (ie, see notMNIST. They get better results by doing SGD where examples are sampled using non-uniform strateg...

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Dispatches from the front-lines of AI Research. Tuesday, February 08, 2005. Google is your friend. After a long hiatus, I'm going to start blogging again with this. Paper on "Automatic Meaning Discovery using Google" by Paul Vitanyi. Guy) and Rudi Cilibrasi. Which has generated a flurry of interest, even sparking a slashdot. Versus "hoarse" "rider"( 31,400. For example which is building a hand-crafted knowledge base. Secondly they have impressive experimental results, especially one involving the heirarc...

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Dispatches from the front-lines of AI Research. Saturday, December 04, 2004. A Support Vector Machine. Shivani Agarwal has a very good introductory presentation. Training an SVM requires the solution of a very large quadratic programming. Problem (finding lagrange multipliers. SMO is competitive with other SVM training methods such as Projected Conjugate Gradient "chunking" and in addition is easier to implement. SMO is the work of John Platt. Finally, there is also an SVM mailing list. AI for Fun and Pr...

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Dispatches from the front-lines of AI Research. Sunday, October 24, 2004. Scientists at the University of Florida have successfully conducted a very intriguing experiment. We're just starting out. But using this model will help us understand the crucial bit of information between inputs and the stuff that comes out . And you can imagine the more you learn about that, the more you can harness the computation of these neurons into a wide range of applications.". Best of luck guys, you'll need it. If you ge...

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Dispatches from the front-lines of AI Research. Tuesday, October 12, 2004. Livingstone Version 2, I presume. Pointed me to this. Article about NASA successfully uploading a diagnostic AI software called Livingstone Version 2 onto a satellite. They're using it to simulate and identify faults in its robotic systems and plan alternate courses of action when failures occur. Like most news articles, this one is light on technical details. At AAAI'04 on AI and NASA's New Exploration Vision. But there already i...

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I am a developer and security researcher deeply interested in Big Data/cloud computing and machine Learning. Peekaboo: Andy’s Computer Vision and Machine Learning Blog. Machine Learning, etc. 2014 Jason Trost. Powered by Jekyll. Using the HPSTR Theme.

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http://www.johndcook.com/blog/2010/11/29/where-to-wait-for-an-elevator

John D. Cook. Where to wait for an elevator. Imagine a bank of three elevators along a wall. The elevators are in a straight line but they are not evenly spaced. Where do you stand in order to minimize the expected distance you’ll need to walk to catch the first elevator that arrives? This scenario comes from the opening paragraph of a paper. Distance and that the minimal average distance to the elevator is in fact achieved by standing at the median. This problem illustrates three optimization theorems:.

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Insights by Danielle Fong. Notes from a girl from the future. Acceptably Nonterrible Revision Control for Mathematica. I discovered today, to my amazement and horror, that the otherwise exceptionally advanced and useful Mathematica 7 has only a single level of undo in its notebooks. And no redo. Furthermore, the lone autosave feature, hidden deep, is triggered upon every cell evaluation. Every time you evaluate something in a notebook, it will save the file – overwriting the last version. 3 Now every tim...

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http://robustmathematicalmodeling.blogspot.com/2011/11/how-biased-are-maximum-entropy-models.html

The Robust Mathematical Modeling Blog. When modeling Reality is not an option. Sunday, November 27, 2011. How biased are maximum entropy models? This is of interest to the Experimental Probabilistic Hypersurface. Approach which computes the probability distribution.that maximizes entropy. For difficult to compute models (read too long to run on a computer). Here is the paper: How biased are maximum entropy models? By Jakob H. Macke,. Peter E. Latham. The abstract reads:. Subscribe to: Post Comments (Atom).

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Machine Learning, etc

Machine Learning, etc. Tuesday, May 12, 2015. Some ICLR posters that caught my eye:. Similar to Yann LeCun's "Siamese Network" but potentially makes learning problem easier by looking at 2 pairs of examples at each step - two examples of same class two examples of different class. The loss models these pair distances - first distance must be smaller. Biased here because I've looked at fonts before (ie, see notMNIST. They get better results by doing SGD where examples are sampled using non-uniform strateg...

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