hunch_net hunch_net-2005 hunch_net-2005-124 knowledge-graph by maker-knowledge-mining
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Introduction: We are planning to have a workshop on atomic learning Jan 7 & 8 at TTI-Chicago. Details are here . The earlier request for interest is here . The primary deadline is abstracts due Nov. 20 to jl@tti-c.org.
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same-blog 1 1.0 124 hunch net-2005-10-19-Workshop: Atomic Learning
Introduction: We are planning to have a workshop on atomic learning Jan 7 & 8 at TTI-Chicago. Details are here . The earlier request for interest is here . The primary deadline is abstracts due Nov. 20 to jl@tti-c.org.
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Introduction: Many Machine Learning related events are coming up this fall. September 9 , abstracts for the New York Machine Learning Symposium are due. Send a 2 page pdf, if interested, and note that we: widened submissions to be from anybody rather than students. set aside a larger fraction of time for contributed submissions. September 15 , there is a machine learning meetup , where I’ll be discussing terascale learning at AOL. September 16 , there is a CS&Econ; day at New York Academy of Sciences. This is not ML focused, but it’s easy to imagine interest. September 23 and later NIPS workshop submissions start coming due. As usual, there are too many good ones, so I won’t be able to attend all those that interest me. I do hope some workshop makers consider ICML this coming summer, as we are increasing to a 2 day format for you. Here are a few that interest me: Big Learning is about dealing with lots of data. Abstracts are due September 30 . The Bayes
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Introduction: The workshop on the Meaningful Use of Complex Medical Data is happening again, August 9-12 in LA, near UAI on Catalina Island August 15-17. I enjoyed my visit last year, and expect this year to be interesting also. The first Bay Area Machine Learning Symposium is August 30 at Google . Abstracts are due July 30.
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Introduction: We are planning to have a workshop on atomic learning Jan 7 & 8 at TTI-Chicago. Details are here . The earlier request for interest is here . The primary deadline is abstracts due Nov. 20 to jl@tti-c.org.
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Introduction: One prescription for solving a problem well is: State the problem, in the simplest way possible. In particular, this statement should involve no contamination with or anticipation of the solution. Think about solutions to the stated problem. Stating a problem in a succinct and crisp manner tends to invite a simple elegant solution. When a problem can not be stated succinctly, we wonder if the problem is even understood. (And when a problem is not understood, we wonder if a solution can be meaningful.) Reinforcement learning does step (1) well. It provides a clean simple language to state general AI problems. In reinforcement learning there is a set of actions A , a set of observations O , and a reward r . The reinforcement learning problem, in general, is defined by a conditional measure D( o, r | (o,r,a) * ) which produces an observation o and a reward r given a history (o,r,a) * . The goal in reinforcement learning is to find a policy pi:(o,r,a) * -> a
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