2 citations · 6 across the 4 of their papers we have counts for
6 papers
Prototype-based Personalized Pruning
Jangho Kim, Simyung Chang, Sungrack Yun +1
Nowadays, as edge devices such as smartphones become prevalent, there are increasing demands for personalized services. However, traditional personalization methods are not suitabl…
SubSpectral Normalization for Neural Audio Data Processing
Simyung Chang, Hyoungwoo Park, Janghoon Cho +3
Convolutional Neural Networks are widely used in various machine learning domains. In image processing, the features can be obtained by applying 2D convolution to all spatial dimen…
Genetic-Gated Networks for Deep Reinforcement
Simyung Chang, John Yang, Jaeseok Choi +1
We introduce the Genetic-Gated Networks (G2Ns), simple neural networks that combine a gate vector composed of binary genetic genes in the hidden layer(s) of networks. Our method ca…
URNet : User-Resizable Residual Networks with Conditional Gating Module
Sang-ho Lee, Simyung Chang, Nojun Kwak
Convolutional Neural Networks are widely used to process spatial scenes, but their computational cost is fixed and depends on the structure of the network used. There are methods t…
Towards Governing Agent's Efficacy: Action-Conditional -VAE for Deep Transparent Reinforcement Learning
John Yang, Gyujeong Lee, Minsung Hyun +2
We tackle the blackbox issue of deep neural networks in the settings of reinforcement learning (RL) where neural agents learn towards maximizing reward gains in an uncontrollable w…
Sym-parameterized Dynamic Inference for Mixed-Domain Image Translation
Simyung Chang, SeongUk Park, John Yang +1
Recent advances in image-to-image translation have led to some ways to generate multiple domain images through a single network. However, there is still a limit in creating an imag…