PPT-Domain Adaptation

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 Singledomain Learning Predict. 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 . Hal . Daume. III. Domain Adaptation. Document Classifier. Document Classifier. Symbolism. Features/Data. General. Source. Target. Baselines: . SrcOnly. Features. Baselines: . TgtOnly. Features. Baselines: All. 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. Under. : Prof. Amitabha Mukherjee. By. : Narendra Roy. Roll no. : 11451. Group. : 6. Published by. : . Himanshu Bhatt,. Deepali Semwal. Shourya Roy. Introduction. Supervised machine learning classifications assume both training and test data are sampled from same domain or distribution (. NIPS Adaptation Workshop. With thanks to: . Collaborators:. . Ming-Wei . Chang, . Michael Connor, Gourab Kundu, Alla Rozovskaya. Funding. : . NSF, MIAS-DHS, NIH, DARPA, ARL, DoE. Adaptation. without. 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. 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. 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 . 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. 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.