PPT-Structured Perceptron

Alice Lai and Shi Zhi Presentation Outline Introduction to Structured Perceptron ILPCRF Model Averaged Perceptron Latent Variable Perceptron Motivation An algorithm to learn weights for structured prediction. Phrases . assignment out today:. Unsupervised learning. Google n-grams data. Non-trivial pipeline. Make sure you allocate time to actually . run . the program. Hadoop. assignment (out . next week). :. 36 . of . 42. Machine Learning. : More ANNs,. Genetic and Evolutionary Computation (GEC). Discussion: . Genetic Programming. William H. Hsu. Department of Computing and Information Sciences, KSU. KSOL course page: . Algorithms for Efficient. Large Margin . Structured Prediction. Ming-Wei Chang . and Scott Wen-tau Yih. Microsoft Research. 1. Motivation. . Many NLP tasks are structured. Parsing, Coreference, Chunking, SRL, Summarization, Machine translation, Entity Linking,…. in Miniature Detector Using a Multilayer . Perceptron. By Adam Levine. Introduction. Detector needs algorithm to reconstruct point of. interaction in . horizontal plane. Geant4 Simulation. Implement Geant4 C++ libraries. Registration. Hw2. is out . Please start working on it as soon as possible. Come to sections with questions. On Thursday (TODAY) we will have two lectures:. Usual one, 12:30-11:45. An additional one, . Outline. Some Sample NLP Task . [Noah Smith]. Structured Prediction For NLP. Structured Prediction Methods. Conditional Random Fields. Structured . Perceptron. Discussion. Motivating Structured-Output Prediction for NLP. conjunctions . the learner is to learn. The number of . conjunctions. : . . log(|C. |) = . n. The elimination algorithm makes . n. . mistakes. Learn from . positive . examples; eliminate active literals. Non-Volatile Main Memory. Qingda Hu*, . Jinglei Ren. , Anirudh Badam, and Thomas Moscibroda. Microsoft Research. *Tsinghua University. Non-volatile memory is coming…. Data storage. 2. Read: ~50ns. conjunctions . the learner is to learn. The number of . conjunctions. : . . log(|C. |) = . n. The elimination algorithm makes . n. . mistakes. Learn from . positive . examples; eliminate active literals. Qingda Hu*, . Jinglei Ren. , Anirudh Badam, and Thomas Moscibroda. Microsoft Research. *Tsinghua University. Non-volatile memory is coming…. Data storage. 2. Read: ~50ns. Write: ~10GB/s. Read: ~10µs. Learning 2. Mike . Mozer. Department of Computer Science and. Institute of Cognitive Science. University of Colorado at Boulder. Review. Two learning rules. Hebbian. learning . regression. Logistic Regression. Mark Hasegawa-Johnson, 2/2022. License: CC-BY 4.0. Outline. One-hot vectors: rewriting the perceptron to look like linear regression. Softmax. : Soft category boundaries. Cross-entropy = negative log probability of the training data. Teacher . Professional Development. Teacher Professional Development. In this . Teacher Professional Development. , you will find practical information on the following:. Overview of Structured Teaching . Linear Classifiers. Mark Hasegawa-Johnson, 3/2020. Including Slides by . Svetlana Lazebnik, 10/2016. License: CC-BY 4.0. Linear Classifiers. Classifiers. Perceptron. Linear classifiers in general. Logistic regression.