PPT-Introduction to Structured Prediction and Domain Adaptation

Alexander Fraser CIS LMU Munich 20171024 WP1 Structured Prediction and Domain Adaptation Outline Introduction to structured prediction and domain adaptation Review of very basic structured prediction. John Blitzer. Shai Ben-David, Koby Crammer, Mark Dredze, Ryan McDonald, Fernando Pereira. Joint work with. Statistical models, multiple domains. Different Domains of Text. Huge variation in vocabulary & style. Generation and Adaptation. Some Notes. Each topic studied so far have a number of fielded applications. That is, they have been used in the “real world”. The topic of this lecture still has some outstanding research questions that need to be answered before we see large numbers of fielded applications . In-domain vs out-domain. Annotated data in. Domain A. A. Parser. Training. Parsing texts in . Domain A. Parsing texts in Domain B . In-domain. Out-domain. Motivation. F. ew or no labeled resources exist for parsing text of the target domain.. John Blitzer. Shai Ben-David, Koby Crammer, Mark Dredze, Ryan McDonald, Fernando Pereira. Joint work with. Statistical models, multiple domains. Different Domains of Text. Huge variation in vocabulary & style. Heterogeneous Face Recognition (HFR). Presenter: Yao-Hung. . Tsai. . . . . Dept.. . of. . Electrical. . Engineering,. . NTU. Oral Presentation:. 2014.05.02. Outline. Face Recognition. Introductions . Name. Department/Program. If research, what are you working on.. Your favorite fruit.. How do you estimate P(. y|x. ) . Types of Learning. Supervised Learning. Unsupervised Learning. Semi-supervised Learning. 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. Steve Branson . Oscar . Beijbom. . Serge . Belongie. CVPR 2013, Portland, Oregon. . UC San Diego. . UC San Diego. . Caltech. Overview. Structured prediction . Learning from larger datasets. Steve Branson . Oscar . Beijbom. . Serge . Belongie. CVPR 2013, Portland, Oregon. . UC San Diego. . UC San Diego. . Caltech. Overview. Structured prediction . Learning from larger datasets. John Blitzer and Hal . Daumé. III. TexPoint. fonts used in EMF. . Read the . TexPoint. manual before you delete this box.: . A. A. A. A. A. A. A. A. A. Classical “Single-domain” Learning. Predict:. John Blitzer. TexPoint. fonts used in EMF. . Read the . TexPoint. manual before you delete this box.: . A. A. A. A. A. A. A. A. A. .. .. .. .. .. .. ?. ?. ?. Unsupervised Domain Adaptation. Running with . Tarun . Mangla. T. , . Nawanol . Theera-Amprnount. *, . Mostafa . Ammar. T. ,. Ellen . Zegura. T. , . Saurabh. . Bagchi. *. 1. Effect . of Bandwidth Prediction Quality on Adaptive . Streaming in . Mobile Environments. - 2 - Abstract Background Accurate identification of protein domain boundaries is useful for protein structure determination and prediction. However, predicting protein domain boundaries from a sequ (* indicates equal contribution). Hao He*. Dina . Katabi. Hao . Wang. *. ICML 2020 Oral. Domain Adaptation. One to One. Source Domain. Target Domain. and. .  .  . Many to One. Single Target Domain.