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Title: Machine Learning/Speech research internship @ Seattle, WA
Time: Summer/Fall 2019
About You
A wiliness to be a better you on a day by day basis.
About Us:
We enable hundreds of millions of commercial and social interactions among our users, between consumers and merchants, and among businesses every day though speech.
What You’ll Do
Section: Events
November 3-6, 2019
Location: Pacific Grove, CA, USA
Section: Events
November 18-21, 2019
Location: Lanzhou, China
Postdoctoral Research Grant (BPD) for the R&D Integrated Program “C4 – Centro de Competências em Cloud Computing”
Reference: “C4_MP_D1-b (En)”
Section: Events
Lecture Date: April 2, 2019
Chapter: SPS Western Australia Chapter
Chapter Chair: Sven Nordholm
Topic: Hyperspectral Unmixing: Insights and Beyond
Issue Date:
Section: Community & Involvement
In recent years the ubiquity of mobile computing platforms such as smartphones and tablet devices has rapidly increased. These devices provide a range of interaction in an untethered environment unimaginable a decade previously. With this ability to interact with services and individuals, comes the need to accurately authenticate the identity of the person requesting the transaction, many of which may carry financial or legally-binding instruction.
Section: Events
Publication: IEEE Signal Processing Magazine
White Paper Due: June 1, 2019
Publication Date: July 2020
CFP Document
Section: Events
Lecture Date: May 17, 2016
Chapter: Turkey
Chapter Chair: Suleyman Serdar Kozat
Topic: Learning Sparsifying Transforms for Signal, Image, and Video Processing
Section: Publications & Resources
Publication: IEEE Transactions on Image Processing
The IEEE Transactions on Image Processing covers novel theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications.
Section: Publications & Resources
Publication: IEEE Transactions on Image Processing
Zero-shot learning (ZSL) for visual recognition aims to accurately recognize the objects of unseen classes through mapping the visual feature to an embedding space spanned by class semantic information. However, the semantic gap across visual features and their underlying semantics is still a big obstacle in ZSL. Conventional ZSL methods construct that the mapping typically focus on the original visual features that are independent of the ZSL tasks, thus degrading the prediction performance.
