PPT-Adversarial and multi-task learning for NLP

Author : helene | Published Date : 2023-11-11

Generative Adversarial Networks GANs Generative Adversarial Networks GANs Goodfellow et al 2014 httpsarxivorgabs14062661 Minimize distance between the distributions

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Adversarial and multi-task learning for NLP: Transcript

Generative Adversarial Networks GANs Generative Adversarial Networks GANs Goodfellow et al 2014 httpsarxivorgabs14062661 Minimize distance between the distributions of real data and generated samples. Subproblems. . Meliha. . Yetisgen-Yildiz. From last week’s discussion. Presentation. Schedule. : . http. ://faculty.washington.edu/melihay/. MEBI591C.htm. 50 . minutes . presentation+discussion+question. Nets. İlke Çuğu 1881739. NIPS 2014 . Ian. . Goodfellow. et al.. At a . glance. (. http://www.kdnuggets.com/2017/01/generative-adversarial-networks-hot-topic-machine-learning.html. ). Idea. . Behind. Transfer Learning. Dog/Cat. Classifier. cat. dog. Data . not directly related to . the task considered. elephant. tiger. Similar domain, different tasks. Different domains, same task. http://weebly110810.weebly.com/396403913129399.html. —An Introduction. Binghui. Wang, Computer Engineering. Supervisor: Neil . Zhenqiang. Gong. 01/13/2017. Outline. Machine Learning (ML) . Adversarial . ML. Attack . Taxonomy. Capability. Adversarial Training . Adversarial examples. Ostrich!. Adversarial examples. Ostrich!. Intriguing properties of neural networks. . Christian . Szegedy. , . Wojciech. . Zaremba. , Ilya . Sutskever. , Joan Bruna, . Dumitru. Presenters: Pooja Harekoppa, Daniel Friedman. Explaining and Harnessing Adversarial Examples. Ian J. . Goodfellow. , Jonathon . Shlens. and Christian . Szegedy. Google Inc., Mountain View, CA. Highlights . ML Reading . Group. Xiao Lin. Jul. 22 2015. I. . Goodfellow. , J. . Pouget-Abadie. , M. Mirza, B. Xu, D. . Warde. -Farley, S. . Ozair. , A. . Courville. and Y. . Bengio. . . "Generative adversarial nets." . for . edge detection. Z. Zeng Y.K. Yu, K.H. Wong. In . IEEE iciev2018, International Conference on Informatics, Electronics & Vision '. June,kitakyushu. exhibition center, japan, 25~29, 2018. (. Jimmy Lin. The . iSchool. University of Maryland. Wednesday, September 2, 2009. NLP. IR. About Me. Teaching Assistant: . Melissa Egan. CLIP. About You (pre-requisites). Must be interested in NLP. Must have strong computational background. Dan Jurafsky. Stanford University. Spring 2020 . Introduction and Course Overview. Thanks to . Tsvetkov. and Black course . for ideas and slides!. How should we use NLP for good and not for bad?. The common misconception is that language has to do with . Dr. Alex Vakanski. Lecture 6. GANs for Adversarial Machine Learning. Lecture Outline. Mohamed Hassan presentation. Introduction to Generative Adversarial Networks (GANs). Jeffrey Wyrick presentation. Dr. Alex Vakanski. Lecture 1. Introduction to Adversarial Machine Learning. . Lecture Outline. Machine Learning (ML). Adversarial ML (AML). Adversarial examples. Attack taxonomy. Common adversarial attacks. Dr. Alex Vakanski. Lecture . 10. AML in . Cybersecurity – Part I:. Malware Detection and Classification. . Lecture Outline. Machine Learning in cybersecurity. Adversarial Machine Learning in cybersecurity. Transfer Learning. Transfer a model trained on . source. data A to . target . data B. Task transfer: . in this case, . the source and target data can be the same. Image classification -> image segmentation.

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