17 citations · 30 across the 16 of their papers we have counts for
18 papers
Understanding the Mpemba Effect through a Rule of Thumb
Ran Yaacoby
In the Mpemba effect, a system prepared farther from equilibrium relaxes faster than one prepared closer to it. Its persistence across diverse systems, and under different dynamics…
Proper Scoring Rule-based Diffusion for Probabilistic Weather Forecasting
Joonhyeong Park, Giung Nam, Hyungi Lee +3
Recent probabilistic weather forecasters train stochastic predictors with the continuous ranked probability score (CRPS) to generate each ensemble member in a single forward pass.…
Improving Constrained Language Generation via Self-Distilled Twisted Sequential Monte Carlo
Sooyeon Kim, Giung Nam, Byoungwoo Park +1
Recent work has framed constrained text generation with autoregressive language models as a probabilistic inference problem. Among these, Zhao et al. (2024) introduced a promising…
Ex Uno Pluria: Insights on Ensembling in Low Precision Number Systems
Giung Nam, Juho Lee
While ensembling deep neural networks has shown promise in improving generalization performance, scaling current ensemble methods for large models remains challenging. Given that r…
Lipsum-FT: Robust Fine-Tuning of Zero-Shot Models Using Random Text Guidance
Giung Nam, Byeongho Heo, Juho Lee
Large-scale contrastive vision-language pre-trained models provide the zero-shot model achieving competitive performance across a range of image classification tasks without requir…
Enhancing Transfer Learning with Flexible Nonparametric Posterior Sampling
Hyungi Lee, Giung Nam, Edwin Fong +1
Transfer learning has recently shown significant performance across various tasks involving deep neural networks. In these transfer learning scenarios, the prior distribution for d…
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…
Improving Ensemble Distillation With Weight Averaging and Diversifying Perturbation
Giung Nam, Hyungi Lee, Byeongho Heo +1
Ensembles of deep neural networks have demonstrated superior performance, but their heavy computational cost hinders applying them for resource-limited environments. It motivates d…
Diversity Matters When Learning From Ensembles
Giung Nam, Jongmin Yoon, Yoonho Lee +1
Deep ensembles excel in large-scale image classification tasks both in terms of prediction accuracy and calibration. Despite being simple to train, the computation and memory cost…
Meta Learning Low Rank Covariance Factors for Energy-Based Deterministic Uncertainty
Jeffrey Willette, Hae Beom Lee, Juho Lee +1
Numerous recent works utilize bi-Lipschitz regularization of neural network layers to preserve relative distances between data instances in the feature spaces of each layer. This d…
Sequential Reptile: Inter-Task Gradient Alignment for Multilingual Learning
Seanie Lee, Hae Beom Lee, Juho Lee +1
Multilingual models jointly pretrained on multiple languages have achieved remarkable performance on various multilingual downstream tasks. Moreover, models finetuned on a single m…