PPT-Unsupervised Domain Adaptation: From Practice to Theory

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 . In this context our method seeks a domain adaptation solution by learning a mapping function which aligns the source sub space with the target one We show that the solution of the corresponding optimization problem can be obtained in a simple closed 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. ShaSha. . Xie. * Lei Chen. Microsoft ETS. 6/13/2013. Model Adaptation, Key to ASR Success. http://youtu.be/5FFRoYhTJQQ. Adaptation. Modern ASR systems are statistics-rich. Acoustic model (AM) uses GMM or DNN. 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:. Alexander Fraser. CIS, LMU Munich. 2017-10-24 . WP1: Structured Prediction and Domain Adaptation. Outline. Introduction to structured prediction and domain adaptation. Review of very basic structured prediction. Amy Lampen. Laura Braun. Ashley Borowiak. Roy's Adaptation Model focuses on a person's . coping (adaptive) abilities. in response to a constantly changing . environment. (Lopes, Pagliuca, & Araujo, 2006).. ShaSha. . Xie. * Lei Chen. Microsoft ETS. 6/13/2013. Model Adaptation, Key to ASR Success. http://youtu.be/5FFRoYhTJQQ. Adaptation. Modern ASR systems are statistics-rich. Acoustic model (AM) uses GMM or DNN. (* 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. . EEE 161. Intro to Transmission . Lines. WEEK 2. EEE 161. Phasor transformation. EEE 161 Week 2, Lecture 1, Video 1. Objectives. Students will be able to explain . why. is the relationship between sinusoidal signals and phasors valid in the previous table. with Quasi-Synchronous Grammar Features. David A. Smith (UMass Amherst). Jason Eisner (Johns Hopkins). 1. This Talk in a Nutshell. 2. in. the. beginning. im. Anfang. Parser projection. Unsupervised. Supervised.