17 citations · 30 across the 16 of their papers we have counts for
8 papers · 1 filter
Function Space Bayesian Pseudocoreset for Bayesian Neural Networks
Balhae Kim, Hyungi Lee, Juho Lee
A Bayesian pseudocoreset is a compact synthetic dataset summarizing essential information of a large-scale dataset and thus can be used as a proxy dataset for scalable Bayesian inf…
Probabilistic Imputation for Time-series Classification with Missing Data
SeungHyun Kim, Hyunsu Kim, EungGu Yun +3
Multivariate time series data for real-world applications typically contain a significant amount of missing values. The dominant approach for classification with such missing value…
Towards Safe Self-Distillation of Internet-Scale Text-to-Image Diffusion Models
Sanghyun Kim, Seohyeon Jung, Balhae Kim +3
Large-scale image generation models, with impressive quality made possible by the vast amount of data available on the Internet, raise social concerns that these models may generat…
Traversing Between Modes in Function Space for Fast Ensembling
EungGu Yun, Hyungi Lee, Giung Nam +1
Deep ensemble is a simple yet powerful way to improve the performance of deep neural networks. Under this motivation, recent works on mode connectivity have shown that parameters o…
Regularizing Towards Soft Equivariance Under Mixed Symmetries
Hyunsu Kim, Hyungi Lee, Hongseok Yang +1
Datasets often have their intrinsic symmetries, and particular deep-learning models called equivariant or invariant models have been developed to exploit these symmetries. However,…
Sparse Weight Averaging with Multiple Particles for Iterative Magnitude Pruning
Moonseok Choi, Hyungi Lee, Giung Nam +1
Given the ever-increasing size of modern neural networks, the significance of sparse architectures has surged due to their accelerated inference speeds and minimal memory demands.…
Martingale Posterior Neural Processes
Hyungi Lee, Eunggu Yun, Giung Nam +2
A Neural Process (NP) estimates a stochastic process implicitly defined with neural networks given a stream of data, rather than pre-specifying priors already known, such as Gaussi…
Decoupled Training for Long-Tailed Classification With Stochastic Representations
Giung Nam, Sunguk Jang, Juho Lee
Decoupling representation learning and classifier learning has been shown to be effective in classification with long-tailed data. There are two main ingredients in constructing a…