Artificial Intelligence & Machine Learning Lab. (AIM) 

(formerly Statistical Learning for Signal Processing Lab.) Artificial Intelligence & Machine Learning Lab. was established under the guidance of Professor Chang D. Yoo in 1999, the year he arrived at KAIST. Using various machine learning theories and novel signal processing techniques, signals such as image, text, speech, audio, video, EEG and financial data are processed for longstanding and emerging applications.  


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 [Lab. Introduction]

AIM Lab. News

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Team KAIST-SLSP participated in Imagenet Large Scale Visual Recognition Challenge 2016 (ILSVRC 2016) and ranked 7th place in object detection part (DET), and 5th pla...
IEIE Workshop 'Theory and Exercise in Deep Learning' successfully took place in Multi-purpose Hall, IT Convergence Building(N1),KAIST, Daejeon, Korea through Apr. 29– 30...
'Machine Learning Tutorial 2016’ successfully took place in Multi-purpose Hall, IT Convergence Building(N1), KAIST, Daejeon, Korea through Mar. 24 – 27,2016. This tutorial...
SLSP has two graduates this February, who have spent memorable times and developed their research capacity for two years. Having made several achievements, MS cand...

Recently Accepted Papers

paper

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Hyunwoo Nam, Chang D. Yoo, "Content Adaptive Video Summarization using Spatio-Temporal Feature", HouJeung Han, Sunghun Kang, Chang D. Yoo, "MULTI-VIEW VISUAL SPEECH RECOGNITION BASED ON MULTI TASK LEARNING", Junyeong Kim, Chang D Yoo, "DEEP PARTIAL PERSON RE-IDENTIFICATION VIA ATTENTION MODEL", International Conference on Image Processing, 2017

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Hyunsin Park, Chang D. Yoo, "Melody extraction and detection through LSTM-RNN with harmonic sum loss", in Proceedings of International Conference on Acoustics, Speech and Signal Processing, New Orleans, USA, 2017

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Mingoo Song, Chang D. Yoo, "Multimodal Representation: Kneser-Ney Smoothing/Skip-gram based Neural Language Model", in Proceedings of International Conference on Image Processing, Phoenix, USA, 2016.

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Haeyong Kang, Chang D. Yoo, and Yongcheon Na, "Maximum Margin Learning of t-SPNs for Cell Classification with Filtered Input", IEEE Journal of Selected Topics in Signal Processing, vol.10, no.1, pp.130-139, February 2016.