no code implementations • WS 2019 • Hyungtak Choi, Lohith Ravuru, Tomasz Dryja{\'n}ski, Sunghan Rye, Dong-Hyun Lee, Hojung Lee, Inchul Hwang
This paper describes our submission to the TL;DR challenge.
1 code implementation • ICLR 2019 • Seil Na, Yo Joong Choe, Dong-Hyun Lee, Gunhee Kim
Although deep convolutional networks have achieved improved performance in many natural language tasks, they have been treated as black boxes because they are difficult to interpret.
no code implementations • 11 Jul 2018 • Hosung Park, Dong-Hyun Lee, Minkyu Lim, Yoseb Kang, Juneseok Oh, Ji-Hwan Kim
In this paper, a time delay neural network (TDNN) based acoustic model is proposed to implement a fast-converged acoustic modeling for Korean speech recognition.
no code implementations • 23 Nov 2015 • Dong-Hyun Lee
Recent work (Bengio et al., 2013) has shown howDenoising Auto-Encoders(DAE) become gener-ative models as a density estimator.
no code implementations • 14 Feb 2015 • Yoshua Bengio, Dong-Hyun Lee, Jorg Bornschein, Thomas Mesnard, Zhouhan Lin
Neuroscientists have long criticised deep learning algorithms as incompatible with current knowledge of neurobiology.
1 code implementation • 23 Dec 2014 • Dong-Hyun Lee, Saizheng Zhang, Asja Fischer, Yoshua Bengio
Back-propagation has been the workhorse of recent successes of deep learning but it relies on infinitesimal effects (partial derivatives) in order to perform credit assignment.
11 code implementations • 1 Jul 2013 • Ian J. Goodfellow, Dumitru Erhan, Pierre Luc Carrier, Aaron Courville, Mehdi Mirza, Ben Hamner, Will Cukierski, Yichuan Tang, David Thaler, Dong-Hyun Lee, Yingbo Zhou, Chetan Ramaiah, Fangxiang Feng, Ruifan Li, Xiaojie Wang, Dimitris Athanasakis, John Shawe-Taylor, Maxim Milakov, John Park, Radu Ionescu, Marius Popescu, Cristian Grozea, James Bergstra, Jingjing Xie, Lukasz Romaszko, Bing Xu, Zhang Chuang, Yoshua Bengio
The ICML 2013 Workshop on Challenges in Representation Learning focused on three challenges: the black box learning challenge, the facial expression recognition challenge, and the multimodal learning challenge.
Ranked #12 on Facial Expression Recognition (FER) on FER2013