Protected: Advances in NMF Part 2: AutoEncoders and Automated Feature Detection

February 6, 2014

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Metric Learning: Some Quantum Statistical Mechanics

November 14, 2013

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system

i made for my buddy Sebass; just a quick review of quantum stat mech In our last post, we presented a formalism for music recommendations called Logistic Markov Embedding (LME).  This  technique uses some mathematics that arises in other modern machine learning methods, such as Maximum Entropy  (MaxEnt) methods, and the Restricted Boltzmann Machines (RMBs) […]

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Music Recommendations and the Logistic Metric Embedding

October 28, 2013

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embedding

In this post, we are going to see  how to build our own music recommender, using the Logistic Metric Embedding (LME) model developed by Joachims (of SVMLight fame). The core idea of this recommender is that people listen to songs in a specific sequence, and that certain songs sound better when they follow other songs. We […]

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A Ruby DSL Design Pattern for Distributed Computing

August 10, 2013

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Cloud Crawler

Frequently in my work in big data and machine learning, I need to run large calculations in parallel.  There are several great tools for this, including Hadoop, StarCluster,  gnu-parallel, etc.   The ruby world lacks comparable tools although ruby has had distributed computing for a long time: http://blog.new-bamboo.co.uk/2012/04/11/the-druby-book-distributed-and-parallel-computing-with-ruby-is-finally-out Having learned ruby while at Aardvark, but […]

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Causality, Correlation, and Brownian Motion

August 1, 2013

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random-force

A recent question on Quora asked if machine learning could learn something from the Black Scholes model of Finance http://www.quora.com/Machine-Learning/Can-we-learn-any-lessons-from-the-Black-Scholes-solution-to-pricing-risk-to-machine-learning-algorithms-for-personalization-recommendation-algorithms I have been curious about this myself, but from a slightly different perspective, which I share here: Introduction There is a deep relation between statistical mechanics (stat mech) and machine learning (ML). From Exponential Families […]

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Causality vs Correlation: Granger Causality

May 27, 2013

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paris-with-dog

One of the most repeated mantra’s of Machine Learning is that “A Causation is not a Correlation!” When faced with this statement, I’m never really sure how to respond.  After all, the entire point of science is to measure correlations and other signals and determine models that explain their cause and can predict future events. […]

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Advances in Convex NMF: Linear Programming

May 6, 2013

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Today I am going to look at a very important advance in one of my favorite Machine Learning algorithms,   NMF (Non-Negative Matrix Factorization)  [1]. NMF is a curious algorithm in that it allows us to do something very natural and seemingly straightforward, namely, to find clusters of data points, and yet, mathematically, it is known to be non-convex […]

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