lowrank.net

Welcome to my homepage

I am a scientist working on CNTK. Microsoft's deep learning toolkit. I finished my PhD in Computer Science. At Cornell University in the summer of 2012. Prior to that, I received my Bachelor and Masters (with honours) from the Department of Informatics and Telecommunications. At the University of Athens, Greece. My research intersts revolve mainly around Machine Learning. Isn't it remarkable that we can build models that solve a hard problem better than any other algorithm we could have written?

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Nikos Karampatziakis

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Welcome to my homepage | lowrank.net Reviews

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I am a scientist working on CNTK. Microsoft's deep learning toolkit. I finished my PhD in Computer Science. At Cornell University in the summer of 2012. Prior to that, I received my Bachelor and Masters (with honours) from the Department of Informatics and Telecommunications. At the University of Athens, Greece. My research intersts revolve mainly around Machine Learning. Isn't it remarkable that we can build models that solve a hard problem better than any other algorithm we could have written?

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1 nikos karampatziakis

2 summary

3 publications

4 software

5 links

6 gnuplot

7 introduction

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Welcome to my homepage | lowrank.net Reviews

https://lowrank.net

I am a scientist working on CNTK. Microsoft's deep learning toolkit. I finished my PhD in Computer Science. At Cornell University in the summer of 2012. Prior to that, I received my Bachelor and Masters (with honours) from the Department of Informatics and Telecommunications. At the University of Athens, Greece. My research intersts revolve mainly around Machine Learning. Isn't it remarkable that we can build models that solve a hard problem better than any other algorithm we could have written?

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lowrank.net

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Publications

http://lowrank.net/nikos/pubs.html

P Mineiro and N. Karampatziakis " Fast Label Embeddings via Randomized Linear Algebra. In Proceedings of the 25th European Conference on Machine Learning, ECML, 2015. A Agarwal, S. M. Kakade, N. Karampatziakis, L. Song, and G Valiant " Least Squares Revisited: Scalable Approaches for Multi-class Prediction. In Proceedings of the 31st International Conference on Machine Learning, ICML, 2014. N Karampatziakis and P. Mineiro " Discriminative Features via Generalized Eigenvectors. In Uncertainty in Artificia...

2

Software

http://lowrank.net/nikos/software.html

Is a SVM for identifying blocks of code in binary executables. Is a decision tree package that can handle high dimensional sparse data. Efficiently trains neural networks from high dimensional sparse data. More Software Coming Soon.

3

Links

http://lowrank.net/nikos/gnuplot.html

I have resurrected Toshihiko Kawano's awesome gnuplot website.

4

Links

http://lowrank.net/nikos/links.html

John Langford's Machine Learning (Theory). Andrew Gelman's Statistical Modeling, Causal Inference, and Social Science. Fernando Pereira's Earning My Turns. The Journal of Machine Learning Gossip.

5

gnuplot tips (not so Frequently Asked Questions)

http://lowrank.net/gnuplot/index-e.html

Not so Frequently Asked Questions -. Use for Technical Paper Submission. Use for WWW Access Log. Basic Use - from start to the end. Use for a Numerical Calculation - Plots of Calculated Results. Use for Experimental Data - Plots of Experimental Data. Plot Functions and the Least-Squares fitting to experimental data. Where can I download the current version of gnuplot? Where can I get FAQ of gnuplot? What can I do if I find some mistakes in this document? Do I have an archive of this web-site? I want to g...

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machinedlearnings.com

Machined Learnings: November 2014

http://www.machinedlearnings.com/2014_11_01_archive.html

Cue Butlerian Jihad in 3, 2, 1, . Sunday, November 16, 2014. There's plenty of unlabeled data, so lately I've been spending more time with unsupervised methods. Nikos. And I have spent some time with CCA. Which is akin to SVD but assumes a bit of structure on the data. In particular, it assumes there are two (or more) views of the data, where a view is basically a set of features. A generative interpretation. Mathrm{subject to} ;& mathbf{X} a top mathbf{A} top mathbf{A} mathbf{X} a = n mathbf{I},. Basica...

machinedlearnings.com

Machined Learnings: February 2014

http://www.machinedlearnings.com/2014_02_01_archive.html

Cue Butlerian Jihad in 3, 2, 1, . Friday, February 21, 2014. Stranger in a Strange Land. I attended the SIAM PP 2014. The Data Must Flow. One of the first things I heard at the conference was that “map-reduce ignores data locality”. The speaker, Steve Plimpton, clearly understood map-reduce, having implemented MapReduce for MPI. This was a big clue that they mean something very different by data locality (i.e., they do not mean “move the code to the data”). Moderately distorted, is a good match for what ...

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lowrank.net

Welcome to my homepage

I am a scientist working on CNTK. Microsoft's deep learning toolkit. I finished my PhD in Computer Science. At Cornell University in the summer of 2012. Prior to that, I received my Bachelor and Masters (with honours) from the Department of Informatics and Telecommunications. At the University of Athens, Greece. My research intersts revolve mainly around Machine Learning. Isn't it remarkable that we can build models that solve a hard problem better than any other algorithm we could have written?

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