517 citations · 842 across the 38 of their papers we have counts for
9 papers · 1 filter
Scale Mixtures of Neural Network Gaussian Processes
Hyungi Lee, Eunggu Yun, Hongseok Yang +1
Recent works have revealed that infinitely-wide feed-forward or recurrent neural networks of any architecture correspond to Gaussian processes referred to as Neural Network Gaussia…
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…
Hybrid Generative-Contrastive Representation Learning
Saehoon Kim, Sungwoong Kim, Juho Lee
Unsupervised representation learning has recently received lots of interest due to its powerful generalizability through effectively leveraging large-scale unlabeled data. There ar…
Adversarial purification with Score-based generative models
Jongmin Yoon, Sung Ju Hwang, Juho Lee
While adversarial training is considered as a standard defense method against adversarial attacks for image classifiers, adversarial purification, which purifies attacked images in…
Learning to Pool in Graph Neural Networks for Extrapolation
Jihoon Ko, Taehyung Kwon, Kijung Shin +1
Graph neural networks (GNNs) are one of the most popular approaches to using deep learning on graph-structured data, and they have shown state-of-the-art performances on a variety…
SetVAE: Learning Hierarchical Composition for Generative Modeling of Set-Structured Data
Jinwoo Kim, Jaehoon Yoo, Juho Lee +1
Generative modeling of set-structured data, such as point clouds, requires reasoning over local and global structures at various scales. However, adopting multi-scale frameworks fo…
Posterior distributions for Hierarchical Spike and Slab Indian Buffet processes
Lancelot F. James, Juho Lee, Abhinav Pandey
Bayesian nonparametric hierarchical priors are highly effective in providing flexible models for latent data structures exhibiting sharing of information between and across groups.…
Mini-Batch Consistent Slot Set Encoder for Scalable Set Encoding
Bruno Andreis, Jeffrey Willette, Juho Lee +1
Most existing set encoding algorithms operate under the implicit assumption that all the set elements are accessible, and that there are ample computational and memory resources to…
Improving Uncertainty Calibration via Prior Augmented Data
Jeffrey Willette, Juho Lee, Sung Ju Hwang
Neural networks have proven successful at learning from complex data distributions by acting as universal function approximators. However, they are often overconfident in their pre…