hips.seas.harvard.edu

Harvard Intelligent Probabilistic Systems

Harvard Intelligent Probabilistic Systems. School of Engineering and Applied Sciences. Harvard School of Engineering and Applied Sciences. Building Intelligent Probabilistic Systems. In the HIPS group, we are interested in building intelligent algorithms. What makes a system intelligent? Discovering Structure with Bayesian Nonparametrics. A perpetual challenge in statistical modelling is trying to find the parsimonious complexity. Point Processes as Primitives of Computation. Friday, December 11th, 2015.

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Harvard Intelligent Probabilistic Systems | hips.seas.harvard.edu Reviews

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Harvard Intelligent Probabilistic Systems. School of Engineering and Applied Sciences. Harvard School of Engineering and Applied Sciences. Building Intelligent Probabilistic Systems. In the HIPS group, we are interested in building intelligent algorithms. What makes a system intelligent? Discovering Structure with Bayesian Nonparametrics. A perpetual challenge in statistical modelling is trying to find the parsimonious complexity. Point Processes as Primitives of Computation. Friday, December 11th, 2015.

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7 recent news

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Harvard Intelligent Probabilistic Systems | hips.seas.harvard.edu Reviews

https://hips.seas.harvard.edu

Harvard Intelligent Probabilistic Systems. School of Engineering and Applied Sciences. Harvard School of Engineering and Applied Sciences. Building Intelligent Probabilistic Systems. In the HIPS group, we are interested in building intelligent algorithms. What makes a system intelligent? Discovering Structure with Bayesian Nonparametrics. A perpetual challenge in statistical modelling is trying to find the parsimonious complexity. Point Processes as Primitives of Computation. Friday, December 11th, 2015.

INTERNAL PAGES

hips.seas.harvard.edu

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Event Calendar | Harvard Intelligent Probabilistic Systems

http://hips.seas.harvard.edu/content/event-calendar

Harvard Intelligent Probabilistic Systems. School of Engineering and Applied Sciences. Harvard School of Engineering and Applied Sciences.

2

External Resources | Harvard Intelligent Probabilistic Systems

http://hips.seas.harvard.edu/content/external-resources

Harvard Intelligent Probabilistic Systems. School of Engineering and Applied Sciences. Harvard School of Engineering and Applied Sciences. The Matrix Reference Manual. Luc Devroye - Non-Uniform Random Number Generation. Trevor Hastie, Robert Tibshirani, and Jerome Friedman - Elements of Statistical Learning. ET Jaynes - Probability Theory: The Logic of Science. David J.C. MacKay - Information Theory, Inference and Learning Algorithms.

3

Publications | Harvard Intelligent Probabilistic Systems

http://hips.seas.harvard.edu/pubs

Harvard Intelligent Probabilistic Systems. School of Engineering and Applied Sciences. Harvard School of Engineering and Applied Sciences. Graph-Sparse LDA: A Topic Model with Structured Sparsity. Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence. Submitted. { arXiv:1410.4510 [stat.ML]. Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE. 104:28. 2016. { PDF. Convolutional Networks on Graphs for Learning Molecular Fingerprints. Proceedings...

4

New Computational Tools for Statistical Inference | Harvard Intelligent Probabilistic Systems

http://hips.seas.harvard.edu/content/new-computational-tools-statistical-inference

Harvard Intelligent Probabilistic Systems. School of Engineering and Applied Sciences. Harvard School of Engineering and Applied Sciences. New Computational Tools for Statistical Inference. Accelerating MCMC via Parallel Predictive Prefetching. Thirtieth Conference on Uncertainty in Artificial Intelligence (UAI). 2014. { arXiv:1403.7265 [stat.ML]. Firefly Monte Carlo: Exact MCMC with Subsets of Data. Training Restricted Boltzmann Machines on Word Observations.

5

Discovering Structure in Data with Bayesian Nonparametrics | Harvard Intelligent Probabilistic Systems

http://hips.seas.harvard.edu/content/discovering-structure-data-bayesian-nonparametrics

Harvard Intelligent Probabilistic Systems. School of Engineering and Applied Sciences. Harvard School of Engineering and Applied Sciences. Discovering Structure in Data with Bayesian Nonparametrics. A perpetual challenge in statistical modelling is trying to find the parsimonious complexity. Graph-Sparse LDA: A Topic Model with Structured Sparsity. Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence. Submitted. { arXiv:1410.4510 [stat.ML]. 2012 { arXiv:0912.4896 [stat.CO]. Journal ...

