19 citations · 44 across the 13 of their papers we have counts for
17 papers
Towards Defensive Autonomous Driving: Collecting and Probing Driving Demonstrations of Mixed Qualities
Jeongwoo Oh, Gunmin Lee, Jeongeun Park +8
Designing or learning an autonomous driving policy is undoubtedly a challenging task as the policy has to maintain its safety in all corner cases. In order to secure safety in auto…
Optimal Algorithms for Stochastic Multi-Armed Bandits with Heavy Tailed Rewards
Kyungjae Lee, Hongjun Yang, Sungbin Lim +1
In this paper, we consider stochastic multi-armed bandits (MABs) with heavy-tailed rewards, whose -th moment is bounded by a constant for . First, we propose a…
Generative Autoregressive Networks for 3D Dancing Move Synthesis from Music
Hyemin Ahn, Jaehun Kim, Kihyun Kim +1
This paper proposes a framework which is able to generate a sequence of three-dimensional human dance poses for a given music. The proposed framework consists of three components:…
Deep Elastic Networks with Model Selection for Multi-Task Learning
Chanho Ahn, Eunwoo Kim, Songhwai Oh
In this work, we consider the problem of instance-wise dynamic network model selection for multi-task learning. To this end, we propose an efficient approach to exploit a compact b…
Deep Virtual Networks for Memory Efficient Inference of Multiple Tasks
Eunwoo Kim, Chanho Ahn, Philip H. S. Torr +1
Deep networks consume a large amount of memory by their nature. A natural question arises can we reduce that memory requirement whilst maintaining performance. In particular, in th…
Tsallis Reinforcement Learning: A Unified Framework for Maximum Entropy Reinforcement Learning
Kyungjae Lee, Sungyub Kim, Sungbin Lim +2
In this paper, we present a new class of Markov decision processes (MDPs), called Tsallis MDPs, with Tsallis entropy maximization, which generalizes existing maximum entropy reinfo…
Interactive Text2Pickup Network for Natural Language based Human-Robot Collaboration
Hyemin Ahn, Sungjoon Choi, Nuri Kim +2
In this paper, we propose the Interactive Text2Pickup (IT2P) network for human-robot collaboration which enables an effective interaction with a human user despite the ambiguity in…
Maximum Causal Tsallis Entropy Imitation Learning
Kyungjae Lee, Sungjoon Choi, Songhwai Oh
In this paper, we propose a novel maximum causal Tsallis entropy (MCTE) framework for imitation learning which can efficiently learn a sparse multi-modal policy distribution from d…
Learning Instance-Aware Object Detection Using Determinantal Point Processes
Nuri Kim, Donghoon Lee, Songhwai Oh
Recent object detectors find instances while categorizing candidate regions. As each region is evaluated independently, the number of candidate regions from a detector is usually l…
Deep Pose Consensus Networks
Geonho Cha, Minsik Lee, Jungchan Cho +1
In this paper, we address the problem of estimating a 3D human pose from a single image, which is important but difficult to solve due to many reasons, such as self-occlusions, wil…
Generative Single Image Reflection Separation
Donghoon Lee, Ming-Hsuan Yang, Songhwai Oh
Single image reflection separation is an ill-posed problem since two scenes, a transmitted scene and a reflected scene, need to be inferred from a single observation. To make the p…
Text2Action: Generative Adversarial Synthesis from Language to Action
Hyemin Ahn, Timothy Ha, Yunho Choi +2
In this paper, we propose a generative model which learns the relationship between language and human action in order to generate a human action sequence given a sentence describin…
Sparse Markov Decision Processes with Causal Sparse Tsallis Entropy Regularization for Reinforcement Learning
Kyungjae Lee, Sungjoon Choi, Songhwai Oh
In this paper, a sparse Markov decision process (MDP) with novel causal sparse Tsallis entropy regularization is proposed.The proposed policy regularization induces a sparse and mu…
A Nonparametric Motion Flow Model for Human Robot Cooperation
Sungjoon Choi, Kyungjae Lee, H. Andy Park +1
In this paper, we present a novel nonparametric motion flow model that effectively describes a motion trajectory of a human and its application to human robot cooperation. To this…
Uncertainty-Aware Learning from Demonstration using Mixture Density Networks with Sampling-Free Variance Modeling
Sungjoon Choi, Kyungjae Lee, Sungbin Lim +1
In this paper, we propose an uncertainty-aware learning from demonstration method by presenting a novel uncertainty estimation method utilizing a mixture density network appropriat…
Unsupervised Holistic Image Generation from Key Local Patches
Donghoon Lee, Sangdoo Yun, Sungjoon Choi +3
We introduce a new problem of generating an image based on a small number of key local patches without any geometric prior. In this work, key local patches are defined as informati…
Density Matching Reward Learning
Sungjoon Choi, Kyungjae Lee, Andy Park +1
In this paper, we focus on the problem of inferring the underlying reward function of an expert given demonstrations, which is often referred to as inverse reinforcement learning (…