Mini-Batch Consistent Slot Set Encoder for Scalable Set Encoding

NeurIPS 2021 Mini-Batch Consistent Slot Set Encoder for Scalable Set Encoding Bruno Andreis, Jeffrey Willette, Juho Lee and Sung Ju Hwang  Most existing set encoding algorithms operate under the assumption that all the elements of the set are accessible during training and infere...

Hardware-Adaptive Efficient Latency Predictor for NAS via Meta-Learning

NeurIPS 2021 Hardware-Adaptive Efficient Latency Predictor for NAS via Meta-Learning Hayeon Lee, Sewoong Lee, Song Chong and Sung Ju Hwang  For deployment, neural architecture search should be hardware-aware, in order to satisfy the device-specific constraints (e.g., memory usage...

Hit and Lead Discovery with Explorative RL and Fragment-based Molecule Generation

NeurIPS 2021 Hit and Lead Discovery with Explorative RL and Fragment-based Molecule Generation Soojung Yang, Doyeong Hwang, Seul Lee, Seongok Ryu and Sung Ju Hwang  Recently, utilizing reinforcement learning (RL) to generate molecules with desired properties has been highlighted ...

Edge Representation Learning with Hypergraphs

NeurIPS 2021 Edge Representation Learning with Hypergraphs Jaehyeong Jo, Jinheon Baek, Seul Lee, Dongki Kim, Minki Kang and Sung Ju Hwang  Graph neural networks have recently achieved remarkable success in representing graph-structured data, with rapid progress in both the node e...

Adaptive Proximal Gradient Methods for Structured Neural Networks

NeurIPS 2021 Adaptive Proximal Gradient Methods for Structured Neural Networks Jihun Yun, Aurelie C. Lozano, Eunho Yang  We consider the training of structured neural networks where the regularizer can be non-smooth and possibly non-convex. While popular machine learning librarie...

Neural Complexity Measures

NeurIPS 2020 Neural Complexity Measures Yoonho Lee, Juho Lee, Sung Ju Hwang, Eunho Yang, Seungjin Choi  While various complexity measures for diverse model classes have been proposed, specifying an appropriate measure capable of predicting and explaining generalization in deep ne...

MetaPerturb: Transferable Regularizer for Heterogeneous Tasks and Architectures

NeurIPS 2020 MetaPerturb: Transferable Regularizer for Heterogeneous Tasks and Architectures Jeongun Ryu, Jaewoong Shin, Hae Beom Lee, Sung Ju Hwang  Regularization and transfer learning are two popular techniques to enhance generalization on unseen data, which is a fun...

Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Prediction

NeurIPS 2020 Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Prediction Jinheon Baek, Dong Bok Lee, Sung Ju Hwang  Many practical graph problems, such as knowledge graph construction and drug-to-drug interaction, require to handle multi-relati...

Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning

NeurIPS 2020 Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning Jaehyung Kim, Youngbum Hur, Sejun Park, Eunho Yang, Sung Ju Hwang, Jinwoo Shin  While semi-supervised learning (SSL) has proven to be a promising way for leveraging unlabeled ...

Bootstrapping Neural Processes

NeurIPS 2020 Bootstrapping Neural Processes Juho Lee, Yoonho Lee, Jungtaek Kim, Eunho Yang, Sung Ju Hwang, Yee Whye Teh  Unlike in the traditional statistical modeling for which a user typically hand-specify a prior, Neural Processes (NPs) implicitly define a broad class of stoch...