presentations
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presentations [2012/09/27 15:09] – hj | presentations [2012/11/06 20:49] – hj | ||
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* Graphical Models: {{: | * Graphical Models: {{: | ||
- | * Deep Neural Networks: {{: | + | * Deep Neural Networks: {{: |
- | * Feature Dimensionality Reduction: {{: | + | * Feature Dimensionality Reduction: {{: |
- | * Conditional Random Field: {{: | + | * Conditional Random Field: {{: |
- | * Stochastic Gradient Descent: {{: | + | * Stochastic Gradient Descent: {{: |
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+ | Each person makes a presentation (35-40 minutes) on one of the above topics and leaves 5 minutes for Q&A. **The midterm presentation has been scheduled in a single 3-hr time slot from 11am-2pm at __LAS3033__ (not our regular classroom) on Oct 29 (Mon). As a result, the class on Oct 24 is cancelled.** | ||
- | Each person makes a presentation (35-40 minutes) on one of the above topics and leaves 5 minutes for Q&A. | ||
**2. Final Presentation: | **2. Final Presentation: | ||
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+ | * Discriminative Training of HMMs: {{: | ||
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+ | * Bayesian Learning of HMMs: {{: | ||
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+ | * Latent Semantic Analysis for Language Model: | ||
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+ | * Weighted Finite State Transducer Optimization for Speech and Language Processing: | ||
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+ | * SPAM filtering: | ||
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+ | * Auto Summarization: | ||
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+ | * Open-Domain Q&A: {{: |
presentations.txt · Last modified: 2012/11/28 16:28 by hj