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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Ph.D candidate Janghoon Cho successfully finished his preliminary Ph.D defense. His thesis was "Summarizing Distribution: Submodular Probability Density Cover". ...
AIM lab. started new project called Visual Turing Test(VTT). On Sep. 8. 2017. AIM lab hosted the kick-off workshop of the new project. Workshop was very successful.
Dawit Mureja got second place at KAIST URP competition. Research topic was "Developing new mechanisms to enhance the performance of Neural Turingmachines for one-shot lear...
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...

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.