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iFLYTEK Laboratory for Neural Computing for Machine Learning (iNCML)

Welcome to the homepage of iFLYTEK Laboratory for Neural Computing and Machine Learning (iNCML). This lab supports research in areas of neural computing models and algorithms for machine learning, with applications to speech recognition and understanding, natural language processing, image/video recognition.

iNCML



iFLYTEK Laboratory for Neural Computing
and Machine Learning (iNCML)

Lassonde 2054, Department of Electrical
Engineering and Computer Science
York University, 4700 Keele Street,
Toronto, Ontario CANADA



Research Aims of the Lab

  1. Explore new neural computing models for machine learning: we explore novel neural computing models and algorithms, which are powerful enough to tackle complex real-world artificial intelligent tasks and meanwhile efficient enough to make good use of big-data.
  2. Investigate neural representations of knowledge for artificial cognition: we investigate a new research area to represent world knowledge (including common sense, common knowledge and domain-specific information) as distributed representations in continuous semantic spaces to achieve advanced cognition, such as teaching machines to think like humans.
  3. Advance machine intelligence in speech recognition and understanding, natural language processing and computer vision: we focus on three main application areas: a) human-machine dialogue systems via speech or text, such as personal assistant agent in smart phones. b) deep natural language processing and understanding, such as automatic machine question & answer (Q&A) systems in general domains or special fields (medical, health, legal, etc). c) Image and video scene analysis, such as autonomous robot navigation and controlling.
Last modified:
2016/01/21 20:13