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Deep Learning: Present, Challenges and Future

March 27, 2015

On Monday, March 30th and Tuesday, March 31,
we are visited by Dr. Kyunghyun Cho,
a deep learning scientist from the University of Montreal, LISA lab


Dr. Cho will be giving two talks at UNM:

When: Monday March 30th at 4 PM
Where: PIBBS
PIBBS Suite, Castetter 1424

When: Tuesday March 31st at 2:00 pm
Where: The Mind Research Network, main conference room.
1101 Yale Blvd NE, Albuquerque, NM 87131 (Map)

Students and faculty are invited to either talk, but space is limited on Monday. If you plan on coming Monday, please contact me at erroneus@gmail.com.

Contact:
Devon Hjelm
erroneus@gmail.com

Title: Deep Learning: Present, Challenges and Future

Abstract:

Deep learning has become increasingly popular due to its success in challenging machine learning tasks such as large-scale object recognition, speech recognition and machine translation. In deep learning, we aim to build general computational models for data and tasks that exhibit rich, complex underlying structures, such as vision, speech and human languages. In this talk, I will give a brief overview of the principles behind deep learning and present in detail my latest research on neural machine translation and image/video description generation. I will conclude the talk by discussing the future of
deep learning research, which I believe will let us gain insight into highly complicated natural phenomena.

Bio:

Kyunghyun Cho is a postdoctoral researcher at the University of Montreal (Canda) under the supervision of Prof. Yoshua Bengio since early 2014. He has received B.Sc. in computer science from KAIST (Korea) in 2009. He continued his study at Aalto University (Finland) and received Ph.D. degree in 2014. His main research interest includes neural networks, generative models and their applications, especially, to language understanding.