380 citations · 1.6k across the 84 of their papers we have counts for
10 papers · 1 filter
Domain Adaptation Using Adversarial Learning for Autonomous Navigation
Jaeyoon Yoo, Yongjun Hong, YungKyun Noh +1
Autonomous navigation has become an increasingly popular machine learning application. Recent advances in deep learning have also resulted in great improvements to autonomous navig…
Homomorphic Parameter Compression for Distributed Deep Learning Training
Jaehee Jang, Byungook Na, Sungroh Yoon
Distributed training of deep neural networks has received significant research interest, and its major approaches include implementations on multiple GPUs and clusters. Paralleliza…
Quantized Memory-Augmented Neural Networks
Seongsik Park, Seijoon Kim, Seil Lee +2
Memory-augmented neural networks (MANNs) refer to a class of neural network models equipped with external memory (such as neural Turing machines and memory networks). These neural…
How Generative Adversarial Networks and Their Variants Work: An Overview
Yongjun Hong, Uiwon Hwang, Jaeyoon Yoo +1
Generative Adversarial Networks (GAN) have received wide attention in the machine learning field for their potential to learn high-dimensional, complex real data distribution. Spec…
Adversarial Training for Disease Prediction from Electronic Health Records with Missing Data
Uiwon Hwang, Sungwoon Choi, Han-Byoel Lee +1
Electronic health records (EHRs) have contributed to the computerization of patient records and can thus be used not only for efficient and systematic medical services, but also fo…
Polyphonic Music Generation with Sequence Generative Adversarial Networks
Sang-gil Lee, Uiwon Hwang, Seonwoo Min +1
We propose an application of sequence generative adversarial networks (SeqGAN), which are generative adversarial networks for discrete sequence generation, for creating polyphonic…
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-…
DNA Steganalysis Using Deep Recurrent Neural Networks
Ho Bae, Byunghan Lee, Sunyoung Kwon +1
Recent advances in next-generation sequencing technologies have facilitated the use of deoxyribonucleic acid (DNA) as a novel covert channels in steganography. There are various me…
DeepCCI: End-to-end Deep Learning for Chemical-Chemical Interaction Prediction
Sunyoung Kwon, Sungroh Yoon
Chemical-chemical interaction (CCI) plays a key role in predicting candidate drugs, toxicity, therapeutic effects, and biological functions. In various types of chemical analyses,…
Building a Neural Machine Translation System Using Only Synthetic Parallel Data
Jaehong Park, Jongyoon Song, Sungroh Yoon
Recent works have shown that synthetic parallel data automatically generated by translation models can be effective for various neural machine translation (NMT) issues. In this stu…