67 citations · 163 across the 7 of their papers we have counts for
9 papers
Skill-based Meta-Reinforcement Learning
Taewook Nam, Shao-Hua Sun, Karl Pertsch +2
While deep reinforcement learning methods have shown impressive results in robot learning, their sample inefficiency makes the learning of complex, long-horizon behaviors with real…
Cluster-Promoting Quantization with Bit-Drop for Minimizing Network Quantization Loss
Jung Hyun Lee, Jihun Yun, Sung Ju Hwang +1
Network quantization, which aims to reduce the bit-lengths of the network weights and activations, has emerged for their deployments to resource-limited devices. Although recent st…
Rapid Neural Architecture Search by Learning to Generate Graphs from Datasets
Hayeon Lee, Eunyoung Hyung, Sung Ju Hwang
Despite the success of recent Neural Architecture Search (NAS) methods on various tasks which have shown to output networks that largely outperform human-designed networks, convent…
FedMix: Approximation of Mixup under Mean Augmented Federated Learning
Tehrim Yoon, Sumin Shin, Sung Ju Hwang +1
Federated learning (FL) allows edge devices to collectively learn a model without directly sharing data within each device, thus preserving privacy and eliminating the need to stor…
Learning to Perturb Word Embeddings for Out-of-distribution QA
Seanie Lee, Minki Kang, Juho Lee +1
QA models based on pretrained language mod-els have achieved remarkable performance on various benchmark datasets.However, QA models do not generalize well to unseen data that fall…
Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation
Dongchan Min, Dong Bok Lee, Eunho Yang +1
With rapid progress in neural text-to-speech (TTS) models, personalized speech generation is now in high demand for many applications. For practical applicability, a TTS model shou…