Rapid Neural Architecture Search by Learning to Generate Graphs from Datasets

ICLR 2021 Rapid Neural Architecture Search by Learning to Generate Graphs from Datasets Hayeon Lee, Eunyoung Hyung and Sung Ju Hwang  Despite the success of recent Neural Architecture Search (NAS) methods on various tasks which have shown to output networks that largely outperfo...

Meta-GMVAE: Mixture of Gaussian VAE for Unsupervised Meta-Learning

ICLR 2021 Meta-GMVAE: Mixture of Gaussian VAE for Unsupervised Meta-Learning Dong Bok Lee, Dongchan Min, Seanie Lee and Sung Ju Hwang  Unsupervised learning aims to learn meaningful representations from unlabeled data which can captures its intrinsic structure, that can be transf...

Learning to Sample with Local and Global Contexts in Experience Replay Buffers

ICLR 2021 Learning to Sample with Local and Global Contexts in Experience Replay Buffers Youngmin Oh, Kimin Lee, Jinwoo Shin, Eunho Yang, Sung Ju Hwang  Experience replay, which enables the agents to remember and reuse experience from the past, has played a significant role in th...

FedMix: Approximation of Mixup under Mean Augmented Federated Learning

ICLR 2021 FedMix: Approximation of Mixup under Mean Augmented Federated Learning Tehrim Yoon, Sumin Shin, Sung Ju Hwang and Eunho Yang  Federated learning (FL) allows edge devices to collectively learn a model without directly sharing data within each device, thus preserving priv...

Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning

ICLR 2021 (formerly ICML 2020 Workshop - Best Student Paper Award) Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning Wonyong Jeong, Jaehong Yoon, Eunho Yang and Sung Ju Hwang  While existing federated learning approaches mostl...

Diversity Matters When Learning From Ensembles

ICLR 2021 Diversity Matters When Learning From Ensembles  Giung Nam, Jongmin Yoon, Yoonho Lee, Juho Lee   Deep ensembles excel in large-scale image classification tasks both in terms of prediction accuracy and calibration. Despite being simple to train, the computa...

Contrastive Learning with Adversarial Perturbations for Conditional Text Generation

ICLR 2021 Contrastive Learning with Adversarial Perturbations for Conditional Text Generation Seanie Lee, Dong Bok Lee and Sung Ju Hwang  Recently, sequence-to-sequence (seq2seq) models with the Transformer architecture have achieved remarkable performance on various conditional ...

Accurate Learning of Graph Representations with Graph Multiset Pooling

ICLR 2021 Accurate Learning of Graph Representations with Graph Multiset Pooling Jinheon Baek, Minki Kang and Sung Ju Hwang  Message-passing graph neural networks have been widely used on modeling graph data, achieving impressive results on a number of graph classification and li...

Why Not to Use Zero Imputation? Correcting Sparsity Bias in Training Neural Networks

ICLR 2020 Why Not to Use Zero Imputation? Correcting Sparsity Bias in Training Neural Networks Joonyoung Yi, Juhyuk Lee, Kwang Joon Kim, Sung Ju Hwang, Eunho Yang  Handling missing data is one of the most fundamental problems in machine learning. Among many approaches, ...

Scalable and Order-robust Continual Learning with Additive Parameter Decomposition

ICLR 2020 Scalable and Order-robust Continual Learning with Additive Parameter Decomposition Jaehong Yoon, Saehoon Kim, Eunho Yang, Sung Ju Hwang  While recent continual learning methods largely alleviate the catastrophic problem on toy-sized datasets, there are issues that ...