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ericmajinglong.com

Eric J. Ma – Page 2 – To whom much is given, much is expected. (Luke 12:48)

http://www.ericmajinglong.com/page/2

Eric J. Ma. To whom much is given, much is expected. (Luke 12:48). PyFlatten: A package for flattening nested data structures. Yesterday, I released PyFlatten. To PyPI – it’s a utility that can flatten nested data structures (e.g. list of lists; dictionaries of lists of tuples) into a single 1-by-N vector, while also returning an ‘unflattener’ function that can restore the original data structure from the flattened version. For others to use. The real heroes are David Duvenaud. May 15, 2016. May 14, 2016.

malmaud.com

Links - Jon Malmaud

http://www.malmaud.com/links

Josh Tenenbaum's lab at MIT (cocosci). Ryan Adam's lab at Harvard (HIPS).

beehive.cs.princeton.edu

People |

http://beehive.cs.princeton.edu/people

Events & calendar. Barbara E Engelhardt PhD. Assistant Professor, Department of Computer Science and Center for Statistics and Machine Learning (2014-present). Derek Aguiar, PhD. Postdoctoral Fellow, Princeton University (Sept 2014-present). Derek completed his PhD in Brown University’s Computer Science Department with Sorin Istrail in 2014. His current work focuses on Bayesian nonparametric statistical models for phasing, imputation, and other large-scale genomics data problems. Mark Carty, PhD. Roberta...

github.com

GitHub - JasperSnoek/spearmint: Spearmint is a package to perform Bayesian optimization according to the algorithms outlined in the paper: Practical Bayesian Optimization of Machine Learning Algorithms. Jasper Snoek, Hugo Larochelle and Ryan P. Adams. Ad

https://github.com/JasperSnoek/spearmint

Spearmint is a package to perform Bayesian optimization according to the algorithms outlined in the paper: Practical Bayesian Optimization of Machine Learning Algorithms. Jasper Snoek, Hugo Larochelle and Ryan P. Adams. Advances in Neural Information Processing Systems, 2012. Http:/ people.seas.harvard.edu/ jsnoek/software.html. Use Git or checkout with SVN using the web URL. Feb 12, 2016. 1 variable with 1 dimension fixed. Failed to load latest commit information. Oct 20, 2014. Feb 12, 2016. May 13, 2014.

bmi.emory.edu

Department of Biomedical Informatics (BMI) | Shamim Nemati, PhD

http://www.bmi.emory.edu/Nemati

Center for Lifelong Learning. School of Public Health. Main (Robert W. Woodruff). Michael C. Carlos Museum. H1N1 (Swine) Flu Info. 2009 Campus Security Report. CLINICAL AND TRANSLATIONAL INFORMATICS. Shamim Nemati, PhD. Last Updated: June 14, 2016. Assistant Professor, Department of Biomedical Informatics. Dr Nemati obtained his PhD degree. And the Laboratory for Computational Physiology and Clinical Inference ( CPCI. And a research fellow at the Brigham and Womens Hospital and the Harvard Medical School.

nematilab.info

The Nemati Lab | About the Nemati Lab

http://nematilab.info/people/shamim/index.html

The Nemati Lab at Emory University. Fill out my online form. Email: shamim.nemati / emory, edu. Web: http:/ www.bmi.emory.edu/Nemati. Shamim Nemati, PhD. Dr Nemati obtained his PhD degree. In Electrical Engineering and Computer Science from the Massachusetts Institute of Technology (MIT) in 2013. While at MIT, he was a member of the Laboratory for Computational Physiology( LCP. And the Laboratory for Computational Physiology and Clinical Inference ( CPCI. As a James S. McDonnell Foundation ( JSMF. He has...

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hips.seas.harvard.edu

Harvard Intelligent Probabilistic Systems

Harvard Intelligent Probabilistic Systems. School of Engineering and Applied Sciences. Harvard School of Engineering and Applied Sciences. Building Intelligent Probabilistic Systems. In the HIPS group, we are interested in building intelligent algorithms. What makes a system intelligent? Discovering Structure with Bayesian Nonparametrics. A perpetual challenge in statistical modelling is trying to find the parsimonious complexity. Point Processes as Primitives of Computation. Friday, December 11th, 2015.

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Nízkoenergetické montované a murované domy na klú? Ocenenie od Ministerstva výstavby. Nájomný byty v Novej Vsi. Nedávno sme zahájili výstavbu bytového domu v obci Nová Ves v okrese Veľký Krtíš. Po dokončení v ňom bude celkovo 18 bytov. Plánovaný termín odovzdania bytov nájomníkom je leto 2017. MAXA reality s.r.o. Adresa: HIPS, s.r.o., Š. Moysesa 431/14, 965 01 Žiar nad Hronom, Slovensko. Mobil: 0918 921 750, 0917 726 958, E-mail: hips@hips.sk. 2011 HIPS, s.r.o.

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