3 citations · 7 across the 5 of their papers we have counts for
5 papers
Disentangling Options with Hellinger Distance Regularizer
Minsung Hyun, Junyoung Choi, Nojun Kwak
In reinforcement learning (RL), temporal abstraction still remains as an important and unsolved problem. The options framework provided clues to temporal abstraction in the RL, and…
Task-oriented Design through Deep Reinforcement Learning
Junyoung Choi, Minsung Hyun, Nojun Kwak
We propose a new low-cost machine-learning-based methodology which assists designers in reducing the gap between the problem and the solution in the design process. Our work applie…
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…
3D Human Pose Estimation Using Convolutional Neural Networks with 2D Pose Information
Sungheon Park, Jihye Hwang, Nojun Kwak
While there has been a success in 2D human pose estimation with convolutional neural networks (CNNs), 3D human pose estimation has not been thoroughly studied. In this paper, we ta…