277 citations · 1.6k across the 56 of their papers we have counts for
7 papers · 1 filter
NSML: A Machine Learning Platform That Enables You to Focus on Your Models
Nako Sung, Minkyu Kim, Hyunwoo Jo +8
Machine learning libraries such as TensorFlow and PyTorch simplify model implementation. However, researchers are still required to perform a non-trivial amount of manual tasks suc…
Automatic Music Highlight Extraction using Convolutional Recurrent Attention Networks
Jung-Woo Ha, Adrian Kim, Chanju Kim +2
Music highlights are valuable contents for music services. Most methods focused on low-level signal features. We propose a method for extracting highlights using high-level feature…
Highrisk Prediction from Electronic Medical Records via Deep Attention Networks
You Jin Kim, Yun-Geun Lee, Jeong Whun Kim +3
Predicting highrisk vascular diseases is a significant issue in the medical domain. Most predicting methods predict the prognosis of patients from pathological and radiological mea…
StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
Yunjey Choi, Minje Choi, Munyoung Kim +3
Recent studies have shown remarkable success in image-to-image translation for two domains. However, existing approaches have limited scalability and robustness in handling more th…
Representation Learning of Music Using Artist Labels
Jiyoung Park, Jongpil Lee, Jangyeon Park +2
In music domain, feature learning has been conducted mainly in two ways: unsupervised learning based on sparse representations or supervised learning by semantic labels such as mus…
Energy-Based Sequence GANs for Recommendation and Their Connection to Imitation Learning
Jaeyoon Yoo, Heonseok Ha, Jihun Yi +5
Recommender systems aim to find an accurate and efficient mapping from historic data of user-preferred items to a new item that is to be liked by a user. Towards this goal, energy-…
Overcoming Catastrophic Forgetting by Incremental Moment Matching
Sang-Woo Lee, Jin-Hwa Kim, Jaehyun Jun +2
Catastrophic forgetting is a problem of neural networks that loses the information of the first task after training the second task. Here, we propose a method, i.e. incremental mom…