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Optimization in Markov Random Fields for Learning

Explore optimization techniques in Markov Random Fields for learning with focus on energy minimization and deepest descent methods. Research by Kegan and Dr. Tappen presented. Convergence criteria based on a 5% change in energy.

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Optimization in Markov Random Fields for Learning

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  1. UCF REU Week 10 Lam Tran

  2. Learning in MRF

  3. Learning in MRF 1) X* = arg min C(X) (lowest energy, stop x when less than .5% change) 2) L(X) = (X* - t)^2 3) Deepest Descent for L(X) and update θ(Kegan and Dr. Tappen, NIP) 4) Repeat until it converged

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