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This shows you the differences between two versions of the page.

--- start [2015/10/01 15:14] – hj
+++ start [2021/05/28 19:05] (current) – hj
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-====== iFLYTEK Laboratory for Neural Computing for Machine Learning (iNCML) ======
+====== Laboratory for Neural Computing for Machine Learning (NCML) ======
-Welcome to the homepage of **iFLYTEK Laboratory for Neural Computing for Machine Learning (iNCML)**.  //This lab supports research in areas of neural computing models and methods for machine learning, with their applications to speech recognition and understanding, natural language processing, image/video recognition.// \\
+Welcome to the homepage of ** Laboratory for Neural Computing and Machine Learning (NCML)**.  //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-signage.jpg?0x200 |iNCML}}
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-\\
-**iFLYTEK Laboratory for Neural Computing \\
-for Machine Learning (iNCML)** \\
-Lassonde 2054, Department of Electrical \\
-Engineering and Computer Science \\
-York University, 4700 Keele Street, \\
-Toronto, Ontario  CANADA
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 ===== Research Aims of the Lab =====
-  - **Explore new neural computing models for machine learning:** we explore new neural computing models and algorithms to take advantage of big-data and advanced computing resources for complex artificial intelligent tasks.
+  - **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.
-  - **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 and cognitive tasks, namely //teaching machines to think like humans//.
+  - **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//.
-  - **Advance machine intelligence in speech recognition and understanding, natural language processing and computer vision:** we focus on three main application areas: i) //human-machine dialogue systems// via speech or text, such as personal assistant agent in smart phones. ii) //Deep natural language processing and understanding//, such as automatic machine Question and Answer systems in general domains or special fields (medical, health, legal, etc). iii) //Image and video scene analysis//, such as autonomous robot navigation and controlling.
+  - **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.